Abstract
Background
High-resolution computed tomography (HRCT) is recommended for early diagnosis and monitoring in idiopathic inflammatory myopathy (IIM)-associated interstitial lung disease (ILD). Multiparameter spectral computed tomography (CT) has demonstrated the potential to assess lung disease. However, the application of quantitative spectral CT biomarkers in IIM-associated ILD remains underexplored. This study aimed to investigate the performance of quantitative spectral CT biomarkers in characterizing and differentiating between patients with antisynthetase syndrome (ASS)-associated ILD and those with polymyositis/dermatomyositis (PM/DM)-associated ILD.
Methods
This retrospective study enrolled 164 patients treated with ASS or PM/DM who underwent noncontrast dual-layer spectral CT (DLCT) between March 2024 and September 2024. After 15 cases were excluded (age <18 years, n=3; malignancy, n=2; nonclinical ILD, n=10), 68 patients with ASS-associated ILD and 81 with PM/DM—associated ILD were analyzed. Quantitative parameters including lung lobe volume, monoenergetic CT number (MCTN) at 70 keV, effective atomic number (Zeff), and electron density (ED) of each lung lobe were compared between the two groups. The distribution of ILD pattern was also compared.
Results
The ILD pattern distribution did not differ significantly between the two groups. Meanwhile, in the analysis of spectral CT parameters, patients with ASS, as compared to the those with PM/DM, demonstrated significantly reduced lung volume [right lower lobe (RLL): 455.72±232.00 vs. 589.85±225.66 cm3, P<0.001; left lower lobe (LLL): 418.33±214.43 vs. 535.92±203.30 cm3, P<0.001], a higher MCTN (RLL: −708.80 vs. −743.10 HU, P=0.042; LLL: −705.10 vs. −745.40 HU, P=0.008), and higher ED [RLL: 29.65 vs. 26.10 percentage of electron density relative to water (%EDW), P=0.041; LLL: 2 9.80 vs. 26.00 %EDW, P=0.008]. In the subgroup analysis of nonspecific interstitial pneumonia (NSIP)-dominant cases, these differences persisted for the left lower-lobe (ED: 27.70 vs. 24.35 %EDW, P=0.033).
Conclusions
Patients with ASS-associated ILD, as compared to those with PM/DM-associated ILD, had reduced volume, enhanced MCTN, and greater ED of the lower lung lobes. The difference in disease severity between these patient groups could be detected by spectral CT parameters.
Keywords: Myositis; antisynthetase syndrome (ASS); lung diseases, interstitial; dual-energy
Introduction
Polymyositis (PM), dermatomyositis (DM), and antisynthetase syndrome (ASS) are the most common subtypes of idiopathic inflammatory myopathy (IIM), which is an autoimmune disease characterized by the inflammation of skeletal muscles and extramuscular manifestations including skin involvement, cardiac involvement, and lung involvement (1). Interstitial lung disease (ILD) is the most serious complication and the major cause of morbidity and mortality in both patients with PM/DM and those with ASS (2). Identifying and quantifying the characteristics of ILD in patients with PM/DM or ASS is critical for both disease monitoring and for the evaluation of treatment response. Although there are differences in disease-specific autoantibodies between patients with PM/DM and those with ASS (e.g., ASS is characterized by the presence of anti-aminoacyl-transfer synthetase), certain clinical features, such as cutaneous manifestations, overlap, making effective classification challenging (3-5). Moreover, testing for disease-specific autoantibodies is not commonly available. Therefore, there is a need to develop a more convenient method for identifying IIM subtypes. In this study, we aimed to examine relevant biomarkers from a radiological perspective.
High-resolution computed tomography (HRCT) is the gold standard for the early diagnosis and identification of different ILD patterns. However, the most commonly used HRCT-based methods are visual measurement and semiquantitative analysis (6-8), which have relatively low reproducibility and often require experienced specialists (9). Recently, a few computer-based automatic methods (10-13) were developed for ILD assessment, but these may require dedicated software and a relatively longer procedure.
Dual-layer spectral computed tomography (DLCT) can not only provide images similar to those of conventional computed tomography (CT) but can also generate quantitative parameters based on substance composition evaluation (14). Spectral CT is capable of creating virtual monoenergetic images, effective atomic number (Zeff) maps, and electron density (ED) maps. Zeff reflects the intrinsic compositional properties of a material and has recently been used to evaluate the severity of connective tissue disease-associated ILD (CTD-ILD) (15). ED maps can detect lung lesion extent with high sensitivity (16), but its use as a quantitative indicator in ILD assessment has not been examined. Monoenergetic CT number (MCTN) represents the true attenuation of materials to X-ray beams as compared with the CT number from conventional CT. However, to our knowledge, few studies have investigated the use of quantitative imaging indicators derived from spectral CT to differentiate between ASS and PM/DM.
Therefore, we aimed to investigate the performance of quantitative image indicators derived from the spectral CT (i.e., MCTN, Zeff, and ED) and lung volume in characterizing and differentiating between patients with ASS-associated ILD and PM/DM-associated ILD. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1741/rc).
Methods
Patients
This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the West China Hospital of Sichuan University Biomedical Research Ethics Committee (approval No. 2022-341). This retrospective analysis included data from a larger, ethically approved cohort study of immune-related ILD, and thus the requirement for individual informed consent was waived. A total of 164 consecutive patients with ASS or PM/DM who underwent dual-layer spectral chest CT between March 2024 and September 2024 were enrolled in this study. The exclusion criteria were as follows: (I) age <18 years; (II) a history of malignancy; and (III) normal, subclinical, or preclinical interstitial lung abnormality (ILA) diagnosed by HRCT. Finally, 68 patients with ASS-associated ILD and 81 patients with PM/DM-associated ILD were enrolled. A flowchart of patient recruitment is presented in Figure 1. The diagnosis of ASS was based on the criteria proposed by Connors et al. (17), and patients with PM/DM were diagnosed according to the Bohan and Peter criteria, modified Sontheimer criteria, and 224th European Neuro Muscular Centre International Workshop guidelines (18-20). ILD was diagnosed based on the results of chest HRCT and multidisciplinary group discussion (21,22).
Figure 1.
Flowchart of patient recruitment and assessment. ASS, antisynthetase syndrome; CT, computed tomography; ED, electron density; MNCT, monoenergetic computed tomography number; ILD, interstitial lung disease; PM/DM, polymyositis/dermatomyositis; Zeff, effective atomic number.
Imaging procedure
All nonenhanced chest CT images were obtained with a DLCT scanner (Spectral CT 7500, Philips Healthcare, Amsterdam, the Netherlands) under the following parameters: tube voltage, 100 kV; automatic tube current, 223 mA (80–130 mAs); and reconstruction matrix, 1,024×1,024. Images were reconstructed via the iDose 5 algorithm (Philips Healthcare) with a Y-sharp filter, 1-mm slice thickness, and 1-mm increment reconstruction. Both conventional CT images and spectral-based images (SBIs) were generated.
Image analysis
Quantitative imaging parameters
All SBI scans were transferred to the IntelliSpace Portal (Philips Healthcare) workstation for postprocessing and analysis. The clinical information of patients was anonymized . First, the lung segmentation was performed automatically via the chronic obstructive pulmonary disease (COPD) module in the workstation. After segmentation, five lung lobes including the right upper lobe (RUL), right middle lobe (RML), right lower lobe (RLL), left upper lobe (LUL), and left lower lobe (LLL) were identified, with the airways excluded. The segmentation results were manual verified by two thoracic radiologists (with 8 and 12 years’ experience, respectively). With the segmentation results, the quantitative parameters of lung lobe volume, MNCT at 70 keV, Zeff, and ED of each lung lobe were obtained in the Spectral CT Viewer module of the workstation (Figure 2).
Figure 2.
Histogram of the spectral CT parameters of electron density and effective atomic number in each lung lobe in a 55-year-old female patient with PM/DM. (A) The 3D lung lobe segment results from the COPD module and the coronal and axial HRCT images of the chest. The histogram of electron density and effective atomic number in RUL, RML, RLL, LUL, and LLL are displayed in (B-F) respectively. %EDW, percentage of electron density relative to water; 3D, three-dimensional; COPD, chronic obstructive pulmonary disease; CT, computed tomography; LLL, left lower lobe; LUL, left upper lobe; PM/DM, polymyositis/dermatomyositis; RLL, right lower lobe; RML, right meddle lobe; RUL, right upper lobe.
ILD pattern
The HRCT-based radiological patterns of ILD in all patients were categorized as nonspecific interstitial pneumonia (NSIP), usual interstitial pneumonia (UIP), organizing pneumonia (OP), NSIP with OP, or UIP with OP according to the criteria proposed by the American Thoracic Society/European Respiratory Society guidelines (23).
Statistical analysis
All statistical analyses were performed with SPSS 26.0 software (IBM Corp., Armonk, NY, USA). All continuous variables were checked for normality via the Kolmogorov-Smirnov test and are presented as the mean ± standard deviation or as the median [interquartile range (IQR)], as appropriate. Categorical data are presented as absolute frequencies and proportions. For comparing continuous variables statistically, the independent samples t-test was applied to data with a normal distribution, while the Mann-Whitney test was applied for data that were not normally distributed. The comparison of categorical variables was performed via the chi-squared test. A P value <0.05 was considered statistically significant.
Results
Comparison of demographic and clinical features between the ASS and PM/DM groups
A total of 149 patients were enrolled, among whom 68 were diagnosed with ASS and 81 with PM/DM. The mean age in the ASS and PM/DM groups was 55.24 years and 52.33 years, respectively (P=0.068). The majority of patients were female, with a 72.6% female prevalence in the ASS group and 75.31% in the PM/DM group (P=0.653). There were no statistically significant differences in respiratory symptoms between the ASS and PM/DM groups. Diabetes mellitus was the most common comorbidity in PM/DM group (14.8%), while hyperlipidemia was the most common comorbidity in the ASS group (8.82%). No significant difference was found between the ASS and PM/DM groups in terms of disease duration or pulmonary function test parameters. It should be noted that not all patients (ASS group: n=38; PM/DM group: n=47) in the retrospective cohort underwent pulmonary function tests proximate (±4 weeks) to spectral CT examination. The demographic and clinical features of the ASS and PM/DM groups are summarized in Table 1.
Table 1. Demographics and clinical characteristics of all patients (N=149).
| Variable | ASS (n=68) | PM/DM (n=81) | P |
|---|---|---|---|
| Age (years) | 55.24±9.13 | 52.33±9.95 | 0.068 |
| Sex | 0.653 | ||
| Female | 49 (72.06) | 61 (75.31) | |
| Male | 19 (27.94) | 20 (24.69) | |
| Respiratory symptom | |||
| Cough | 23 (33.82) | 19 (23.46) | 0.161 |
| Dyspnea | 24 (35.29) | 24 (29.63) | 0.461 |
| Comorbidity | |||
| Diabetes mellitus | 0 | 12 (14.81) | <0.001 |
| Hypertension | 1 (1.47) | 8 (9.88) | 0.072 |
| Hyperlipidemia | 6 (8.82) | 11 (13.58) | 0.363 |
| Disease duration (months) | 24 [12, 36] | 15 [6, 40] | 0.109 |
| Pulmonary function test | (N=38) | (N=47) | |
| FVC% | 83.33±18.52 | 89.54±16.84 | 0.133 |
| FEV1% | 83.50±16.74 | 89.16±17.72 | 0.159 |
| DLCO% | 62.97±19.01 | 64.93±16.70 | 0.638 |
Data are presented as mean ± standard deviation, n (%), or median [interquartile range]. ASS, antisynthetase syndrome; DLCO%, percentage of the predicted diffusion capacity for carbon monoxide; FEV1%, percentage of forced expiratory volume in the first second; FVC%, percentage of the predicted forced vital capacity; PM/DM, polymyositis/dermatomyositis.
Comparison of ILD patterns in the ASS and PM/DM groups
The distribution of ILD patterns in two groups is presented in Table 2. In the ASS group, the most prevalent ILD pattern was NSIP (73.53%), followed by NSIP + OP (16.18%), UIP (5.88%), OP (1.47%), and UIP + OP (16.18%). In the PM/DM group, the most prevalent ILD pattern was also NSIP (71.60%), followed by NSIP + OP (22.22%) and UIP (6.17%). No OP or UIP + OP patterns were observed in the PM/DM group. The difference in distribution of ILD pattern between the two groups was not statistically significant (P=0.394) (Figure 3).
Table 2. The distribution of ILD pattern in the two groups.
| Group | NSIP | UIP | OP | UIP + OP | NSIP + OP | P |
|---|---|---|---|---|---|---|
| ASS | 50 (73.53) | 4 (5.88) | 2 (2.94) | 1 (1.47) | 11 (16.18) | 0.394 |
| PM/DM | 58 (71.60) | 5 (6.17) | 0 | 0 | 18 (22.22) |
Data are presented as n (%). ASS, antisynthetase syndrome; ILD, interstitial lung disease; NSIP, nonspecific interstitial pneumonia; OP, organizing pneumonia; PM/DM, polymyositis/dermatomyositis; UIP, usual interstitial pneumonia.
Figure 3.
Comparison of ILD pattern distribution between the ASS and PM/DM groups. ASS, antisynthetase syndrome; ILD, interstitial lung disease; NSIP, nonspecific interstitial pneumonia; OP, organizing pneumonia; PM/DM, polymyositis/dermatomyositis; UIP, usual interstitial pneumonia.
Comparison of spectral quantitative imaging parameters of each lung lobe in the ASS and PM/DM groups
The differences in quantitative imaging parameters from spectral CT of each lung lobe and the whole lung between the ASS and PM/DM groups are presented in Table 3. Patients with ASS, as compared to patients with PM/DM, had a lower volume, higher MCTN, and a higher ED value in both the RLL and LLL (all P values <0.05). Meanwhile, the Zeff of the bilateral LLLs showed no statistically difference between two groups. The volume, MCNT, Zeff, and ED of the RUL, RUM, LUL, and bilateral lung were not statistically significant different between the two groups (Figure 4).
Table 3. Spectral CT parameters for each lung lobe in the ASS and PM/DM groups.
| Lung lobe | DLCT parameter | ASS (n=68) | PM/DM (n=81) | P |
|---|---|---|---|---|
| RUL | Volume (cm3) | 832.33±257.93 | 759.51±233.12 | 0.072 |
| MCTN (HU) | −834.25 (−859.80, −797.72) | −829.20 (−857.00, −797.30) | 0.565 | |
| Zeff | 5.56 (5.51, 5.69) | 5.60 (5.53, 5.71) | 0.589 | |
| ED (%EDW) | 17.30 (14.60, 20.88) | 17.80 (14.90, 21.00) | 0.543 | |
| RML | Volume (cm3) | 345.04±132.39 | 340.19±131.63 | 0.824 |
| MCTN (HU) | −823.15 (−848.20, −754.65) | −833.00 (−852.70, −785.40) | 0.173 | |
| Zeff | 5.69 (5.60, 5.89) | 5.65 (5.58, 5.81) | 0.321 | |
| ED (%EDW) | 18.05 (15.62, 25.02) | 17.20 (15.10, 22.00) | 0.196 | |
| RLL | Volume (cm3) | 455.72±232.00 | 589.85±225.66 | <0.001 |
| MCTN (HU) | −708.80 (−758.73, −590.58) | −743.10 (−801.30, −652.10) | 0.042 | |
| Zeff | 6.02 (5.87, 6.24) | 5.94 (5.81, 6.26) | 0.317 | |
| ED (%EDW) | 29.65 (24.60, 41.50) | 26.10 (20.40, 35.20) | 0.041 | |
| LUL | Volume (cm3) | 880.60±347.31 | 897.63±310.19 | 0.752 |
| MCTN (HU) | −827.40 (−861.05, −793.30) | −838.20 (−855.70, −804.60) | 0.577 | |
| Zeff | 5.75 (5.58, 5.87) | 5.71 (5.60, 5.83) | 0.782 | |
| ED (%EDW) | 17.75 (14.47, 20.97) | 16.80 (14.90, 20.00) | 0.528 | |
| LLL | Volume (cm3) | 418.33±214.43 | 535.92±203.30 | <0.001 |
| MCTN (HU) | −705.10 (−762.73, −562.65) | −745.40 (−799.60, −667.30) | 0.008 | |
| Zeff | 6.17 (5.95, 6.45) | 6.06 (5.88, 6.29) | 0.063 | |
| ED (%EDW) | 29.80 (24.35, 43.80) | 26.00 (20.50, 33.70) | 0.008 | |
| BL | Volume (cm3) | 2,888.49±842.72 | 3,130.90±957.85 | 0.106 |
| MCTN (HU) | −798.35 (−829.35, −736.98) | −801.60 (−832.10, −748.20) | 0.568 | |
| Zeff | 5.82 (5.72, 5.97) | 5.82 (5.70, 5.94) | 0.393 | |
| ED (%EDW) | 22.52 (19.23, 29.91) | 21.56 (17.40, 26.18) | 0.085 |
Data are presented as mean ± standard deviation or median (interquartile range). %EDW, percentage electron density relative to water; ASS, antisynthetase syndrome; BL, bilateral lung; CT, computed tomography; DLCT, dual-layer spectral CT; ED, electron density; HU, Hounsfield unit; LLL, left lower lobe; LUL, left upper lobe; MCTN, monoenergetic computed tomography number; PM/DM, polymyositis/dermatomyositis; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe; Zeff, effective atomic number.
Figure 4.
Box whisker plots demonstrating the differences in (A) lung volume, (B) MCTN at 70 keV, (C) Zeff, and (D) ED between the ASS and PM/DM in each lung lobe. *, a statistically significant difference between the two groups (P<0.05). %EDW, percentage electron density relative to water; ASS, antisynthetase syndrome; CT, computed tomography; ED, electron density; HU, Hounsfield unit; LLL, left lower lobe; LUL, left upper lobe; MCTN, monoenergetic computed tomography number; PM/DM, polymyositis/dermatomyositis; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe; Zeff, effective atomic number.
Subgroup analysis of spectral CT parameters in patients with ASS or PM/DM and the NSIP ILD pattern
Among patients with the NSIP pattern, there remained a lower lung volume , higher MCTN, and ED in the LLL of the ASS group as compared to the PM/DM group (all P values <0.05). Meanwhile, the ASS group had a higher RUL volume and a lower RLL volume compared with the PM/DM group (all P values <0.05). The ASS group had a higher ED as measured by percentage of electron density relative to water (%EDW) (median 28.30 %EDW, IQR, 23.52–37.80 %EDW) in the RLL compared with the PM/DM group (median 25.70 %EDW, IQR, 20.25–32.93 %EDW), but not significantly so (P=0.096). The detailed results are provided in Table 4 and Figure 5.
Table 4. Subgroup analysis of spectral CT parameters in patients with an NSIP ILD pattern in the ASS and PM/DM groups.
| Lung lobe | DECT parameter | ASS (n=50) | PM/DM (n=58) | P |
|---|---|---|---|---|
| RUL | Volume (cm3) | 868.18±271.35 | 762.58±212.03 | 0.025 |
| MCTN (HU) | −840.10 (−869.12, −818.50) | −835.85 (−861.60, −806.75) | 0.280 | |
| Zeff | 5.54 (5.50, 5.64) | 5.58 (5.51, 5.70) | 0.403 | |
| ED (%EDW) | 16.55 (13.75, 18.88) | 17.15 (14.53, 20.05) | 0.258 | |
| RML | Volume (cm3) | 363.54±144.80 | 350.05±134.87 | 0.618 |
| MCTN (HU) | −826.85 (−852.97, −798.65) | −840.55 (−853.75, −799.23) | 0.500 | |
| Zeff | 5.67 (5.54, 5.77) | 5.63 (5.57, 5.81) | 0.844 | |
| ED (%EDW) | 17.45 (15.17, 20.60) | 16.40 (15.10, 20.48) | 0.567 | |
| RLL | Volume (cm3) | 493.60±247.46 | 600.84±199.33 | 0.014 |
| MCTN (HU) | −722.80 (−770.72, −625.12) | −746.15 (−802.93, −675.83) | 0.102 | |
| Zeff | 6.00 (5.82, 6.17) | 5.91 (5.79, 6.12) | 0.461 | |
| ED (%EDW) | 28.30 (23.52, 37.80) | 25.70 (20.25, 32.93) | 0.096 | |
| LUL | Volume (cm3) | 935.43±343.61 | 905.45±279.91 | 0.618 |
| MCTN (HU) | −836.40 (−866.17, −803.17) | −843.80 (−856.40, −817.80) | 0.851 | |
| Zeff | 5.69 (5.55, 5.82) | 5.71 (5.59, 5.81) | 0.490 | |
| ED (%EDW) | 16.75 (13.83, 20.00) | 16.00 (14.80, 18.75) | 0.956 | |
| LLL | Volume (cm3) | 450.61±225.20 | 556.68±189.27 | 0.009 |
| MCTN (HU) | −727.75 (−763.18, −590.30) | −760.75 (−802.97, −696.20) | 0.029 | |
| Zeff | 6.12 (5.86, 6.38) | 6.01 (5.85, 6.18) | 0.200 | |
| ED (%EDW) | 27.70 (24.05, 41.20) | 24.35 (20.00, 30.87) | 0.033 | |
| BL | Volume (cm3) | 3,052.16±822.38 | 3,177.11±840.46 | 0.438 |
| MCTN (HU) | −807.00 (−840.00, −767.12) | −809.65 (−837.45, −772.70) | 0.968 | |
| Zeff | 5.79 (5.68, 5.92) | 5.79 (5.68, 5.88) | 0.689 | |
| ED (%EDW) | 21.42 (18.70, 26.59) | 19.96 (17.06, 24.32) | 0.212 |
Data are presented as mean ± standard deviation or median (interquartile range). %EDW, percentage electron density relative to water; ASS, antisynthetase syndrome; BL, bilateral lung; CT, computed tomography; DLCT, dual-layer spectral CT; ED, electron density; HU, Hounsfield unit; ILD, interstitial lung disease; LLL, left lower lobe; LUL, left upper lobe; MCTN, monoenergetic computed tomography number; NSIP, nonspecific interstitial pneumonia; PM/DM, polymyositis/dermatomyositis; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe; Zeff, effective atomic number.
Figure 5.
Box whisker plots demonstrating the subgroup analysis of spectral CT parameters of (A) lung volume, (B) MCTN at 70 keV, (C) Zeff, and (D) ED in each lung lobe between the ASS and PM/DM groups with an NSIP ILD pattern. *, a statistically significant difference between the two groups (P<0.05). %EDW, percentage electron density relative to water; ASS, antisynthetase syndrome; CT, computed tomography; ED, electron density; HU, Hounsfield unit; LLL, left lower lobe; LUL, left upper lobe; MCTN, monoenergetic computed tomography number; NSIP, nonspecific interstitial pneumonia; PM/DM, polymyositis/dermatomyositis; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe; Zeff, effective atomic number.
Comparison of spectral quantitative imaging parameters across lung lobes in the ASS or PM/DM groups
In both the ASS and PM/DM groups, a consistent gradient was observed. Lung volume was significantly lower in the lower lobes (RLL and LLL) than in the upper lobes (RUL and LUL) (all P values <0.001), while the MCTN and ED were significantly higher in the lower lobes (all P values <0.01) (Figure S1).
Discussion
In our study, we compared the quantitative imaging parameters derived from the DLCT between patients with ASS-associated ILD and those with PM/DM-associated ILD. The principal findings of our study indicated that among patients with ILD, those with ASS had a lower volume and higher MCTN and ED value in the bilateral LLLs as compared with patients with PM/DM. We have reason to believe that there is a difference in disease severity between ASS and PM/DM in the LLLs and that this the difference can be detected by DLCT.
To our knowledge, this is the first study to investigate the application of quantitative imaging parameters derived from noncontrast spectral CT in patients with ASS or PM/DM. A previous study on the clinical features of ASS and PM/DM found that ASS shares common clinical features with PM/DM, including ILD (24). Among patients with ILD, differentiating between those with ASS and those with PM/DM is critical for monitoring disease progression and informing drug selection. As it pertains to radiological patterns, ASS and PM/DM patients predominantly have the NSIP pattern, followed by the NSIP + OP or OP pattern, with the UIP pattern being relatively less frequent (25-28). This ILD pattern distribution in ASS and PM/DM patients is consistent with that found in our study. Furthermore, the distribution of HRCT-based ILD pattern between two groups was not significantly different. This suggests that the ILD pattern as assessed by HRCT may have limited utility in detecting differences in radiologic features between ASS and PM/DM patients.
Whereas conventional HRCT appeared to lack the ability to discriminate the ILD patterns between ASS and PM/DM, spectral quantitative imaging parameters were able to detect this difference according to the pathological basis of ILD. In our study, lung volume loss—especially in the lower lobes—was more severe in the ASS group than in the PM/DM group, even among patients with the NSIP pattern. This lung volume loss in ASS and PM/DM patients with ILD has been reported in previous research (8,29-32). The MCTN of the lower lung lobes was also higher in the ASS group than in the PM/DM group. The MCTN used in our study was at the 70-keV energy level, which has been demonstrated to have the highest accuracy (33). In ILD, inflammatory cell infiltration in the interstitial space promotes the infiltration of fibroblasts and the formation of fibrotic foci due to the production of extracellular matrix proteins by myofibroblasts (34-37). The MCTN increases with the formation of consolidations, ground-glass opacities, and reticulations, which can be observed on HRCT in patients with ILD (38).
In our study, the ED of the lower lobes in the ASS group was also larger than that of the PM/DM group. ED reflects the distribution of electrons in atoms or molecules and the probability of an electron being present in a specific region around the atomic nucleus (39). ED images have been primarily used in radiotherapy dose calculation. Recently, ED maps and ED values have been applied for diagnosing disc herniation (40), lymph nodes metastasis (14,41), and hematoma (42). ED varies with the location of the electron, elemental composition, and the structure of the tissue (14). In Daoud et al.’s study (16), ED maps provided improved contrast enhancement, a reduction in artifacts, and better characterization of tissues in lung lesion detection. Moreover, owing to dual-layer detector technology, the ED maps generated a strong noise-suppression effect, improving ground-glass opacity visualization. In other work, ED was found to vary with the different elemental composition and correlate with the solid component density of tissue (41,43). The elevated ED and MCTN observed in the lower lobes of patients with ASS likely reflects increased tissue density from parenchymal abnormalities. Pathologically, this could represent a variable combination of inflammatory cell infiltration, edema, fibroblastic proliferation, and extracellular matrix deposition. These processes are more characteristic of fibrotic phenotypes, such as those exhibiting the UIP pattern, whereas inflammatory patterns such as cellular NSIP may undergo attenuation changes driven primarily by cellularity and edema. Future studies with larger samples are needed to determine if spectral CT parameters can differentiate between these distinct pathological substrates.
In our study, ED appeared to be more sensitive than Zeff in detecting the differences in lung lesion between ASS and PMDM. Zeff reflects the substance composition of different materials (44). However, in our study, there were no difference in Zeff between the two groups for any of the lung lobes (all P values >0.05). This may be partly due to the fact that the ED images were not influenced by the noise induced by rib-relative hardening artifacts (or photon starvation artifact) in the lungs. Jeong et al. (39) compared ED images between the cervical spine and lumber spine and found that ED images are likely to be more effective in locations with higher noise levels. In Chen et al.’s study (15), Zeff exhibited an excellent ability to evaluate CTD-ILD severity, with a higher Zeff value, higher MCTN, and lower lung volume being observed in extensive CTD-ILD as compared to limited CTD-ILD. Unlike the patient enrollment in our study, which focused on ASS and PM/DM, that conducted in Chen et al.’s study consisted of patients with a variety of CTD subtypes and grouped patients according to a combined semiquantitative visual ILD staging system and pulmonary function parameters. Moreover, the scanner used was different from that in our study. It should also be noted that ASS and PM/DM are subtypes of idiopathic IIMs and share many commonalties. It is possible that the differences in the severity of ILD between ASS and PM/DM are not as significant as those between limited CTD-ILD and extensive CTD-ILD.
Even under the condition of NSIP, the volume, MNCT, and ED of the LLL differed between the ASS and PM/DM groups in our study. The reason why we focused on the NSIP pattern in our subgroup analysis is that the predominant ILD pattern in ASS or PM/DM patients is NSIP, and the change in lung volume or consolidation is lower in those with the NSIP pattern than i n those with the OP or NSIP + OP pattern; thus, there would be less bias produced due to individual differences. Furthermore, we found a difference in severity of lung lesion in the LLLs between the ASS and PM/DM groups. To our knowledge, no previous study has compared the disease severity between ASS-associated ILD and PM/DM-associated ILD, and the bulk of the related research has primarily investigated these features separately (8,38). According to our results, spectral quantitative imaging parameters are capable of detecting differences in disease severity. Furthermore, our findings demonstrate that quantitative spectral CT parameters can objectively assess disease severity and parenchymal characteristics in IIM-ILD. This supports their potential utility in monitoring the disease course and evaluating treatment response, complementing conventional clinical and functional assessments.
We further compared the parameters across lung lobes within each disease group and discovered a consistent severity gradient. The lower lobes, which are the most severely affected in IIM-ILD, exhibited the highest MCTN, ED, and Zeff values, along with the lowest volumes, with the upper lobes exhibiting the opposite pattern. This internal validation confirms that these quantitative parameters are sensitive to the gradient of parenchymal involvement. This property supports their potential utility in tracking changes in disease burden over time, whether in response to therapy or due to natural progression.
Beyond cross-sectional characterization, the quantitative nature of spectral CT parameters may hold significant value for longitudinal application. Serial measurements of lung lobe volume, MCTN, and ED could offer sensitive, objective tools for monitoring disease progression or regression in response to therapy within clinical trials. Future prospective studies with serial scans are needed to establish the responsiveness and minimal clinically important difference of these imaging biomarkers.
Certain limitations to this study should be examined. First, we employed a single-center design, which could have introduced selection bias. Our study was not sufficiently powered to analyze the difference between ILD patterns within each myositis subtype. Although the differences in radiologic features between the NSIP and UIP patterns have considerable prognostic implications, the small number of patients with a UIP-dominant pattern in our study (ASS: n=4; PM/DM: n=5) precluded a statistically reliable comparison. Future studies with larger, prospectively enrolled cohorts, specifically powered to compare ILD patterns, are essential to defining the distinct spectral CT profiles of patients with IIMs and the NSIP or UIP pattern. Second, the scanner in our study was a DLCT scanning system, and thus the results may differ for other dual-energy CT systems. Third, there was a lack of histopathological validation. Fourth, using a preliminary, cross-sectional design, we investigated the performance of quantitative imaging parameters derived from DLCT in differentiating between ASS-associated ILD and PM/DM-associated ILD. The utility of these spectral quantitative imaging parameters was not investigated for longitudinal end points such as mortality or treatment response. Fifth, standardized pulmonary function test data proximate to the CT scan were not consistently available for all patients in our retrospective cohort, which precluded a direct correlation between our imaging biomarkers and functional status. Future studies incorporating healthy controls and other ILD subtypes (e.g., idiopathic pulmonary fibrosis) are warranted to fully establish the diagnostic and differential diagnostic value of spectral CT parameters.
Conclusions
Our results suggest that among patients with ILD, those with ASS had a lower lung volume, higher MNCT, and greater ED in the lower lung lobes as compared to those with PM/DM. These differences could be detected by the quantitative spectral CT parameters.
Supplementary
The article’s supplementary files as
Acknowledgments
None.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the West China Hospital of Sichuan University Biomedical Research Ethics Committee (approval No. 2022-341), and individual consent for this retrospective analysis was waived.
Footnotes
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1741/rc
Funding: This study was supported by the Sichuan Science and Technology Program (No. 2023YFQ0095).
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1741/coif). All authors report the funding from the Sichuan Science and Technology Program (No. 2023YFQ0095). H.L. is an employee of Philips Healthcare. The authors have no other conflicts of interest to declare.
Data Sharing Statement
Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1741/dss
References
- 1.Lundberg IE, Fujimoto M, Vencovsky J, Aggarwal R, Holmqvist M, Christopher-Stine L, Mammen AL, Miller FW. Idiopathic inflammatory myopathies. Nat Rev Dis Primers 2021;7:87. 10.1038/s41572-021-00325-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Gono T, Kuwana M. Interstitial lung disease and myositis. Curr Opin Rheumatol 2024;36:466-72. 10.1097/BOR.0000000000001037 [DOI] [PubMed] [Google Scholar]
- 3.Lundberg IE, Tjärnlund A, Bottai M, Werth VP, Pilkington C, Visser M, et al. 2017 European League Against Rheumatism/American College of Rheumatology classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups. Ann Rheum Dis 2017;76:1955-64. 10.1136/annrheumdis-2017-211468 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Hum RM, Lilleker JB, Lamb JA, Oldroyd AGS, Wang G, Wedderburn LR, et al. Comparison of clinical features between patients with anti-synthetase syndrome and dermatomyositis: results from the MYONET registry. Rheumatology (Oxford) 2024;63:2093-100. 10.1093/rheumatology/kead481 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Mammen AL, Allenbach Y, Stenzel W, Benveniste O; ENMC 239th Workshop Study Group. 239th ENMC International Workshop: Classification of dermatomyositis, Amsterdam, the Netherlands, 14-16 December 2018. Neuromuscul Disord 2020;30:70-92. 10.1016/j.nmd.2019.10.005 [DOI] [PubMed] [Google Scholar]
- 6.Warrick JH, Bhalla M, Schabel SI, Silver RM. High resolution computed tomography in early scleroderma lung disease. J Rheumatol 1991;18:1520-8. [PubMed] [Google Scholar]
- 7.Topcu A, Mursaloglu HH, Yalcinkaya Y, Karakurt S, Yagiz B, Alaca Z, Demir M, Coskun BN, Dalkilic E, Inanc N. Evaluation of rheumatoid arthritis and connective tissue disease-related interstitial lung disease with pulmonary physiologic test, HRCT, and patient-based measures of dyspnea and functional disability. Clin Rheumatol 2021;40:3797-805. 10.1007/s10067-021-05693-9 [DOI] [PubMed] [Google Scholar]
- 8.Waseda Y, Johkoh T, Egashira R, Sumikawa H, Saeki K, Watanabe S, Matsunuma R, Takato H, Ichikawa Y, Hamaguchi Y, Shiraki A, Muro Y, Yasui M, Prosch H, Herold C, Kasahara K. Antisynthetase syndrome: Pulmonary computed tomography findings of adult patients with antibodies to aminoacyl-tRNA synthetases. Eur J Radiol 2016;85:1421-6. 10.1016/j.ejrad.2016.05.012 [DOI] [PubMed] [Google Scholar]
- 9.Felder FN, Walsh SLF. Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges. ERJ Open Res 2023;9:00145-2023. 10.1183/23120541.00145-2023 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Chung JH, Adegunsoye A, Cannon B, Vij R, Oldham JM, King C, Montner SM, Thirkateh P, Barnett S, Karwoski R, Bartholmai BJ, Strek M, Nathan SD. Differentiation of Idiopathic Pulmonary Fibrosis from Connective Tissue Disease-Related Interstitial Lung Disease Using Quantitative Imaging. J Clin Med 2021;10:2663. 10.3390/jcm10122663 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ufuk F, Demirci M, Altinisik G. Quantitative computed tomography assessment for systemic sclerosis-related interstitial lung disease: comparison of different methods. Eur Radiol 2020;30:4369-80. 10.1007/s00330-020-06772-2 [DOI] [PubMed] [Google Scholar]
- 12.Ito Y, Ichikawa Y, Murashima S, Sakuma H, Iwasawa T, Arinuma Y, Nakajima A. Novel deep-learning analysis for connective tissue disease-related interstitial lung disease extent assessment on CT: a preliminary cross-sectional study. Rheumatology (Oxford) 2025;64:SI14-20. 10.1093/rheumatology/keae491 [DOI] [PubMed] [Google Scholar]
- 13.Walsh SLF, Mackintosh JA, Calandriello L, Silva M, Sverzellati N, Larici AR, et al. Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-Resolution Computed Tomography. Am J Respir Crit Care Med 2022;206:883-91. 10.1164/rccm.202112-2684OC [DOI] [PubMed] [Google Scholar]
- 14.Luo YH, Mei XL, Liu QR, Jiang B, Zhang S, Zhang K, Wu X, Luo YM, Li YJ. Diagnosing cervical lymph node metastasis in oral squamous cell carcinoma based on third-generation dual-source, dual-energy computed tomography. Eur Radiol 2023;33:162-71. 10.1007/s00330-022-09033-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Chen L, Zhu M, Lu H, Yang T, Li W, Zhang Y, Xie Q, Li Z, Wan H, Luo F. Quantitative evaluation of disease severity in connective tissue disease-associated interstitial lung disease by dual-energy computed tomography. Respir Res 2022;23:47. 10.1186/s12931-022-01972-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Daoud B, Cazejust J, Tavolaro S, Durand S, Pommier R, Hamrouni A, Bornet G. Could Spectral CT Have a Potential Benefit in Coronavirus Disease (COVID-19)? AJR Am J Roentgenol 2021;216:349-54. 10.2214/AJR.20.23546 [DOI] [PubMed] [Google Scholar]
- 17.Connors GR, Christopher-Stine L, Oddis CV, Danoff SK. Interstitial lung disease associated with the idiopathic inflammatory myopathies: what progress has been made in the past 35 years? Chest 2010;138:1464-74. 10.1378/chest.10-0180 [DOI] [PubMed] [Google Scholar]
- 18.Bohan A, Peter JB. Polymyositis and dermatomyositis (first of two parts). N Engl J Med 1975;292:344-7. 10.1056/NEJM197502132920706 [DOI] [PubMed] [Google Scholar]
- 19.Sontheimer RD. Would a new name hasten the acceptance of amyopathic dermatomyositis (dermatomyositis siné myositis) as a distinctive subset within the idiopathic inflammatory dermatomyopathies spectrum of clinical illness? J Am Acad Dermatol 2002;46:626-36. 10.1067/mjd.2002.120621 [DOI] [PubMed] [Google Scholar]
- 20.Allenbach Y, Mammen AL, Benveniste O, Stenzel W, Immune-Mediated Necrotizing Myopathies Working Group . 224th ENMC International Workshop:: Clinico-sero-pathological classification of immune-mediated necrotizing myopathies Zandvoort, The Netherlands, 14-16 October 2016. Neuromuscul Disord 2018;28:87-99. 10.1016/j.nmd.2017.09.016 [DOI] [PubMed] [Google Scholar]
- 21.Jee AS, Sheehy R, Hopkins P, Corte TJ, Grainge C, Troy LK, et al. Diagnosis and management of connective tissue disease-associated interstitial lung disease in Australia and New Zealand: A position statement from the Thoracic Society of Australia and New Zealand. Respirology 2021;26:23-51. 10.1111/resp.13977 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Fischer A, Antoniou KM, Brown KK, Cadranel J, Corte TJ, du Bois RM, Lee JS, Leslie KO, Lynch DA, Matteson EL, Mosca M, Noth I, Richeldi L, Strek ME, Swigris JJ, Wells AU, West SG, Collard HR, Cottin V, ERS/ATS Task Force on Undifferentiated Forms of CTD-ILD . An official European Respiratory Society/American Thoracic Society research statement: interstitial pneumonia with autoimmune features. Eur Respir J 2015;46:976-87. 10.1183/13993003.00150-2015 [DOI] [PubMed] [Google Scholar]
- 23.. American Thoracic Society/European Respiratory Society International Multidisciplinary Consensus Classification of the Idiopathic Interstitial Pneumonias . This joint statement of the American Thoracic Society (ATS), and the European Respiratory Society (ERS) was adopted by the ATS board of directors, June 2001 and by the ERS Executive Committee, June 2001. Am J Respir Crit Care Med 2002;165:277-304. 10.1164/ajrccm.165.2.ats01 [DOI] [PubMed] [Google Scholar]
- 24.Ahn SS, Park YB, Lee SW. Clinical Features of Anti-Synthetase Syndrome Associated with Prognosis in Patients with Dermatomyositis and Polymyositis. J Clin Med 2022;11:2052. 10.3390/jcm11072052 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Patel P, Marinock JM, Ajmeri A, Brent LH. A Review of Antisynthetase Syndrome-Associated Interstitial Lung Disease. Int J Mol Sci 2024;25:4453. 10.3390/ijms25084453 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Hallowell RW, Danoff SK. Diagnosis and Management of Myositis-Associated Lung Disease. Chest 2023;163:1476-91. 10.1016/j.chest.2023.01.031 [DOI] [PubMed] [Google Scholar]
- 27.Flashner BM, VanderLaan PA, Nurhussien L, Rice MB, Hallowell RW. Pulmonary histopathology of interstitial lung disease associated with antisynthetase antibodies. Respir Med 2022;191:106697. 10.1016/j.rmed.2021.106697 [DOI] [PubMed] [Google Scholar]
- 28.Douglas WW, Tazelaar HD, Hartman TE, Hartman RP, Decker PA, Schroeder DR, Ryu JH. Polymyositis-dermatomyositis-associated interstitial lung disease. Am J Respir Crit Care Med 2001;164:1182-5. 10.1164/ajrccm.164.7.2103110 [DOI] [PubMed] [Google Scholar]
- 29.Cottin V, Thivolet-Béjui F, Reynaud-Gaubert M, Cadranel J, Delaval P, Ternamian PJ, Cordier JF; Groupe d’Etudes et de Recherche sur les Maladies “Orphelines” Pulmonaires. Interstitial lung disease in amyopathic dermatomyositis, dermatomyositis and polymyositis. Eur Respir J 2003;22:245-50. 10.1183/09031936.03.00026703 [DOI] [PubMed] [Google Scholar]
- 30.Kim TS, Lee KS, Chung MP, Han J, Park JS, Hwang JH, Kwon OJ, Rhee CH. Nonspecific interstitial pneumonia with fibrosis: high-resolution CT and pathologic findings. AJR Am J Roentgenol 1998;171:1645-50. 10.2214/ajr.171.6.9843306 [DOI] [PubMed] [Google Scholar]
- 31.Solomon J, Swigris JJ, Brown KK. Myositis-related interstitial lung disease and antisynthetase syndrome. J Bras Pneumol 2011;37:100-9. 10.1590/s1806-37132011000100015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Raghu G, Remy-Jardin M, Myers JL, Richeldi L, Ryerson CJ, Lederer DJ, et al. Diagnosis of Idiopathic Pulmonary Fibrosis. An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline. Am J Respir Crit Care Med 2018;198:e44-68. 10.1164/rccm.201807-1255ST [DOI] [PubMed] [Google Scholar]
- 33.Darras KE, McLaughlin PD, Kang H, Black B, Walshe T, Chang SD, Harris AC, Nicolaou S. Virtual monoenergetic reconstruction of contrast-enhanced dual energy CT at 70keV maximizes mural enhancement in acute small bowel obstruction. Eur J Radiol 2016;85:950-6. 10.1016/j.ejrad.2016.02.019 [DOI] [PubMed] [Google Scholar]
- 34.Vij R, Strek ME. Diagnosis and treatment of connective tissue disease-associated interstitial lung disease. Chest 2013;143:814-24. 10.1378/chest.12-0741 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Spagnolo P, Distler O, Ryerson CJ, Tzouvelekis A, Lee JS, Bonella F, Bouros D, Hoffmann-Vold AM, Crestani B, Matteson EL. Mechanisms of progressive fibrosis in connective tissue disease (CTD)-associated interstitial lung diseases (ILDs). Ann Rheum Dis 2021;80:143-50. 10.1136/annrheumdis-2020-217230 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Panagopoulos P, Goules A, Hoffmann-Vold AM, Matteson EL, Tzioufas A. Natural history and screening of interstitial lung disease in systemic autoimmune rheumatic disorders. Ther Adv Musculoskelet Dis 2021;13:1759720X211037519. [DOI] [PMC free article] [PubMed]
- 37.Basuita M, Fidler LM. Myositis Antibodies and Interstitial Lung Disease. J Appl Lab Med 2022;7:240-58. 10.1093/jalm/jfab108 [DOI] [PubMed] [Google Scholar]
- 38.Palmucci S, Di Mari A, Cancemi G, Pennisi I, Mauro LA, Sambataro G, Sambataro D, Galioto F, Fazio G, Ferlito A, Pino F, Basile A, Vancheri C. Clinical and Radiological Features of Interstitial Lung Diseases Associated with Polymyositis and Dermatomyositis. Medicina (Kaunas) 2022;58:1757. 10.3390/medicina58121757 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Jeong J, Ham S, Shim E, Kim BH, Kang WY, Kang CH, Ahn KS, Lee KC, Choi H. Electron density dual-energy CT can improve the detection of lumbar disc herniation with higher image quality than standard and virtual non-calcium images. Eur Radiol 2024;34:7334-46. 10.1007/s00330-024-10782-9 [DOI] [PubMed] [Google Scholar]
- 40.Sedaghat S, Langguth P, Larsen N, Campbell G, Both M, Jansen O. Diagnostic Accuracy of Dual-Layer Spectral CT Using Electron Density Images to Detect Post-Traumatic Prevertebral Hematoma of the Cervical Spine. Rofo 2021;193:1445-50. 10.1055/a-1529-7010 [DOI] [PubMed] [Google Scholar]
- 41.Nagano H, Takumi K, Nakajo M, Fukukura Y, Kumagae Y, Jinguji M, Tani A, Yoshiura T. Dual-Energy CT-Derived Electron Density for Diagnosing Metastatic Mediastinal Lymph Nodes in Non-Small Cell Lung Cancer: Comparison With Conventional CT and FDG PET/CT Findings. AJR Am J Roentgenol 2022;218:66-74. 10.2214/AJR.21.26208 [DOI] [PubMed] [Google Scholar]
- 42.Nelles C, Lennartz S. Spinal Hematoma Visualized with Dual-energy CT-derived Electron Density Overlay Maps. Radiology 2023;307:e222680. 10.1148/radiol.222680 [DOI] [PubMed] [Google Scholar]
- 43.Hünemohr N, Paganetti H, Greilich S, Jäkel O, Seco J. Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy. Med Phys 2014;41:061714. 10.1118/1.4875976 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Agostini A, Borgheresi A, Mari A, Floridi C, Bruno F, Carotti M, Schicchi N, Barile A, Maggi S, Giovagnoni A. Dual-energy CT: theoretical principles and clinical applications. Radiol Med 2019;124:1281-95. 10.1007/s11547-019-01107-8 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
The article’s supplementary files as
Data Availability Statement
Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1741/dss