Influence of observer-dependency on left ventricular hypertrabeculation mass measurement and its relationship with left ventricular volume and ejection fraction –  comparison between manual and semiautomatic CMR image analysis methods

Marcin Kubik; Alicja Dąbrowska-Kugacka; Karolina Dorniak; Marta Kutniewska-Kubik; Ludmiła Daniłowicz-Szymanowicz; Ewa Lewicka; Edyta Szurowska; Grzegorz Raczak

doi:10.1371/journal.pone.0230134

. 2020 Mar 11;15(3):e0230134. doi: 10.1371/journal.pone.0230134

Influence of observer-dependency on left ventricular hypertrabeculation mass measurement and its relationship with left ventricular volume and ejection fraction – comparison between manual and semiautomatic CMR image analysis methods

Marcin Kubik ^1,^#, Alicja Dąbrowska-Kugacka ^1,^*,^#, Karolina Dorniak ^2,^‡, Marta Kutniewska-Kubik ^3,^‡, Ludmiła Daniłowicz-Szymanowicz ¹, Ewa Lewicka ¹, Edyta Szurowska ⁴, Grzegorz Raczak ¹

Editor: Otavio Rizzi Coelho-Filho⁵

PMCID: PMC7065796 PMID: 32160262

Abstract

Background

Recent studies concerning left ventricular noncompaction (LVNC) suggest that the extent of left ventricular (LV) hypertrabeculation has no impact on prognosis. The variety of methods of LV noncompacted myocardial mass (NCM) assessment may influence the results. Hence, we compared two methods of NCM estimation: largely observer-independent Hautvast’s_(H) computed algorithm-based approach and commonly used Jacquier’s_(J) method, and their associations with LV end-diastolic volume (EDV) and ejection fraction (EF).

Methods

Cardiac magnetic resonance images of 77 persons (45±17yo) - 42 LVNC, 15 non-ischemic dilative cardiomyopathy, 20 control group were analyzed. LVNC patients were divided into the subgroup with normal (LVNC_N) and high EDV (LVNC_DCM). NCM and total left ventricular mass (LVM) were estimated by Hautvast’s [excluding intertrabecular blood (ITB) and including papillary muscles (PMs) into NCM] and Jacquier’s approach (including ITB and PMs, if unclearly distinguished, into NCM).

Results

The cut-off value of NCM for LVNC diagnosis was 22% (AUC 0.933) for NCM_H/LVM_H and 26% (AUC 0.883) for NCM_J/LVM_J. Inter- and intra-observer variability (estimated by coefficient of variation [CoV] and intraclass correlation coefficient [ICC]) of NCM_H/LVM_H appeared better than of NCM_J/LVM_J (CoV 4.3%, ICC 0.981 and CoV 4.9%, ICC 0.978; respectively for NCM_H/LVM_H, while for NCM_J/LVM_J: CoV 19.7%, ICC 0.15 and CoV 12.9%, ICC 0.504). In LVNC_N subgroup, the correlation between EDV and NCM_H was stronger than NCM_J (r = 0.677, p<0.001 vs. r = 0.480, p = 0.038; respectively). In LVNC the EDV correlated with NCM_H/LVM_H (r = 0.391, p<0.01), but not with NCM_J/LVM_J. In the overall group a relationship was present between EF and NCM_H/LVM_H (r = -0.449, p<0.001), but not NCM_J/LVM_J. Only NCM_H/LVM_H explained the variability of EDV (b 0.434, p<0.001).

Conclusions

Choosing a method of NCM assessment that is less observer-dependent might increase the reliability of results. The impact of method selection on the LV parameters and cut-off values for hypertrabeculation should be further investigated.

Introduction

Left ventricular noncompaction (LVNC) is so far considered to be a unique inherited cardiomyopathy [1]. It is characterized by a spongy morphological appearance of a left ventricular (LV) myocardium with a mesh of prominent trabeculae separated by deep intertrabecular recesses [2,3]. The LV hypertrabeculation, however, can be present in healthy individuals, as well as in cardiomyopathies [4,5].

Clinically, LVNC is associated with an increased risk of cardiovascular events similar to non-ischemic dilative cardiomyopathy (nDCM) [1,3]. The LV end-diastolic volume (EDV) and ejection fraction (EF) may be the significant markers of adverse outcomes in LVNC [1,4]. In turn, the clinical significance of the LV hypertrabeculation is unclear, and some studies indicate that it is not a prognostic factor of adverse cardiovascular outcomes [1,4,6]. These observations may be related to the variety of different criteria for LVNC recognition using cardiac magnetic resonance (CMR) imaging [4,7]. They are mostly based on the estimation of a thickness ratio between LV noncompacted and compacted layers or of a mass ratio between an LV noncompacted myocardial mass (NCM) and total LV mass (LVM) [8,9,10]. The methods assessing the mass ratio differ in the approach of in- or exclusion of intertrabecular blood pool (ITB) and papillary muscles (PMs) from NCM. Such an approach may affect the LVNC diagnosis and the assessment of the influence of LV hypertrabeculation on EDV and EF. A brief review of some diagnostic LVNC criteria is shown in Table 1.

Table 1. Review of the most popular left ventricular noncompaction (LVNC) recognition criteria using cardiac magnetic resonance (CMR) examination.

Established by Petersen et al. [8]:
1. NC/C ratio ≥ 2.3 (end-diastole, long-axis views)

Established by Grothoff et al. [9]:
    1. LV noncompacted mass > 15g/m²
    2. LV noncompacted mass > 25% of the total LV mass
    3. Trabeculation in basal segments of LV and NC/C of ≥ 3:1
    4. Methodology:
    • primary LVNC recognition based on echocardiographic Jenni et al. criteria [11]
    • implementation of the CAAS MRV post-processing software (Pie-Medical Imaging, Maastricht, Netherlands) for contouring myocardial layers
    • exclusion of blood pool from the noncompacted mass
    • inclusion of the papillary muscles in the compacted myocardial mass
    • criteria established in LVNC patients without LGE

Established by Jacquier et al. [10]:
    1. Trabeculated LV mass > 20% of LV global mass
    2. Methodology:
    • primary LVNC recognition based on echocardiographic criteria by Jenni et al. [11] and CMR criteria by Petersen et al. [8]
    • implementation of the Argus^™ post-processing software (Siemens) for contouring myocardial layers
    • inclusion of the papillary muscles in the compacted myocardial mass, however, with the possibility of their inclusion into the trabeculation area if not clearly distinguished
    • inclusion of blood pool into the LV noncompacted mass
    • in case of a highly trabeculated LV, the assessment of global LV mass was performed by positioning the endocardial contour at the outer edges of the trabeculation net

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BSA–body surface area; LV–left ventricle, NC/C–noncompacted/compacted ratio; LGE–late gadolinium enhancement

Jacquier's method of LVNC recognition adds ITB into NCM, which might falsely augment the real estimate of the latter. Additionally, it gives a possibility to include PMs either into the LV compacted layer mass (CLM) or into the LV trabeculation area if not clearly distinguished. Such a non-uniform approach to NCM estimation may decrease its reproducibility, as PMs in LVNC are often multiple and fragmented, and hence, their inclusion in either of the two layers can be equivocal. An algorithm differentiating ITB from NCM was proposed and described by Hautvast et al. [12] and is currently available as part of Philips’ proprietary analysis software for the LV volumes and masses. This algorithm enables the exclusion of ITB from NCM, but so far, its value in LVNC diagnosis was not confirmed.

Our study aimed to compare the two different methods of measurement of NCM and its percentage of LVM: proposed by Jacquier et al. [10] and by Hautvast’s [12] computed algorithm, and evaluate their possible impact on EDV and EF.

Materials and methods

Study design

The study was planned and performed following the European Association of Cardiovascular Imaging (EACVI) cardiac diagnostics guidelines and the Polish National Health Fund. Thus, the CMR scans were conducted as part of the standard out- and inpatients cardiac diagnostic process.

After receiving the written consent of the department heads of radiology and cardiology, respectively, the computed academic medical database records (available only in the Hospital of the Medical University of Gdansk, Poland) from 2011 to 2018 were searched for clinical data of previously examined with CMR patients with the pre-determined clinical diagnosis of LVNC, nDCM, and those without any cardiac disease (a control group). LVNC diagnosis was made based on the high clinical pre-test probability combined with structural findings assessed by two imaging methods: transthoracic echocardiography and CMR. The clinical pre-test probability was considered high in the presence of the LVNC-related symptoms (e.g., dyspnea, syncope, arrhythmia), and/or unexplained primary impaired LV function, and/or family history of cardiomyopathy. The echocardiographic LVNC criteria were adopted according to Jenni et al.: (i) no coexisting cardiac abnormalities, (ii) a two-layer structure of the LV muscle with a mesh of prominent trabeculae separated by deep perfused intertrabecular recesses (color Doppler) and the ratio of the thick noncompacted endocardium to the thinner compacted epicardium > 2, (iii) the predominant localization of pathology located distal to PMs–the apex, lateral and/or inferior [11]. The CMR diagnosis of LVNC was made based on Petersen's criterion of noncompacted to compacted layer thickness ratio >2.3 in long axes views [8]. Patients over age 35 had either additional non-invasive or invasive investigation of coronary artery disease. The nDCM diagnosis was made based on the global LV function impairment (EF <40%) and LV dilatation (EDV >117% of the normal values for age and sex) [13]. No genetic tests were performed. The control group consisted of patients with EF and EDV in the normal range and without any radiological or clinical evidence of cardiac disease. To compare LVNC patients with enlarged LV (LVNC_DCM) with nDCM, the LVNC group was divided into subgroups: LVNC with normal range LV (LVNC_N) and LVNC_DCM. The group inclusion criteria are listed in Table 2.

Table 2. Left ventricular noncompaction (LVNC), nonischemic dilative cardiomyopathy (nDCM) and control group inclusion criteria.

1) LVNC group:
    a. Petersen at al. CMR criterion of NC/C >2.3 in long axes views [8]
    b. CMR confirmation criterion of NCM_J/LVM_J with a modified cut-off value of >31%
    c. No coronary artery disease

2) LVNC subgroup with normal LV (LVNC_N):
    a. EDV < 117% of URL by age and sex, by Kawel-Boehm et al. [13]
    b. EF > 40%
    c. Fulfilled LVNC group criteria

3)LVNC subgroup with enlarged LV (LVNC_DCM):
    a. EDV > 117% of URL by age and sex, by Kawel-Boehm et al. [13]
    b. EF ≤ 40%
    c. Fulfilled LVNC group criteria

4) nDCM group:
    a. EDV > 117% of URL by age and sex, by Kawel-Boehm et al. [13]
    b. EF < 40%
    c. Unfulfilled LVNC group criteria
    d. No coronary artery disease

5) Control group:
    a. EDV < 100% of URL by age and sex, by Kawel-Boehm et al. [13]
    b. EF > LRL, by Kawel-Boehm et al. [13]
    c. Unfulfilled LVNC group criteria
    d. Cardiac disease excluded

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CMR–cardiac magnetic resonance; LV–left ventricle, EDV–LV end-diastolic volume; EF–LV ejection fraction; NCM_J/LVM_J—noncompacted/compacted LV layer mass ratio m. Jacquier et al. [10]; URL–upper range limit; LRL–lower range limit

Acquisition and analysis of CMR data

All patients underwent CMR using a 3.0 T scanner (Philips Achieva, Philips BV Eindhoven, The Netherlands) with a 32-channel phased-array receiver coil with repeated breath-holds. The segmented steady-state free-precession sequence was used to acquire cine images of the LV in two-, three-, and four-chamber views as well as in short-axis views to obtain a stack of contiguous short-axis slices to include the entire LV with a slice thickness of 8 mm and 2 mm gaps, according to standardized protocols [14]. The parallel acquisition technique, with an acceleration factor of 2, was used. The short-axis cine stack was analyzed semi-automatically with the use of the Philips Extended MR Workspace cardiac software package.

Epi- and endocardial contours were placed for each slice from the level of the mitral valve down to the apex. If necessary, the endocardial and epicardial contours were manually corrected.

The amount of NCM and LVM was estimated by two methods, according to Jacquier (_J) and utilizing Hautvast’s (_H) computed algorithm incorporated in the Philips’ proprietary software for LV masses analysis [10,12]. NCM and LVM estimated by Jacquier's method were called NCM_J, LVM_J, and NCM_J/LVM_J. [10] Adequately, NCM_H, LVM_H, and NCM_H/LVM_H were calculated using Hautvast’s algorithm. [12]

In brief, differences between methods concerned the observer-dependent inclusion of the PMs’ mass in CLM and the observer-independent inclusion of the ITB’s mass in NCM.

The estimate of CLM_J, NCM_J, and NCM_J/LVM_J

According to Jacquier's approach, three contours were traced in all slices of the LV short-axis view in end-diastole (Fig 1):

an epicardial–it delineated the outer edge of the LV compacted layer, and also delimited the volume combined with EDV and the volume of the LV compacted layer,
an endocardial–it delineated the inner edge of the LV compacted layer and also delimited the standard EDV (depending on the PMs’ fragmentation),

Thus, CLM_J was calculated as the difference between these two upper mentioned volumes multiplied by the density of the heart muscle (γ 1,05g/dl). In this method, PMs’ mass was OPTIONALLY included in CLM_J unless they were excessively fragmented, depending on the opinion of the observer and treated as trabeculation.
an inner endocardial–it delineated the inner edge of the LV trabecular layer, and thus, set an observer-dependent conventional border between the LV trabecular layer and the LV cavity without trabeculation; it also delimited only the volume of the LV blood pool but without ITB.

Thus, NCM_J was calculated as the volume difference between the EDV delimited by the endocardial contour and the volume delimited by the inner endocardial contour, multiplied by the density of cardiac muscle. ITB’s mass was ABSOLUTELY included in NCM_J. In consequence, NCM_J consisted of two to three masses: (i) the trabeculae, (ii) ITB, and OPTIONALLY and OBSERVER DEPENDENTLY (iii) PMs. LVM_J was calculated, adding NCM_J to CLM_J as follows: LVM_J = NCM_J + CLM_J.

The estimate of CLM_H, NCM_H, NCM_H/LVM_H

According to Hautvast’s computed algorithm, only two contours were traced in all slices of the LV short-axis view in end-diastole (Fig 2):

the epicardial
and the endocardial, both similar to the corresponding Jacquier’s contours.

In this method, the CLM_H was calculated similarly to the CLM_J as the volume difference between two volumes delimited by the epicardial end endocardial contours multiplied by the density of the heart muscle (γ 1,05g/dl). In opposition to Jacquier’s method, however, after manual correction of the endocardial contour, PMs’ mass was UNCONDITIONALLY excluded from CLM_H and automatically included in NCM_H.

In turn, NCM_H was estimated utilizing the postprocessing Philips’ software as follows:

first, the standard EDV was obtained based on the endocardial contour location in all short-axis view slides,
subsequently, EDV_H was estimated using the Hautvast’s algorithm [11], based on the difference in signal intensity of blood and myocardial muscle inside the endocardial contour.

As a result, ITB was ABSOLUTELY excluded from the LV trabecular layer and became part of the LV blood pool. Consistently, the EDV_H represented the volume of blood pool inside the LV at end-diastole. Thus, the NCM_H consisted of ONLY two masses: of (i) the trabeculae and UNOPTIONALLY (ii) PMs, and was calculated by the formula: EDV—EDV_H, multiplied by the density of cardiac muscle. LVM_H was calculated by adding NCM_H to CLM_H as follows: LVM_H = NCM_H + CLM_H.

The detailed comparison between methods is shown in Table 3.

Table 3. The detailed comparison between the two methods of the trabecular mass measurement: By Jacquier et al. and by the semi-automatic Hautvast’s algorithm implemented into Philip’s CMR software [10,12].

Method	Hautvast’s	Jacquier’s
CLM	papillary muscles *absolutely not included*	papillary muscles *included* unless excessively fragmented^*
LV papillary muscles	*absolutely included* in the NCM	*not included* in the NCM unless excessively fragmented^*
LV intertrabecular blood pool mass	*absolutely excluded* from the NCM (it *was* the part of the LV blood volume)	*absolutely included* in the NCM (it *was not* the part of the LV blood volume)
NCM	contains: • the trabecular mass • the LV papillary muscles mass	contains: • the trabecular mass • the LV intertrabecular blood pool mass • the LV papillary muscles, however, *only if* excessively fragmented^*
estimate of NCM	Algorithm	Observer
number of contours	2	3
epicardial contour position	on the outer edge of the LV compacted layer	on the outer edge of the LV compacted layer
endocardial contour position	on the inner edge of the LV compacted layer, however, separating the papillary muscles from the LV compacted layer	on the inner edge of the LV compacted layer, also covering the LV papillary muscles, and thus, including them in the LV compacted layer
the interior endocardial contour position	not applicable	on the top of the LV trabeculae, thus, delimiting the LV noncompacted layer from the LV cavity

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LV–left ventricular, CLM–LV compacted layer mass, NCM–LV noncompacted layer mass

* observer-dependence

The estimate of EF

The standard EDV and end-systolic volume (ESV) were obtained based on the endocardial border (of the compacted myocardium) contour displacement in all short-axis view slides, and EF was calculated with the formula: (EDV-ESV)/EDV * 100%. PMs and trabeculation were included as part of the LV cavity’s volume.

The institutional research ethics board (The Independent Bioethics Committee for Scientific Researches by the Medical University of Gdansk; no. of consent NKEBN/41/2012) approved the study, and each study participant provided informed written consent to CMR and enrollment of biographical data into the analysis.

Data collection and statistical analysis

Statistical analysis was performed using the licensed Statistica 13 software package (Statsoft Poland). All continuous variables are presented as mean ± standard deviations (SDs) or median with interquartile range. Categorical variables are reported as a percentage. Statistical significance was defined as p<0.05. The Shapiro-Wilk test was used to estimate the distribution. The independent t-Student test (for normally distributed continuous data) and the U-Mann-Whitney test (for not normally distributed continuous data) were used to compare between two groups. Differences between categorical variables were tested with the Chi-square test. In order to determine the cut-off value for the pathological trabecular mass in our population according to Jacquier's method, apart from the mean ± SDs of NCM_J/LVM_J, the upper confidence interval (+95% CI) was assessed in our control group, following Amzulescu et al. [4]. The ± 95% CI was calculated to establish the cut-off values of NCM_H and NCM_H/LVM_H for LVNC recognition.

ANOVA or Kruskal-Wallis test, where appropriate, were performed to investigate differences among the examined groups and afterward the comparison between the subgroups of LVNC (LVNC_N and LVNC_DCM) and the nDCM or the control groups were performed with post-hoc analysis. Subsequently, Pearson’s correlation analyses between NCM or NCM/LVM estimated by both methods vs. EF or EDV were performed. Finally, a multivariate stepwise regression analysis was done to create the best potential model explaining the variability of EF and EDV. The potential difference between sensitivity and specificity of cut-off values for the NCM and NCM/LVM between the LVNC and the control groups was estimated by the receiver operating characteristic curve (ROC).

Inter- and intra-observer measurements

To calculate inter-observer variability (reproducibility), NCM and NCM/LVM were calculated by both methods (Jacquier’s and Hautvast’s) in 10 LVNC randomly selected patients by two independent and experienced observers blinded to each other results; to assess intra-observer variability (repeatability), the two analyses were performed by the same observer in 10 LVNC patients. Coefficients of variation (CoV; as the SD of the differences divided by the mean) and intraclass correlation coefficients (ICC) were calculated. ICC was calculated using a model of the absolute agreement for intra-observer variability and consistency for inter-observer variability. The ICC’s values less than 0.5 assumed indicative of poor, 0.5 to 0.75 of moderate, 0.75 to 0.9 of good, and greater than 0.9 of excellent reliability. CoV was calculated using a within-subject SD method. We assumed CoV less than 5% as excellent, 5 to 10% as good, 10 to 20% as acceptable, and over 20% as poor data compliance [15].

Results

One hundred two examinations were extracted from the CMR database (LVNC, nDCM, the control group– 50, 21, 31, respectively), however, 23 examinations were excluded from further analysis due to death before the study qualification, doubtful diagnosis, CMR artifacts and additional cardiac diseases which could influence the group qualification. The subsequent analysis of NCM_J/LVM_J in the control group determined the cut-off value >31% for the recognition of the pathological trabecular mass percentage (mean 24%; ±95% CI 18–31%). Thus, two of the 44 examinations from the whole LVNC group did not meet the confirmation criterion. Among 77 examinations, which were further analyzed there were 42 (54.5%) with determined diagnosis of LVNC (age 45±17y, men 47.6%), 15 (19.5%) with nDCM (age 45±19y, men 69.2%), and 20 subjects from the control group (age 48±19y, men 54.4%) %)–see S1 Fig. Clinically, in the LVNC group, 2 individuals had prior myocardial inflammation, 2 had previous transient ischemic attack, 1 had paroxysmal atrial fibrillation, 3 suffered from the 1st-grade well-controlled hypertension, 2 were diabetics, 1 was subjected to an ablation procedure due to the Wolf-Parkinson-White syndrome, 1 had the atrioventricular block type II Mobitz I and II, 3 had LBBB, and 1 was subjected to the procedure of the persistent foramen ovale catheter occlusion. When performing the analysis according to Jacquier's method, PMs were visually identified and considered sufficiently separated from trabeculae in 41 out of the 77 examinations–LVNC in 10 (24%) patients, nDCM in 11 (73%) patients, the control group in 20 (100%) subjects; (p<0.001). The whole LVNC group differed from the control in all analyzed parameters (LV volumes, masses, and EF; p<0.001) but age, BSA, and sex (p>0.050)–see Table 4. The differences between the whole LVNC and nDCM groups only concerned left ventricular volumes: EDV, ESV, EDV_H, and NCM_J/LVM_J−see Table 4. In consequence, the only parameters which differentiated both the nDCM and the control group from the whole LVNC were EDV, ESV, EDV_H, and NCM_J/LVM_J. It should be noted that EF in the LVNC group was markedly lower than in the control group and borderline significantly higher than in the nDCM group.

Table 4. Comparison of the whole left ventricular noncompaction group (LVNC) with the non-ischemic dilative cardiomyopathy (nDCM) and the control group.

Parameter	LVNC (N = 42)	nDCM (N = 15)	P_{value LVNC vs DCM}	Control (N = 20)	P_{value LVNC vs Control}
Age [y]	45 (±17)	45 (±19)	0.957	49 (±19)	0.523
BSA [m2]	1.8 (±0.17)	1.88 (±0.26)	0.208	1.92 (±0.29)	0.059
Sex (male)	20 (47.6%)	10 (69.2%)	0.205^**	11 (54.4%)	0.587^**
EDV [ml]	221 (173–73)	281 (±60)	0.038^*	117 (± 30)	<0.001^*
EDV_H [ml]	178 (±62)	221 (±55)	0.028	96 (±27)	<0.001
ESV [ml]	146 (97–221)	218 (±71)	0.035^*	49 (±18)	<0.001^*
EF [%]	31(±12)	24(±10)	0.055	59 (±7)	<0.001
LVM_H [g]	174 (148–225)	228 (148–353)	0.065^*	121 (±33)	<0.001^*
LVM_J [g]	210 (175–304)	284 (±70)	0.494	145 (±38)	<0.001^*
NCM_H [g]	53 (41–71)	61 (35–122)	0.347^*	22 (±5)	<0.001^*
NCM_J [g]	119 (86–166)	108 (61–182)	0.656^*	34 (22–75)	<0.001^*
NCM_H/LVM_H [%]	31 (25–34)	27.8 (±7.3)	0.151^*	19,0 (±4.2)	<0.001^*
NCM_J/LVM_J [%]	41.7 (±11.1)	27.8 (±7.2)	<0.001	24.1 (±10.8)	<0.001

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Data are presented as mean ± SD (CI ±95%) and median with interquartile range (25–75%) values. BSA–body mass index (Du Bois), LV–left ventricular, EDV–LV end-diastolic volume; EDV_H−EDV blood corrected m. Hautvast’s computed algorithm; ESV–LV end-systolic volume; ESV_H−ESV blood corrected m. Hautvast’s computed algorithm; EF–LV ejection fraction; NCM_J−noncompacted layer mass m. Jacquier et al. [10]; LVM_J−total LV mass m. Jacquier et al. [10]; NCM_H−noncompacted layer mass m. Hautvast’s computed algorithm [12]; LVM_H−total LV mass m. Hautvast’s computed algorithm [12]; NCM_J/LVM_J−noncompacted/compacted layer mass ratio m. Jacquier et al. [10]; NCM_H/LVM_H−noncompacted/compacted layer mass ratio m. Hautvast’s computed algorithm [12]

* p values were calculated using the U-Mann-Whitney test

** p values were calculated using the Chi-square test

The LV trabecular distribution in the whole LVNC group is presented in Fig 3. Generally, the noncompacted to compacted layer thickness ratio gradually decreased from the apex to the base of LV. The most excessive trabeculation was observed in all apical and middle lateral and middle posterior segments of LV. In turn, none or discreetly intensified trabeculation was found in the basal and middle septal and anteroseptal segments of LV.

The subgroup comparison

The comparison between the LVNC_N subgroup and the control revealed significant differences in EDV, ESV, EF, NCM_J, and NCM/LVM estimated by both methods, and all of them were significantly higher in LVNC_N, except for the EF. There was a tendency to higher values of EDV_H in LVNC_N.

The comparison between the LVNC_DCM subgroup and the nDCM group revealed no significant differences in any of the analyzed parameters but NCM_J, LVM_J, and NCM_J/LVM_J, which were significantly higher in the LVNC_DCM. Of note, NCM_H/LVM_H did not differentiate these groups. The summary of the results is presented in Tables 5 and 6.

Table 5. Comparison of left ventricular noncompaction subgroup with normal-range left ventricle (LVNC_N) and the control group.

Parameter	LVNC_N (N = 20)	Control (N = 20)	p_post-hoc
Age [y]	38 (±15)	48.9 (±19.1)	0.137
BSA [m2]	1.78 (±0.21)	1,92 (±0.29)	0.219
EDV [ml]	166 (±37)	117 (±30)	0.021
EDV_H [ml]	128 (±29)	96 (±27)	0.051
ESV [ml]	100 (±31)	49 (±18)	0.022
EF [%]	41 (±9)	59 (±7)	<0.001
LVM_H [g]	141 (±38)	121 (±33)	0.376
LVM_J [g]	186 (±45)	145 (±38)	0.074
NCM_H [g]	41 (±15)	22 (±5)	0.111
NCM_J [g]	86 (37–163)	34 (22–75)	<0.008
NCM_H/LVM_H [%]	29.4 (±6.4)	19 (±4.2)	<0.001
NCM_J/LVM_J [%]	40.9 (±10.3)	24 (±11)	<0.001

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Data are presented as mean ± SD (CI ±95%) or median with interquartile range (25–75%) values. Abbreviations as in Table 4.

Table 6. Comparison of left ventricular noncompaction subgroup with enlarged left ventricle (LVNC_DCM) and dilated cardiomyopathy (nDCM) group.

Parameters	LVNC_DCM (N = 22)	nDCM (N = 15)	p_post-hoc
Age [y]	52 (±16)	45 (±19)	0.495
BSA [m2]	1.82 (±0.13)	1.88 (±0,26)	0.434
EDV [ml]	300 (±71)	281 (±60)	0.465
EDV_H [ml]	223 (±48)	221 (±55)	0.914
ESV [ml]	236 (±73)	218 (±71)	0.504
EF [%]	23 (±8)	24 (±10)	0.730
LVM_H [g]	220 (165–447)	228 (148–353)	0.500
LVM_J [g]	337 (±92)	284 (±70)	0.046
NCM_H [g]	65 (41–214)	61 (35–122)	0.106
NCM_J [g]	165 (86–317)	108 (61–182)	0.004
NCM_H/LVM_H [%]	32.2 (±8.3)	27.8 (±7.3)	0.192
NCM_J/LVM_J [%]	42.4 (±11.9)	27.8 (±7.2)	0.001

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Data are presented as mean ± SD (CI ±95%) or median with interquartile range (25–75%) values. Abbreviations as in Table 4.

NCM/LVM estimated by Hautvast’s algorithm did not differ from Jacquier's method in the control group (19.0±4.2% vs. 24.1±11%, p = 0.164) and nDCM (27.8±7.3% vs. 27.8±7.2%, p = 0.989), but it was significantly lower in the LVNC group (30.8±7.5% vs. 41.7±11.0%, p<0.001). The difference remained significant in the LVNC subgroups: LVNC_N (29.4±6.4% vs. 40.9±10.3%, p<0.001) and LVNC_DCM (32.2±8.3% vs. 42.4±11.9%, p<0.001).

Correlation analysis

Both the NCM_H or NCM_J revealed a good and similar correlation concerning EDV in the overall examined group (r = 0.789, p<0.001 vs. r = 0.799, p<0.001; respectively), the LVNC group (r = 0.800, p<0.001 vs. r = 0.816, p<0.001; respectively), and the LVNC_DCM subgroup (r = 0.746, p<0.001 vs. r = 0.748, p<0.001; respectively); however, in the LVNC_N subgroup the correlation between the NCM_H and EDV was stronger in comparison to the NCM_J vs. EDV (r = 0.677, p<0.001 vs. r = 0.480, p = 0.038; respectively).

In turn, in the overall examined group and the LVNC group, the NCM_H or NCM_J revealed a similar correlation to EF (overall examined group: r = -0.556, p<0.001 vs. r = -0.572, p<0.001, and LVNC group: r = -0.502, p<0.001 vs. r = -0.491, p<0.001; respectively). In the LVNC subgroups, no correlation between the NCM_H or NCM_J vs. EF was found (p>0.05).

The correlation of NCM_H/LVM_H concerning EDV revealed a moderate correlation in the overall examined group (r = 0.434, p<0.001) and in the LVNC group (r = 0.391, p<0.01). In addition, this correlation was significant also in LVNC_DCM subgroup (r = 0.457, p = 0.029). Correlation between NCM_H/LVM_H and EF, though mild, was also statistically significant in the overall group (r = -0.449, p<0.001), nevertheless, the subgroup analysis revealed no correlations (LVNC: r = -0.153, p = 0.334; LVNC_DCM: r = 0.110, p = 0.618; LVNC_N: r = -0.143, r = 0.558).

In turn, NCM_J/LVM_J revealed no significant correlations (see Tables A and B in S1 Table).

Regression analysis

In the univariate regression analysis model, the NCM_H, NCM_J, and EF in similar strength explained the variability of EDV. In turn, only the NCM_H/LVM_H, of the two NCM/LVM estimation methods, explained the variability of EDV (F = 17.50, p <0.05) (see Table A in S2 Table).

In the multivariate stepwise regression analysis model (factors included: EF, and NCM_H, or NCM_J, or NCM_H/LVM_H), both the EF and NCM_H or EF and NCM_J models similarly explained the variability of EDV (see Table B in S2 Table).

Cut-off values of noncompacted mass measurements between the LVNC and the control groups (ROC analysis)

The comparison of the cut-off values is presented in Table 7. Concerning the NCM/LVM, both methods with similar sensitivity and specificity differentiate the LVNC and the control group; however, the classifier NCM_H/LVM_H appeared to better differentiate these two groups. In turn, the absolute NCM_H seemed to have a better specificity in comparison to NCM_J.

Table 7. The cut-off values of left ventricular noncompaction mass (NCM) between the left ventricular noncompaction group (LVNC) and the control group–ROC analysis.

Parameter	Cut-off value	AUC	Sensitivity	Specificity
NCM_H	26g	0.955	92.9%	90.9%
NCM_J	39.9g	0.944	95.2%	72.7%
NCM_H/LVM_H	22%	0.933	95.2%	81.8%
NCM_J/LVM_J	26%	0.883	95.2%	81.8%

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NCM_H−noncompacted layer mass m. Hautvast’s computed algorithm [12]; NCM_J/LVM_J−noncompacted/compacted layer mass ratio m. Jacquier et al. [10]; NCM_H/LVM_H−noncompacted/compacted layer mass ratio m. Hautvast’s computed algorithm [12]; AUC–area under the ROC curve

Inter- and intra-observer variability

The results of reproducibility and repeatability are presented in Table 8. Intra- and inter-observer variability of Hautvast’s method was more reproducible and repeatable in comparison to Jacquier’s approach.

Table 8. Comparison of the inter- and intra-observer variability between the method based on Hautvast’s computed algorithm and Jacquier’s approach of noncompacted myocardial mass evaluation.

Parameter	Inter-observer variability (reproducibility)			Intra-observer variability (repeatability)
Parameter	CoV	ICC	ICC’s ±95CI	CoV	ICC	ICC’s ±95CI
NCM_H	4.3%	0.998	0.990 to 0.999	3.7%	0.998	0.991 to 0.999
NCM_H/LVM_H	4.3%	0.981	0.919 to 0.996	4.9%	0.978	0.896 to 0.995
NCM_J	20.5%	0.866	0.552 to 0.965	12.8%	0.873	0.268 to 0.974
NCM_J/LVM_J	19.7%	0.150	-0.532 to 0.714	12,9%	0.504	-0.109 to 0.859

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CoV–coefficient of variation; ICC–intraclass correlation coefficient; NCM_J−noncompacted layer mass m. Jacquier et al. [10]; NCM_H−noncompacted layer mass m. Hautvast’s computed algorithm [12]; NCM_J/LVM_J−noncompacted/compacted layer mass ratio m. Jacquier et al. [10]; NCM_H/LVM_H−noncompacted/compacted layer mass ratio m. Hautvast’s computed algorithm [12]

Discussion

The presented approach to NCM measurement utilizing Hautvast’s computed algorithm method has shown excellent reproducibility and repeatability compared to Jacquier’s approach [10,12]. It differentiated the LVNC group from the controls with a higher specificity considering NCM_H in comparison to NCM_J and might be especially applicable in the LVNC_N subgroup. Comparing to the NCM measurement method based on Hautvast’s computed algorithm, NCM_J/LVM_J neither correlated with EDV or EF nor explained the EDV variability.

The results of our study pointed to the importance and necessity of the automation, standardization, and selection of the NCM measurement method. The estimation of NCM and NCM/LVM should be interpreted with due consideration of the methodology that was applied. In our study, the cut-off values for LVNC recognition related to NCM_H/LVM_H and NCM_H were lower than for Jacquier’s method and also higher than the cut-off value of NCM_J/LVM_J presented in Jacquier’s research [10]. This is in line with doubts regarding the clinical efficacy of Jacquier's method in recognition of LVNC [5]. It also raises the question of whether the percentage of NCM_H may indirectly (through EDV and/or EF) influence the prognosis in LVNC. The lack of influence of the LV trabeculae on the adverse cardiovascular outcomes in LVNC revealed in the recent studies and its clinical similarity to nDCM in terms of genetics, morphology, and clinics, often lead cardiologists and radiologists to perceive LVNC as a form of nDCM [4,16]. These doubts and questions have prompted attempts to establish the criteria of the LVNC diagnosis [17,18], which are most commonly based on Petersen's or Jacquier's observations [4,8,6]. According to Petersen et al., LVNC can be recognized if the criterion of the noncompacted to compacted layer width ratio > 2.3 is fulfilled in at least one LV segment. Thus, LVNC could be recognized in the case of the clinically silent trabeculae in the apex of a healthy LV [4,19]. In turn, the Jacquier's approach significantly overestimates the actual NCM and may lead to false conclusions regarding its influence on adverse cardiovascular events [20]. In turn, the major advantage of the presented computed method is its semi-automatic character, exclusion of the blood pool from analysis and simplification related to the unanimous inclusion of PMs into the trabecular mass. Bricq et al. previously introduced the semiautomatic assessment of the trabeculated and compacted LV mass, but the authors excluded both PMs and ITB from NCM [20,21].

It is essential to consider the precision and accuracy of the different methods of NCM analysis. Positano et al. revealed that the Grothoff's approach seemed to better capture the actual extension of trabeculated tissue than the Jacquier's, because of the exclusion of ITB volume from NCM. The inter-observer reproducibility of Grothoff's and Jacquier's methods in that study were quite similar: 9.71% and 8.22%, respectively [22]. In our study, however, the reproducibility of Jacquier’s method was lower (~20%). In contrast, the simplification of the diagnostic method, related to the semi-automated blood-muscle separation, resulted in a better reproducibility of either NCM_H or NCM_H/LVM_H (4.3%).

In consequence, harmonizing the method of the NCM measurement, with the inclusion of PMs into NCM and the exclusion of ITB from NCM utilizing Hautvast’s computed algorithm, resulted in the increased reproducibility in comparison to Jacquier's method. The increased reproducibility of the computed algorithm method was mainly achieved in terms of precision, for it is hard to improve the accuracy (trueness) of the analytic approach when there is no CMR reference method of NCM or NCM/LVM measurement. In the presented approach, the delineation of the inner and outer border of the compacted layer in short axis slices, without PMs, decreases the possible risk of error or observer-related inconsistency. The mathematical algorithm itself was operator-independent, and the risk of error was related only to movement artifacts (arrhythmia, breath-hold difficulties, etc.) and the level of cross-section slice from which the observer started or ended the LV masses analyses.

A crucial issue in clinical practice is the differentiation between LVNC with enlarged LV and nDCM. In our study, no differences in basic morphological or functional CMR parameters between LVNC_DCM and nDCM were observed, except for NCM_J and NCM_J/LVM_J, which should be interpreted in relation to the initial group qualification criteria. The alternative of NCM estimation used in our study, namely NCM_H and NCM_H/LVM_H, did not differentiate these two groups. This discrepancy could be related to the differences in PMs’ quantification between the methods. PMs were sufficiently separated from the trabeculae in all cases of the control and nDCM groups, in contrary to only 24% cases of the LVNC group, 13% cases of the LVNC_DCM, and 35% of the LVNC_N subgroups. Thus, the mathematical algorithm used for the quantification of trabeculation may have slightly overestimated the trabeculated mass in the proportion of patients with nDCM. This, in turn, may have blurred the differentiation between nDCM and LVNC_DCM. In the case of LVNC_N, the presented method performed much better.

In general, LVNC recognition is mostly based on the extent of the LV hypertrabeculation. Doubts arise, however, which amount of the LV trabeculation should be considered pathological. According to Jacquier et al., LVNC could be recognized when the trabeculated LV mass was above 20% of the LV global mass [10]. Our results, however, similarly to the study of Amzulescu et al. [4], indicate that the threshold for NCM_J/LVM_J should preferably be set higher.

In contrary to Jacquier’s approach, moderate correlations were found between NCM_H/LVM_H and EDV in the overall examined group and the LVNC group. In turn, a significant correlation between the NCM_H/LVM_H and EF was observed only in the overall group. The explanation of this may be given by Paun et al. [23]. The authors pointed at the possibly significant compensatory character of hypertrabeculation in LVNC in a malfunctioning LV, which in consequence might have an impact on stroke volume and EF [23], which in consequence might have an impact on stroke volume and EF [23] and might influence the correlation results.

Clinical implications

Operator-independent computed algorithms of the NCM measurement, thanks to its semi-automatic character, might be a solution to increase reproducibility and repeatability, and reduce the time-consuming, operator-dependent input. The presented method might be applicable in the differentiation of LV hypertrabeculation in a non-enlarged (EDV in normal range) and at most mildly impaired (EF >40%) LV. Its possible application in case of an enlarged LV with moderate to severe dysfunction, and also the influence of the observed NCM_H correlation with EDV require further research. Therefore, the estimate of NCM and NCM/LVM should be interpreted with due consideration of the methodology that was applied.

Limitations

The qualification to the groups was based on the well-known but disputable CMR’s criteria by Jacquier and Petersen, however, to increase the probability of LVNC diagnosis, we modified the cut-off value of Jacquier’s method and adopted the value of 31%. As our study concentrated on the analysis of the CMR imaging, we did not relate our results to adverse clinical outcomes but the established parameters of the LV function, such as EF or EDV, as they are considered possible good prognostic factors of adverse outcomes in cardiomyopathies [1,4]. Focusing mainly on LV morphology, we did not perform any LGE or T1-mapping analysis [24].

Conclusions

Supporting information

S1 Fig. Group qualification follow chart.

The stages of group qualification are marked with italics. LVNC–left ventricular noncompaction; nDCM–nonischemic dilated cardiomyopathy; Control–control group; pts.–patients.

(PDF)

Click here for additional data file.^{(97.7KB, pdf)}

S1 Table. Pearson's correlation between left ventricular noncompaction mass and left ventricular end-diastolic volume or ejection fraction.

NCM_H−noncompacted layer mass m. Hautvast’s computed algorithm [12]; NCM_J−noncompacted layer mass m. Jacquier et al. [10]; NCM_J/LVM_J−noncompacted/compacted layer mass ratio m. Jacquier et al. [10]; NCM_H/LVM_H−noncompacted/compacted layer mass ratio m. Hautvast’s computed algorithm [12]; EF–left ventricular ejection fraction; EDV–left ventricular end-diastolic volume.

(DOCX)

Click here for additional data file.^{(15.6KB, docx)}

S2 Table. Regression analysis models.

(DOCX)

Click here for additional data file.^{(16.6KB, docx)}

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0230134.r001

Decision Letter 0

Otavio Rizzi Coelho-Filho

18 Dec 2019

PONE-D-19-30605

Relationship between left ventricular hypertrabeculation mass, left ventricular volume and ejection fraction - comparison between manual and semiautomatic CMR image analysis methods

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Additional Editor Comments (if provided):

Drs. Kubik and coworkers investigated the accuracy of different methods on left ventricular volumes and ejection fraction (EF) to identify noncompacted myocardial mass (NCM). Left ventricular noncompaction (LVNC) is an important condition that faces several diagnostics challenges and the current study aimed to explore novel methods to improve its identification.

Recently T1 mapping characterization with ECV quantification has been shown to provide completará information to LV volumes and LGE (Eur Heart J Cardiovasc Imaging. 2018 Aug 1;19(8):888-895. doi: 10.1093/ehjci/jey022.). I wonder if T1 mapping is available.

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Reviewer #1: The article entitled “Relationship between left ventricular hypertrabeculation mass, left ventricular volume and ejection fraction – comparison between manual and semiautomatic CMR image analysis methods” investigates a very relevant topic related to an increasingly diagnosed condition, left ventricular noncompaction cardiomyopathy(LVNCC). Some points need explanations and are listed below.

1. INTRODUCTION

a. Page 3, line 50 – You state that LVNC is ‘the” unique inherited cardiomyopathy. Many other inherited cardiomyopathy have been described, and no clear evidence of an exclusive genetic background is available. I would prefer to say “A” unique…., although its morphological features may appear in other conditions and genetic mutations either1.

b. Page 3, Line 56 and 57 _ The mentioned reference(yours ref.1) states clearly about the presence of Late Gadolinium Enhancement as a predictor, better than LVEF. So it may be modified as a suggestion.

c. Page 4, Line 80-81 _ You state here that a modification in the Phillips’ approach was made to include papillary muscles in the non-compacted myocardium (NCM). This is a methodological information, presented again in the Methods section, page 9, lines 174-175. It is important to clarify if this correction was automatic or manually performed.

2. METHODS

a. Page 8, line 134 _ CLM abbreviation is first cited here but it significance is found only in Line 192 part of Table 3. Please correct it.

b. Page 10, lines 178 and 179 _ Here you refer to the figures of the two methods used. My suggestion is to refer each figure close to the description of each method, so the readers can correlate easily.

c. Also, a detailed legend of figures 1 and 2 is presented in lines 194 to 204. It seems misplaced. A legend page is needed.

d. Page 10,Table 3 _ what is the meaning of the asterisk? It should be mentioned in the legend.

e. Page 12, line 208 _ the zero after 5 is not necessary. Please exclude.

3. RESULTS

a. Page 13, first paragraph _ in the selection process you excluded 11 of 31 controls. This seems to be related to your confidence interval criteria (18-31%), maybe being to strict. Probably a comment is necessary.

b. Page 14 _ Please clarify the meaning of the asterisk signal in Table 4.

c. Page 14 _ table 4 and lines 249-250. An important point is that the Philips method could not differentiate dilated cardiomyopathies from noncompaction cardiomyopathy, so it may not be clinical applicable. Can you comment on that?

d. Page 16, lines 274 -281 _ Please exclude these lines from the main text and include in the legends page.

e. You present in your tables 4, 5 and 6 two measurements for EDV. One obtained from traditional manual tracing and the second one from Philips software. Why are they so different? From Table 3 definitions, in the Philips algorhytm the endocardial exclusion of papilary muscles only , LVNCdcm decreased 80mL and nearly 40mL in LVNCn.

f. Page 19, lines 309-310 _ Correlation between NCMph/LVMph and EF wasat most modest (r=0.44) although significant but only for the overall group. I suggest presenting all correlations since no data from Jaquier’s method is presented. So, your conclusions at Page23-24 can not be corroborated by the evidence presented.

g. Page 19, lines 313-315 _ The cut-off values are isolated with not context. Plase expand the explanation of these values presented.

h. Page 19, Table 7 _ Can you provide an explanation for the interobserver value of 9.9% for NCM, since it is a semiautomatic method? Since the same slice was used in the comparative analysis and the explanation provided in Pages 393-395 do not apply.

1. Monserrat L, Hermida-Prieto M, Fernandez X, Rodriguez I, Dumont C, Cazon L, Cuesta MG, Gonzalez-Juanatey C, Peteiro J, Alvarez N, Penas-Lado M and Castro-Beiras A. Mutation in the alpha-cardiac actin gene associated with apical hypertrophic cardiomyopathy, left ventricular non-compaction, and septal defects. European heart journal. 2007;28:1953-61.

Reviewer #2: LVNC is a heterogeneous condition with many gaps on its etiopathogenesis, propaedeutic and treatment. One of the major concerns in LVNC is the growing of number of false-positive cases diagnosed by imaging in the recent years. The strength of this well-written paper is to demonstrate a higher reproducibility of a new computed algorithm for the quantification of degree of LV trabeculation when compared with Jaquier’s method (which indeed suffers from the reader variability and may affect the diagnosis). However, I’m concerned with the author’s aims/conclusions regarding the relationship between this new quantification and LV remodeling. As acknowledged by the authors, evidences from the largest cohorts of LVNC patients indicated the degree of LV trabeculation seems to have no prognostic impact on cardiovascular events (neither Petersen’s nor Jaquier’s method). Other studies investigating extracellular matrix by CMR T1 mapping (Araujo-Filho et al, EHJ, 2018) and microcirculation/metabolism by PET (Jenni et al, JACC, 2002; Tavares-Melo et al, EHJ, 2017) failed to find a significant relationship with the amount of trabeculations. These support that the degree of LV trabeculation is not a mediator of adversity. The authors indeed show a statically significant (but clinically questionable) correlation between EDV and LV trabeculation using the algorithm. This is expected once the source of variability of the Jaquiers’s method is removed, but the authors should be careful with the potential prognostic impact of this new approach and may highlight more its potential diagnosis role.

There are other issues that need to be addressed:

# “23 examinations were excluded from further analysis due to death before the study qualification, doubtful diagnosis, CMR artifacts and additional cardiac diseases which could influence the group qualification”. Why did the control group have the largest proportional exclusion (from 31 to 21)? Would this affect the found cut-off value?

# “...in the LVNC group 2 individuals had prior myocardial inflammation”. The authors mean previous myocarditis before diagnosis of LNVC? If yes, how confident are they with the diagnosis of LVNC in these 2 cases?

# “Independent t-Student and U-Mann-Whitney tests were used where appropriate”. The authors should review the statistical approach for multiple comparison in the subgroup analysis.

# “The Chi-squared test was used for nonparametric data”. Please review this statement since chi-squared test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories.

# “In order to determine the cut-off value for the pathological trabecular mass in our population

according to Jacquier's method, apart from the mean ± SDs of NCMJ/LVMJ, the upper confidence interval (+95% CI) was assessed in our control group, following Choi et al”. Those cited authors seems to have used receiver operating characteristics (ROC) curves to determine the optimal cut-off values. Please review the reference.

# “Finally, a multivariate regression analysis model was created to estimate the potential influence of the examined parameters on EF and EDV”. Please provide the independent variables which were included in the model.

# “The F-Snedecor test and t-Student test were used to compare the accuracy of the two analyzed methods.”. Please review if these tests are appropriate to investigate accuracy.

**********

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PLoS One. 2020 Mar 11;15(3):e0230134. doi: 10.1371/journal.pone.0230134.r002

Author response to Decision Letter 0

1 Feb 2020

We would like to express our thanks for a thorough and detailed revision of our manuscript. Please, find answers to Reviewers below.

I. Responses to the Editor:

1) Journal Requirements

We did our best to ensure that our manuscript meets PLOS ONE’s requirements. We re-verified the compliance of the paper with PLOS ONE’s standard and corrected incompatibilities.

2) Ethics Committee Board Full Name

We provided information on the full institutional research ethics board name and the number of consent in the section “Materials and Methods”.

3) Specifying in the ethics statement the type of consent provided by the study participants

In the “Materials and Methods” section, we specified the ethics statement by adding information on the type of consent (informed written consent) provided by the study participants.

4) For reproducible purposes, please provide further information on the medical database records used in this study and explain how researchers may access them

At the beginning of the section “Materials and Methods”, we added information on how to obtain data from the medical database in our medical center. Generally, to obtain access to the database researchers have to receive written consent of heads of departments of radiology and of cardiology. Subsequently, final approval is given by the principal director of the University Clinical Centre of the Medical University of Gdańsk in Poland. Fully deidentified data are available upon request from the corresponding author.

5) Recently T1 mapping characterization with ECV quantification has been shown to provide complete information to LV volumes and LGE. I wonder if T1 mapping is available?

We consider the CMR T1-mapping technique an inspiring option in diagnostics of left ventricular noncompaction, as well as the well-documented late gadolinium enhancement imaging (LGE). However, our study verified a potential superiority of automated radiological evaluation of the extent of left ventricular (LV) noncompacted mass (NCM) and its possible influence on LV end-diastolic volume (EDV) and LV ejection fraction (EF). LGE might improve risk stratification when added to clinical and morphological criteria for LVNC. Nevertheless, in our study, LGE and T1-mapping techniques were not considered.

I. Responses to Reviewer 1:

1) INTRODUCTION

COMMENT_1: Page 3, Line 50 – You state that LVNC is ‘the” unique inherited cardiomyopathy. Many other inherited cardiomyopathy has been described, and no clear evidence of an exclusive genetic background is available. I would prefer to say “A” unique…., although its morphological features may appear in other conditions and genetic mutations either.

RESPONSE_1: We agreed with the opinion of Reviewer 1 on the difference between: „a unique inherited cardiomyopathy” and „the unique…”. As it was mentioned, this particular cardiomyopathy does not have a corresponding specific genotype distinguishing it from the others. According to the above, we changed the words “the unique…” to “a unique…” in the introduction section of our manuscript.

COMMENT_2: Page 3, Lines 56 and 57 – The mentioned reference (your ref. 1) states clearly about the presence of Late Gadolinium Enhancement as a predictor, better than LVEF. So it may be modified as a suggestion.

RESPONSE_2: We fully agree that the role of LGE in different cardiac diseases (i.e., myocarditis, cardiomyopathies, coronary heart disease) is undoubtful and well-described in the medical literature. Although it was recently associated also with LVNC, it seems to be a feature of all cardiomyopathies.

The authors of the ref. 1 clearly stated that “detection of LV fibrosis is a robust independent predictor of poor prognosis.” However, they also clearly pointed to a high rate of cardiovascular events (CEs) in patients with LV dilation and reduced EF - a phenotype of LVNC classified as dilated cardiomyopathy (DCM)-like, the same we analyzed in our study. With regard to EF and EDV as predictors of CEs, the authors referred to the research by Mavrogeni et al. (2012). At univariate analysis, they found EF, EDV, and LGE as independent predictors of CEs. Subsequently, in a multivariate analysis, LGE was the only independent predictor of CEs. Nevertheless, none of the three presented models consisted of both EF and EDV; moreover, no head-to-head improvement analysis was made between EF vs. EDV vs. LGE. In all presented cases, LGE was only additional to EF or EDV; however, it significantly improved the risk assessment.

According to the above mentioned, we have modified our statement. Moreover, we added a reference (no 4; Amzulescu MS et al., JACC Cardiovasc Imaging. 2015;8(8):934-46. doi: 10.1016/j.jcmg.2015.04.015). In this reference, the authors stated that the prognosis of patients with LVNC is mainly affected by the presence of heart failure symptoms (clinical condition), LV dilatation, and systolic dysfunction; however, the presence and extent of LGE were also statistically significant predictors of outcome in their examined population.

COMMENT_3: Page 4, lines 80 and 81 – You state here that a modification in the Phillips’ approach was made to include papillary muscles in the non-compacted myocardium (NCM). This is methodological information, presented again in the Methods section, page 9, lines 174-175. It is important to clarify if this correction was automatic or manually performed.

RESPONSE_3: We agree that the information about the inclusion of papillary muscles in the noncompacted myocardium is a methodological issue, and it was repeated in the section methodology line 165-166; therefore, we have modified the manuscript accordingly. In the methodology section, an explanation was added that after manual correction of the endocardial trace, the process of inclusion of papillary muscles into noncompacted mass was automatic.

2) METHODS and RESULTS

COMMENT_4: Page 8, Line 134 – CLM abbreviation is first cited here but its significance is found only in Line 192 part of Table 3. Please correct it

RESPONSE_4: With all due respect to Reviewer, the first description of the CLM abbreviation was placed on page 3, line 71 of the manuscript.

COMMENT_5: Page 10, Lines 178 and 179 – Here, you refer to the figures of the two methods used. My suggestion is to refer each figure close to the description of each method so that the readers can correlate easily.

RESPONSE_5: According to the Reviewer’s suggestion, the references to figures 1 and 2 were placed near the description of each method.

COMMENT_6: Also, a detailed legend of figures 1 and 2 is presented in lines 194 to 204. It seems misplaced. A legend page is needed.

RESPONSE_6: We have corrected the location of tables and figures’ legends according to submission guidelines.

COMMENT_7: Page 10, Table 3 – What is the meaning of the asterisk? It should be mentioned in the legend.

RESPONSE_7: The asterisk in Table 3 marked an observer-dependence. It is now explained in the legend of Table 3.

COMMENT_8: Page 12, Line 208 – The zero after 5 is not necessary. Please exclude.

RESPONSE_8: Corrected.

3) RESULTS

COMMENT_9: a) Page 13, 1st paragraph – In the selection process, you excluded 11 of 31 controls. This seems to be related to your confidence interval criteria (18-31%), maybe being too strict. Probably a comment is necessary.

RESPONSE_9: Indeed, only 20 of the 31 controls were included in the analysis process. This was due to the fact that as we retrospectively sought for the controls, we primarily enrolled individuals who were initially evaluated as “no cardiac disease.” (31 controls). However, upon careful reviewing their medical history and CMR examinations, we excluded individuals whose morphological features and/or further clinical follow-up were found significant (e.g., borderline myocardial hypertrophy, coronary artery disease found in the course of further testing or medical history suggesting other systemic diseases, etc.) so that the control group was unequivocally healthy. Hence 20 controls were finally considered.

As a result, five controls were excluded from further analysis due to morphological considerations upon CMR review, three controls due to cardiovascular disease confirmed upon further evaluation (i.e., significant coronary artery disease), two controls due to significant imaging artifacts (precluding adequate LVM estimation), and one control due to death prior to the beginning of the study. Hence, we believe that this qualification criterion, though it might seem strict, is probably adequate, based on the careful review of the control group.

Accordingly, we added the information clause: “The study was planned and performed following the European Association of Cardiovascular Imaging (EACVI) cardiac diagnostics guidelines and the Polish National Health Fund. Thus, the CMR scans were performed as part of the standard out- and inpatients cardiac diagnostic process.” to the first paragraph of the “Materials and Methods” section and supplement the manuscript with the figure facilitating understanding the group enrollment process.

COMMENT_10: Page 14 – Please clarify the meaning of the asterisk signal in Table 4.

RESPONSE_10: The asterisk in Table 4 marked the level of significance of comparisons by the U-Mann-Whitney test, and (**) by the Chi-square test. The meaning of the asterisk, as well as double asterisk, was explained in the legend of Table 4.

COMMENT_11: Page 14, Table 4 and lines 249 and 250 – The important point is that the Philips method could not differentiate dilated cardiomyopathies from noncompaction cardiomyopathy, so it may not be clinically applicable. Can you comment on that?

RESPONSE_11: To our knowledge, it is not easy to differentiate between LVNC and nDCM due to (i) overlapping syndrome LVNC/nDCM (DOI: 10.12659/MSM.909172), genetic and clinical similarities (DOI: 10.17219/acem/67457), and possible changing in phenotype (DOI: 10.1016/j.yjmcc.2010.03.00). In turn, one of the assumptions of our research was to verify if the automatic method of measurement of the extent of NCM may potentially influence the LVNC diagnosis in comparison to the one presented by Jacquier et al., in the context of the new approach to NCM evaluation proposed recently by Contour et al. We used the software utilizing the method of LV masses assessment by Hautvast et al. available at our center (not introduced to LVNC diagnosis before).

Our study clearly shows that our method easily differentiates between the LVNC group and the controls. Doubts arise with regard to differentiation between the LVNC with enlarged LV subgroup (LVNCDCM) and nDCM. In our opinion, this problem requires a critical approach and discussion because of the frequent use of Jacquier’s method in researches on LVNC.

The method we propose is less dependent on the observer, and thus, the risk of NCM measurement error is reduced. In turn, it might help differentiate LVNC from other clinical conditions and standardize measurements between clinical centers.

However, the differentiation between LVNCDCM and nDCM is problematic, and the clinical similarity of the two cardiomyopathies, in the light of continued doubts about the impact of LV hypertrabeculation itself on disease outcome, raises a diagnostic and therapeutic question of whether LVNCDCM and nDCM are to be differentiated at all.

As we were probably the first to use Hautvast’s algorithm to differentiate between LVNC and the controls or nDCM, the potential clinical application requires further research. It is also possible that, in combination with the clinical symptoms, and LGE or T1 mapping, Hautvast’s algorithm might potentially contribute to better cardiovascular risk stratification in LVNC.

COMMENT_12: Page 16, Lines 274 - 281. Please exclude these lines from the main text and include them in the legends page.

RESPONSE_12: In accordance with the PLOS ONE’s guidelines, we have placed all tables immediately after the paragraph of their first citation. The tables’ legends are located right under the appropriate tables.

COMMENT_13: You present in your Tables 4, 5 and 6 two measurements for EDV. One obtained from traditional manual tracing and the second one from Philips software. Why are they so different? From Table 3 definitions, in the Philips algorithm, the endocardial exclusion of papillary muscles only, LVNCDCM decreased 80mL and nearly 40mL in LVNCN.

RESPONSE_13: The difference between the two methods is related not only to LV papillary muscles. The presented Hautvast’s algorithm counts voxels according to specific signal intensity. In normal conditions, the border between layers is clearly visible thanks to not excessive trabeculation and well-developed (not fragmented) papillary muscles. The difference between the two end-diastolic volumes is about 20-25ml, what is mostly dependent on the papillary muscles’ volume. The difference, however, increases with papillary muscles fragmentation and the extend of trabeculation (note that the LVNC group had the most excessively fragmented papillary muscles, most excessive trabeculation, and technically speaking, artifacts were also visible mostly in the LVNC group, though it was hard to compare the examined groups in terms of artifacts). The volume difference is probably dependent on (i) intracavitary blood artifacts, (ii) blurred border between muscle tissue and blood due to artifacts, (iii) smaller voxel intensity difference between the tissue of the trabeculae or fragmented papillary muscle parts due to very intensive trabecular net and narrow intertrabecular recesses, and (iv) blurred interlayer border due to excessive and irregularly penetrating LV compacted layer tissue recesses. Especially in situations mentioned above the error related to possible improper voxel accounts in favor of muscle tissue may significantly decrease EDV estimated by the Hautvast’s algorithm (algorithm dependence). The volume difference was also greater In the presence of trabecular net morphology of the LV only seen in the LVNC group, especially in LVNCDCM. We think that our hypotheses on the significance of these observations on results should be taken into account in further studies.

COMMENT_14: Page 19, Lines 309-310 – Correlation between NCMph/LVMph and EF was at most modest (r=0.44) although significant but only for the overall group. I suggest presenting all correlations since no data from Jaquier’s method is presented. So, your conclusions on Page 23-24 can not be corroborated by the evidence presented.

RESPONSE_14: According to Reviewer’s suggestion, we added information on correlations other than NCMPh/LVMPh and EF.

COMMENT_15: Page 19, lines 313-315. The cut-off values are isolated with no context. Please expand the explanation of these values presented.

RESPONSE_15: The cut-off values for the extent of NCM in LVNC serve to compare them with the respective cut-off values of Jacquier’s method. Therefore, we supplemented the paragraph with the respective cut-off values, according to Jacquier's approach, related to the examined population, as well as to the values presented in Jaquier’s original publication.

COMMENT_16: Page 19, Table 7. Can you provide an explanation for the interobserver value of 9.9% for NCM, since it is a semiautomatic method? Since the same slice was used in the comparative analysis and the explanation provided in Pages 393-395 do not apply.

RESPONSE_16: We would like to point out that the whole assessment of the NCM and NCM/LVM was based on a stack of short-axis slices so the risk of error was additively increased, and the NCM value of 9.9% related to Jacquier method which is more observer-dependent. In the revised version of the manuscript, according to the comment of Reviewer 2, we changed the method of intra- and interobserver variability measurement. The repeatability of NCMJ measurement, however, remained very low. Additionally, to improve the readability of the manuscript and distinction of the two methods quoted therein, we decided to unify the designations by changing the name of the Philips’s to Hautvast’s method, noting that Hautvast et al. first described the algorithm, while it is only implemented into the Philips software.

II. Responses to Reviewer 2:

COMMENT_1: I’m concerned with the author’s aims/conclusions regarding the relationship between this new quantification and LV remodeling. As acknowledged by the authors, evidence from the largest cohorts of LVNC patients indicated the degree of LV trabeculation seems to have no prognostic impact on cardiovascular events (neither Petersen’s nor Jaquier’s method). Other studies investigating extracellular matrix by CMR T1 mapping (Araujo-Filho et al, EHJ, 2018) and microcirculation/metabolism by PET (Jenni et al, JACC, 2002; Tavares-Melo et al, EHJ, 2017) failed to find a significant relationship with the amount of trabeculations. This supports that the degree of LV trabeculation is not a mediator of adversity. The authors indeed show a statistically significant (but clinically questionable) correlation between EDV and LV trabeculation using the algorithm. This is expected once the source of variability of the Jaquiers’s method is removed, but the authors should be careful with the potential prognostic impact of this new approach and may highlight more its potential diagnosis role.

RESPONSE_1: The aim of our study was to compare Jacquier’s method (as one of the currently used methods of NCM measurements) to the computed algorithm by Hautvast et al. We demonstrated that the degree of observer-dependency may affect the results obtained. We have considered EDV and EF as important parameters related to adverse outcomes in many cardiac diseases, also cardiomyopathies. In conclusion, we demonstrated the possible consequences of choosing one approach vs. the other. To highlight a more diagnostic role of our paper, we drew attention to this fact in the conclusions section.

COMMENT_2: “23 examinations were excluded from further analysis due to death before the study qualification, doubtful diagnosis, CMR artifacts and additional cardiac diseases which could influence the group qualification”. Why did the control group have the largest proportional exclusion (from 31 to 21)? Would this affect the found cut-off value?

RESPONSE_2: Indeed, only 20 of the 31 controls were included in the analysis process. This was due to the fact that as we retrospectively sought for the controls, we primarily enrolled individuals who were previously evaluated as “no cardiac disease.” (31 controls). However, upon careful reviewing their medical history and CMR examinations, we excluded individuals whose morphological features and/or further clinical follow-up were found significant (e.g., borderline myocardial hypertrophy, coronary artery disease found in the course of further testing or medical history suggesting other systemic diseases, etc.) so that the control group was unequivocally healthy. Hence 20 controls were finally considered.

We want to pay your attention to the fact that not before, but after forming the control group, the NCM/LVM cut-off value of 31% was calculated. Moreover, establishing the LVNC study criterion and the manner of its calculation, we took into consideration the facts that (i) the currently used criterion of LVNC proposed by Jacquier et al. did not fully meet its task, (ii) the so-called “gold standard” criterion of the LVNC recognition has not been established yet, and we did not take into account the LGE in qualifying patients for the LVNC group as an additional criterion. Moreover, the value we adopted influenced only two individuals from the entire LVNC group, which were still clinically questionable.

COMMENT_3: “...in the LVNC group 2 individuals had prior myocardial inflammation”. Do the authors mean previous myocarditis before a diagnosis of LNVC? If yes, how confident are they with the diagnosis of LVNC in these 2 cases?

RESPONSE_3: Generally, we qualified individuals for the LVNC group by carefully revising their prior diagnosis, available medical history, and available additional test results, including echocardiography and CMR. Moreover, in the group qualification process, three independent observers took part, and medical consultations were held to ensure proper group qualification when doubts arose. The co-occurrence of LVNC and myocarditis (especially due to viral infections) is reported in the medical literature. Some case reports pointed out the possible co-existence of these two clinical conditions 1,2,3. On the other hand, some reports suggested an overlooked diagnosis of LV hypertrabeculation/LVNC or misinterpreted LV hypertrabeculation/LVNC as myocarditis 4. We also took into account the possibility of misdiagnosis of myocarditis in CMR 5. Taking the above-mentioned into account in combination with the experience of the cardiologist reading CMR images, based on abnormalities seen in a heart affected by myocarditis 6, we were convinced that the diagnosis of LVNC has been overlooked in these two cases. We would like to point out that two more cases were previously excluded from further analysis from the nDCM group due to prior myocarditis. None of them had excessive trabeculation.

1Patil KG et al. (2014). Left ventricular non-compaction with viral myocarditis: A rare presentation of a rarer disease. J Assoc Physicians India. 62:261-3.

2Oguz K et al. (2015) Which one is Worse? Acute Myocarditis and Co-existing Non-compaction Cardiomyopathy in the Same Patient. J Clin Diagn Res. 9(6): OJ01. DOI: 10.7860/JCDR/2015/11774.6033.

3Dobranici M et al. (2012). Genetic disorder or toxoplasma myocarditis: a case report of dilated cardiomyopathy with hypertrabeculation in a young asymptomatic woman. 5(1):110‐113.

4Stöllberger C et al. (2006). Pitfalls in the diagnosis of left ventricular hypertrabeculation/non-compaction. Postgrad Med J. 82:679–683. DOI: 10.1136/pgmj.2006.046169.

5Emrich T et al. (2015). Cardiac MR enables diagnosis in 90% of patients with acute chest pain, elevated biomarkers and unobstructed coronary arteries. Br J Radiol 88:20150025. DOI: 10.1259/bjr.20150025.

6Matthias G et al. (2013). Cardiac Magnetic Resonance Assessment of Myocarditis. Circulation: Cardiovascular Imaging. 6:833–839. DOI: 10.1161/CIRCIMAGING.113.000416.

COMMENT_4: “Independent t-Student and U-Mann-Whitney tests were used where appropriate.” The authors should review the statistical approach for multiple comparisons in the subgroup analysis.

RESPONSE_4: Being very thankful for statistical suggestions, we re-discussed the choice of the statistical tests with our statisticians and worked out that it would probably be better to perform the ANOVA testing for multiple comparisons in the subgroup analysis. Following Reviewer’s suggestion, we re-analyzed subgroups and substituted current data in Tables 5 and 6 with data obtained from the ANOVA approach and, if applicable, we have adjusted the “Results” section.

COMMENT_5: “The Chi-squared test was used for nonparametric data.” Please review this statement since the chi-squared test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories.

RESPONSE_5: Agreeing with Reviewer’s suggestion, we have replaced the phrase “The Chi-squared test was used for nonparametric data” with: “Differences between categorical variables were tested with the chi-square test.”

COMMENT_6: e) “In order to determine the cut-off value for the pathological trabecular mass in our population. According to Jacquier's method, apart from the mean ± SDs of NCMJ/LVMJ, the upper confidence interval (+95% CI) was assessed in our control group, following Choi et al.”. Those cited authors seem to have used receiver operating characteristics (ROC) curves to determine the optimal cut-off values. Please review the reference.

RESPONSE_6: We used Jacquier’s approach as a comparative method to Hautvast’s algorithm, but with different cut-off values obtained by the estimation of the upper confidence interval of NCM in the control group. In this context, the reference was indeed incorrect. The error was corrected in the manuscript to “Amzulescu et al.” (ref. 4).

COMMENT_7: “Finally, a multivariate regression analysis model was created to estimate the potential influence of the examined parameters on EF and EDV”. Please provide the independent variables which were included in the model.

RESPONSE_7: We have changed the paragraph and added more information on the variables we used to create the model.

COMMENT_8: g) “The F-Snedecor test and t-Student test were used to compare the accuracy of the two analyzed methods.”. Please review if these tests are appropriate to investigate accuracy.

RESPONSE_8: We are again very thankful for statistical suggestions, we re-discussed the choice of the statistical tests with our statisticians, and we worked out that it would probably be better to perform an interclass correlation test (ICC) in assessing the accuracy and to calculate the coefficient of variation. For this purpose, we repeated the statistical analysis and substituted Table 7 with new results in the Inter- and Intra-observer section.

Grateful for comments and suggestions,

The authors

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PLoS One. doi: 10.1371/journal.pone.0230134.r003

Decision Letter 1

Otavio Rizzi Coelho-Filho

24 Feb 2020

Influence of observer-dependency on left ventricular hypertrabeculation mass measurement and its relationship with left ventricular volume and ejection fraction - comparison between manual and semiautomatic CMR image analysis methods

PONE-D-19-30605R1

Dear Dr. Kubik,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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Academic Editor

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Reviewer #2: The authors of the paper entitled "Influence of observer-dependency on left ventricular hypertrabeculation mass measurement and its relationship with left ventricular volume and ejection fraction - comparison between manual and semiautomatic CMR image analysis methods" have addressed all my comments/suggestions.

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PLoS One. doi: 10.1371/journal.pone.0230134.r004

Acceptance letter

Otavio Rizzi Coelho-Filho

27 Feb 2020

PONE-D-19-30605R1

Influence of observer-dependency on left ventricular hypertrabeculation mass measurement and its relationship with left ventricular volume and ejection fraction – comparison between manual and semiautomatic CMR image analysis methods

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Group qualification follow chart.

The stages of group qualification are marked with italics. LVNC–left ventricular noncompaction; nDCM–nonischemic dilated cardiomyopathy; Control–control group; pts.–patients.

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S1 Table. Pearson's correlation between left ventricular noncompaction mass and left ventricular end-diastolic volume or ejection fraction.

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S2 Table. Regression analysis models.

(DOCX)

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Attachment

Submitted filename: Response to Reviewers.docx

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Influence of observer-dependency on left ventricular hypertrabeculation mass measurement and its relationship with left ventricular volume and ejection fraction – comparison between manual and semiautomatic CMR image analysis methods

Marcin Kubik

Alicja Dąbrowska-Kugacka

Karolina Dorniak

Marta Kutniewska-Kubik

Ludmiła Daniłowicz-Szymanowicz

Ewa Lewicka

Edyta Szurowska

Grzegorz Raczak

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Table 1. Review of the most popular left ventricular noncompaction (LVNC) recognition criteria using cardiac magnetic resonance (CMR) examination.

Materials and methods

Study design

Table 2. Left ventricular noncompaction (LVNC), nonischemic dilative cardiomyopathy (nDCM) and control group inclusion criteria.

Acquisition and analysis of CMR data

The estimate of CLMJ, NCMJ, and NCMJ/LVMJ

Fig 1. Diagnostic scheme of a noncompacted layer mass assessment by Jaquier's method.

The estimate of CLMH, NCMH, NCMH/LVMH

Fig 2. Diagnostic scheme of a noncompacted layer mass (NCM) assessment by Hautvast’s computed algorithm method.

Table 3. The detailed comparison between the two methods of the trabecular mass measurement: By Jacquier et al. and by the semi-automatic Hautvast’s algorithm implemented into Philip’s CMR software [10,12].

The estimate of EF

Data collection and statistical analysis

Inter- and intra-observer measurements

Results

Table 4. Comparison of the whole left ventricular noncompaction group (LVNC) with the non-ischemic dilative cardiomyopathy (nDCM) and the control group.

Fig 3. The graphical distribution of the left ventricular trabeculation in the left ventricular noncompaction group.

The subgroup comparison

Table 5. Comparison of left ventricular noncompaction subgroup with normal-range left ventricle (LVNCN) and the control group.

Table 6. Comparison of left ventricular noncompaction subgroup with enlarged left ventricle (LVNCDCM) and dilated cardiomyopathy (nDCM) group.

Correlation analysis

Regression analysis

Cut-off values of noncompacted mass measurements between the LVNC and the control groups (ROC analysis)

Table 7. The cut-off values of left ventricular noncompaction mass (NCM) between the left ventricular noncompaction group (LVNC) and the control group–ROC analysis.

Inter- and intra-observer variability

Table 8. Comparison of the inter- and intra-observer variability between the method based on Hautvast’s computed algorithm and Jacquier’s approach of noncompacted myocardial mass evaluation.

Discussion

Clinical implications

Limitations

Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Otavio Rizzi Coelho-Filho

Roles

Author response to Decision Letter 0

Decision Letter 1

Otavio Rizzi Coelho-Filho

Roles

Acceptance letter

Otavio Rizzi Coelho-Filho

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Cited by other articles

Links to NCBI Databases

The estimate of CLM_J, NCM_J, and NCM_J/LVM_J

The estimate of CLM_H, NCM_H, NCM_H/LVM_H

Table 5. Comparison of left ventricular noncompaction subgroup with normal-range left ventricle (LVNC_N) and the control group.

Table 6. Comparison of left ventricular noncompaction subgroup with enlarged left ventricle (LVNC_DCM) and dilated cardiomyopathy (nDCM) group.