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. 2021 Mar 1;16(3):e0247074. doi: 10.1371/journal.pone.0247074

Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer

Hong-Bing Luo 1,#, Yuan-Yuan Liu 1,#, Chun-hua Wang 1, Hao-Miao Qing 1, Min Wang 1, Xin Zhang 2, Xiao-Yu Chen 1, Guo-Hui Xu 1, Peng Zhou 1,*, Jing Ren 1,*
Editor: Quan Jiang3
PMCID: PMC7920570  PMID: 33647031

Abstract

Objective

To study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer.

Materials and methods

A total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models.

Results

The 106 radiomic features were reduced to 4 ALNM diagnosis–related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947–0.997]) in the training cohort and 0.979 (95% CI [0.952–1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05).

Conclusion

Radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.

Introduction

Breast cancer is the most common malignant cancer in women worldwide as well as in China, and has a high mortality rate [1, 2]. Lymphatic metastasis is the first step in the transition of breast cancer patients to metastatic state, and axillary lymphatic node metastasis is an important predictor of breast cancer recurrence [3]. Therefore, pre-treatment diagnosis of metastatic axillary lymph node (ALNM) is crucial for prognostic assessment and treatment decision-making [4–7]. Currently, lymphadenectomy and/or biopsy is the gold standard for differentiating ALNM from normal lymph nodes; however, these are invasive procedures associated with low repeatability and potential complications [5, 8]. Therefore, development of alternative noninvasive and repeatable methods for preoperative identification of ALNM is a key imperative.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for preoperative evaluation of axillary lymph node status in patients with breast cancer and shows superior performance than other techniques [9, 10]. The traditional DCE-MRI diagnostic criteria for ALNM are based on visual assessment of morphological features [11]. Radiomics provides an innovative quantitative method to predict ALNM in patients with breast cancer [7, 12–19].

Radiomics refers to the science of converting medical images to high-throughput and mineable quantitative features by data characterisation algorithms [20]. These features, termed radiomic features, have the potential to decode the invisible disease characteristics, which are useful for individualized treatment. It is different from the traditional medical images, which are subject to visual interpretation. Use of modern analytical software and artificial intelligence technology has helped unravel an increasing number of useful features obtained through radiomic method, especially in the field of cancer research [21].

Breast cancer is known to be a heterogeneous disease caused by variations in local micro-environment that are mainly governed by spatial and temporal changes in blood flow. Tumor heterogeneity may be represented by different contrast-enhancement patterns on DCE-MRI and amenable to quantitative assessment using radiomic methods based on PK-DCE-MRI [22–25]. Some recent studies have shown that radiomic features extracted from primary breast cancer mass may be used to predict metastases in the sentinel lymph node [15, 16] and axillary lymph nodes [18, 19]. However, to the best of our knowledge, the role of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status is still not studied.

The ALNM of breast cancer, which is similar to the tumor itself, may also exhibit heterogeneity; the heterogeneous characteristics on PK-DCE-MRI can be decoded by radiomic method. Our hypothesis is that the radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes can help diagnose ALNM in patients with breast cancer.

Materials and methods

Subjects

The Medical Ethics Committee of the Sichuan Cancer Hospital & Institute approved this study. The requirement for informed consent of subjects was waived off. We retrospectively reviewed our database to select consecutive women with proven breast cancer by surgical pathology and who underwent DCE-MRI examination before surgery between August 2015 and June 2019. The exclusion criteria were: patients who had received neo-adjuvant chemotherapy; patients for whom quantitative parameters could not be acquired due to data-processing errors.

To ensure that axillary lymph nodes included in radiomic analyses were pathologically metastatic nodes, cases recruited to the ALNM group were required to qualify the following two conditions. First, at least 3 metastatic axillary lymph nodes were confirmed by pathology after axillary lymphadenectomy. Second, there was at least one highly suspicious axillary lymph node on DCE-MRI images in the ipsilateral axilla, which was visible to radiologists. Finally, two radiologists with 8 and 6 years of experience in the interpretation of breast MRI, respectively, reviewed the surgical pathology reports and MRI images together, and selected only one largest and highly suspicious axillary lymph node for each recruited patient for radiomic analysis.

To ensure that axillary lymph nodes included in radiomic analyses were pathologically negative for metastasis, cases recruited to the negative axillary lymph nodes group for control (ALNC) were required to qualify the following conditions. First, all axillary lymph nodes of the patients with negative sentinel lymph node biopsy were considered negative [13]. Second, we only chose the largest visible ipsilateral axillary lymph node of these patients for radiomic analysis.

MRI acquisition

The MRI acquisition (as briefly described below) in this study were not specific to the current research and have been described extensively in our previous study [26]. All DCE-MRI examinations were performed using a 3.0-T Skyra device (Siemens Healthcare, Erlangen, Germany) with a dedicated breast coil (16-channel breast array; Siemens Healthcare, Erlangen, Germany). With the patient in a prone position, Axial T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and DCE-MRI sequences were obtained. The DCE-MRI included T1 mapping and 26 consecutive phases fast dynamic MR acquisition, using the CAIPIRINHA-Dixon-TWIST-VIBE sequence with the temporal resolution 11.8 s/phase and TA 5 min 12 s. Gadodiamide (0.1 mmol/kg; Omniscan, GE Healthcare, Milwaukee, WI) was intravenously administered using a power injector (rate, 2.5 mL/s) at the end of T1 mapping. Then, a 20-mL saline flush was injected (rate, 2.5 mL/s).

Post-processing of MRI images and radiomic analysis

Raw DCE-MRI data were imported into a dedicated post-processing software (Omni-Kinetics, GE Healthcare, Milwaukee, WI). The enhancement kinetics were analyzed using the reference region model [27]. With the reference region set to pectoralis major muscle, voxel-wise perfusion maps were automatically generated.

A schematic illustration of the radiomic analysis process is shown in Fig 1; the process consisted of 3D whole lymph node segmentation, features extraction, features selection, model building, and evaluation.

Fig 1. General radiomic workflow in the study.

Fig 1

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Whole lymph node segmentation

For extracting the radiomic features of each selected lymph node, the same two radiologists manually drew the regions of interest (ROIs) that included the whole lymph node in the early stage of postcontrast image of DCE-MRI. The radiologist with 8 years of experience performed all definitive measurements. Subsequently, all ROIs were merged into one 3D volume of interest (VOI).

Features extraction

A total of 106 radiomic features were extracted and automatically outputted by the software from the VOI, including 1 total voxel number of VOI, 30 standard pharmacokinetic parameters with their corresponding histogram features: (1) Ktrans (min−1), the volume-transfer constant (wash-in rate), which reflects vascular permeability and perfusion; (2) Kep (min−1), the washout-rate constant, which reflects contrast-agent reflux back to the vessels; (3) Vp, the plasma fraction. The histogram features, i.e., the maximum, minimum, median, mean, Std, 10%, 25%, 50%, 75%, and 90% values, of each quantitative parameter, and 75 texture features of T1-weighted images of dynamic contrast enhanced-magnetic resonance imaging (T1 DCE-MRI) (S1 Table), consisting of 12 first-order statistical features, 15 histogram features, 13 Gray-level co-occurrence matrix (GLCM) features, 10 Haralick features, 16 Run length matrix (RLM) features and 9 Morphology metrics features.

Features selection and radiomic model building

First, the training and validation cases were separated at a ratio of 7 to 3. We employed the least absolute shrinkage and selection operator (LASSO) technique and leave-one-out cross-validation (LOOCV) method to select and rank the optimal radiomic features from the primary date set in the training cohort. Tunning minimum criteria selection in the LASSO model used 10-fold cross validation via minimum criteria in the study. Then we used logistic regression to build a pharmacokinetic model (PK-model) based on pharmacokinetic parameters with their corresponding histogram features and a radiomic model based on radiomic features. The Radiomics score (Rad-score) was calculated for each patient according the coefficients of the radiomic model, which was defined as a radiomic signature. The performance was then validated in the validation cohort.

Conventional model building

For conventional model building, some conventional image features of every selected node (including the long and short axis length, short-long axis ratio, fatty hilum status on T2WI sequence, signal intensity on diffusion weighted imaging (DWI), and heterogenous enhancement feature on DCE-MRI sequences) were assessed by the same two radiologists. We used univariate analyses to compare these features between ALNM and ALNC (Table 1). We used logistic regression to build a conventional model based on these candidate features with p < 0.05 in the univariate analyses. The performance was then validated in the validation cohort.

Table 1. Difference of conventional image features between ALNM and ALNC.
Features ALNC (N = 89) ALNM (N = 87) p value
long axis length (mm, mean±SD) 8.73 ±3.15 17.92 ±10.43 0.000
short axis length (mm, mean±SD) 5.23 ±1.98 12.30 ±8.34 0.000
short-long axis ratio 1.78 ±0.61 1.52 ±0.36 0.001
fatty hilum status no 39 71 0.000
yes 50 16
signal intensity on DWI low 18 6 0.010
high 71 81
heterogenous enhancement feature no 76 19 0.000
yes 13 68
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Note: ALNC, Axillary lymph nodes for control; ALNM, Metastatic axillary lymph nodes; DWI, Diffusion weighted imaging

Combined model building

Integrating the Rad-score in the radiomic model with the conventional image features, the combined model was built using the multivariable logistic regression method. The performance of the combined model was validated in the validation cohort.

Comparison of models

Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the above models in diagnosing ALNM. The Delong test was used to compare the diagnostic performance of the conventional model, radiomic model, and the combined model according to the area under the curve (AUC) values.

Establishment of nomogram

To provide an individualized tool for ALNM diagnosis, a nomogram based on the combined model was plotted. The calibration of the nomogram was assessed using calibration curve, accompanied with the Hosmer–Lemeshow test. Harrell’s C-index was measured to quantify the discriminative ability of the nomogram.

Clinical use

Decision curve analysis was conducted to determine the clinical usefulness of the nomogram by quantifying the net benefits at different threshold probabilities in the validation dataset.

Statistical analysis

R statistical software (version 3.6.1) was used for statistical analyses. P values < 0.05 were considered indicative of statistical significance.

Results

Patients and axillary nodes in the study

Finally, a total of 176 axillary lymph nodes (87 ALNM breast cancer patients [mean age, 50.7 years; range, 28–78] and 89 ALNC breast cancer patients [mean age, 50.0 years; range, 30–76] were selected for radiomic analyses.

Radiomic features selection and radiomic signature building

The 106 radiomic features of each selected axillary lymph node were reduced to 4 ALNM diagnosis–related features by LASSO (Fig 2). They were all texture features of T1 DCE-MRI, including Haralick Correlation, Difference Variance, DifferenceEntropy and LongRunEmphasis. A radiomic signature containing these features was constructed. The diagnostic performance of the radiomic signature was excellent. The optimal cutoff value of 0.38 was associated with an AUC of 0.971 (95% CI [0.947–0.995]) in the training cohort and 0.966 (95% CI [0.925–1]) in the validation cohort. The accuracy in the training and validation cohorts was 91.8% and 90.7%, respectively. The sensitivity of the radiomic signature in the training and validation cohorts was 90% and 92.6%, respectively; the specificity was good (93.5% in the training cohort and 88.9% in the validation cohort.

Fig 2. LASSO regression for radiomic feature selection.

Fig 2

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(A) Selection of the parameter (λ) in the LASSO model by 10-fold cross-validation based on minimum criteria. The y-axis indicates binomial deviances. The lower x-axis indicates the log(λ). Red dots indicate the average deviance values for each model with a given λ, and vertical bars through the red dots show the upper and lower values of the deviances. The dotted vertical lines define the optimal values of λ, where the model provides the best fit to the data. (B) A coefficient profile plot was produced against the log (l) sequence. Vertical line was drawn at the value selected using 10-fold cross-validation, where optimal λ resulted in non-zero coefficients.

Building of diagnostic models

The conventional model based on conventional image features, PK-model based on pharmacokinetic parameters with their corresponding histogram features, radiomic model based on all radiomic features, and the combined model integrating radiomic features with conventional image features were built. The diagnostic performance of these models is shown in Table 2. The combined model showed the best diagnostic performance; the optimal cutoff value of 0.671 was associated with an AUC of 0.972 (95% CI [0.947–0.997]) in the training cohort and 0.979 (95% CI [0.952–1]) in the validation cohort. The accuracy in the training and validation cohorts was 92.6% and 92.6%, respectively. The sensitivity of the radiomic signature in the training and validation cohorts was 91.7% and 96.3%, respectively; the specificity was good (93.5% in the training cohort and 88.9% in the validation cohort).

Table 2. Diagnostic performance of all models for detection of metastatic axillary lymph node.

  Training Validation
  AUC (95% confidence interval) ACC Specificity Sensitivity AUC (95% confidence interval) ACC Specificity Sensitivity
Radiomic model 0.971 (0.947–0.995) 0.918 0.935 0.9 0.966 (0.925–1) 0.907 0.889 0.926
Conventional model 0.929 (0.881–0.977) 0.877 0.871 0.883 0.988 (0.968–1) 0.926 0.889 0.963
PK model 0.945 (0.908–0.981) 0.877 0.855 0.9 0.942 (0.88–1) 0.87 0.815 0.926
Combined model 0.972 (0.947–0.997) 0.926 0.935 0.917 0.979 (0.952–1) 0.926 0.889 0.963
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Note: AUC, area under the curve; ACC, accuracy; PK-model, pharmacokinetic model.

Comparison of models and development of nomogram

The diagnostic performance of combined model and radiomic model was better than that of the conventional model (Delong test, p<0.05). However, the diagnostic performance of the combined model was not better than that of the radiomic model (p>0.05). The ROC curves of the four models are shown in Fig 3. We developed a nomogram based on the radiomic signature and conventional image features (Fig 4). The calibration curve for the nomogram was tested by Hosmer-Lemeshow test, which yielded a non-significant result (χ2 = 3, p>0.05) showing good calibration (Fig 5).

Fig 3.

Fig 3

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Receiver operating characteristic (ROC) curves of conventional model, pharmacokinetic model (PK-model), radiomic model, and the combined model for diagnosis of metastatic axillary lymph nodes in the training (A) and validation (B) group at a ratio of 7 to 3.

Fig 4. Nomogram for diagnosis of metastatic axillary nodes.

Fig 4

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The values for each variable correspond to a point at the top of the graph, and the sum of the points for all the variables corresponds to a total point; a line drawn from the total points to the bottom line shows the probability of axillary lymph nodes metastasis. Heterogenous enhancement feature (HEF) is a conventional image feature.

Fig 5.

Fig 5

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Calibration curve of the nomogram for the training (red) and validation (blue) cohorts at a ratio of 7 to 3. The X-axis represents the probability that nomogram diagnosed the axillary lymph nodes metastasis, while Y-axis represents the actual rate of axillary lymph nodes metastasis.

Clinical use

The decision curve analyses for the nomogram and conventional model are presented in Fig 6. The results showed that the net benefit of using the radiomics nomogram for diagnosis of ALNM was greater than that of the conventional model.

Fig 6. Decision curve analysis of the nomogram of combined model and conventional model.

Fig 6

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The y-axis measures the net benefit. The red line represents the nomogram of the combined model. The blue line represents the conventional model. The thin grey line represents the assumption that all patients have axillary lymph nodes metastases. The black line represents the assumption that none of the patients have axillary lymph nodes metastases.

Discussion

The results of this study, though preliminary in scope, reveal that the radiomic features extracted from preoperative PK-DCE-MRI of axillary lymph nodes can be used to diagnose ALNM in patients with breast cancer. In addition, in this study, the texture features depicting heterogeneity were found more helpful than pharmacokinetic quantitative and their histogram features for diagnosis of metastatic axillary nodes.

Use of radiomics analysis for diagnosis and prognostic assessment in the context of breast cancer is a contemporary research hotspot. Most of the studies have focused on discriminating malignant from benign breast tumors [28–30] or on predicting the chemotherapeutic response [31, 32]. Some recent studies have investigated the feasibility of differentiating ALNM by radiomic features, most of these studies were focused on the predictive value of radiomic features extracted from primary breast tumors [17–19]. The radiomic features of breast tumors may be used to predict the ALNM; however, these can not be directly used to diagnose ALNM. Therefore, we designed this study to assess the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastasis status. Our results show that the diagnostic performance of radiomic model for detecting ALNM was better than that of the conventional model. Although not better than radiomic model, the combined model showed the best diagnostic performance among the four models. The accuracy was improved from 0.877 of conventional model to 0.926 of the combined model. These results implied that the radiomic features of axillary lymph node itself are promising bio-markers for helping diagnosing its metastasis status. Besides, all the 4 top-rank radiomic features for discrimination between positive and negative axillary nodes by LASSO regression were texture features of T1 DCE-MRI. This suggests that the texture features have a more prominent discriminative performance than the pharmacokinetic parameters and their corresponding histogram features. These findings are consistent with those a previous study by Schacht et al in which kinetic features showed poorer performance in distinguishing between positive and negative lymph nodes [13]; this was attributable to the fact that some normal axillary lymph nodes may also exhibit patterns of rapid uptake and washout kinetics. The results implied that heterogeneity may be the most important characteristic of ALNM in breast cancer, which is difficult to interpret on visual examination of conventional images; however, it can be depicted by radiomic methods through whole-tumor and voxel-wise quantitative analysis based on PK-DCE-MRI. This is concordant with our hypothesis that ALNM are spatially more heterogeneous than the negative ones. The heterogeneous characteristics of axillary lymph nodes could be comprehensively decoded through radiomic methods on the basis of PK-DCE-MRI in vivo; this could be used as an effective supplement to traditional medical images for diagnosis of ALNM of breast cancer in future.

This study had certain limitations. First, the effect of selection bias on our results cannot be ruled out, as it is difficult to accurately match the selected axillary lymph nodes for radiomic analyses visible on DCE-MRI with the metastatic nodes proven by resection and biopsy. To minimize this bias, we only recruited patients for whom at least 3 axillary lymph nodes metastases were confirmed by pathology after axillary lymphadenectomy; subsequently, we selected only one of the most likely axillary lymph nodes in the ipsilateral axilla as ALNM. Second, the semi-automatic features extraction approach may cause some inter-observer variability. With advances in software and algorithms, the detection and segmentation method should be combined with computer vision algorithms for automated specification of the VOI beyond human perception. Such approaches may theoretically be easily implemented in clinical workflow. Finally, although our results were based on high-field strength MRI and the CDT-VIBE sequence protocol, which has high temporal and spatial resolution essential for PK-DCE-MRI, this was only a single-center study. Thus, our findings need to be verified in a multi-center study with different imaging equipments and protocols.

Conclusions

Based on the preliminary results of our study, it may be inferred that the ALNM of breast cancer are more heterogeneous than the negative nodes. Radiomic methods can be used to decode the heterogeneity of axillary lymph nodes. Our findings provide impetus for further radiomic research to develop a non-invasive tool for diagnosis of metastatic lymph node and individualized treatment.

Supporting information

S1 Table. Texture features of T1 DCE-MRI in the study.

(DOC)

Click here for additional data file. (60.5KB, doc)

Abbreviations

ALNM

Metastatic axillary lymph nodes

ALNC

Axillary lymph nodes for control

PK-DCE-MRI

Pharmacokinetic modeling dynamic contrast-enhanced magnetic resonance imaging

LASSO

Least absolute shrinkage and selection operator

ROC

Receiver operating characteristic

AUC

Area under the curve

FOV

Field of view

TA

Total acquisition time

CAIPIRINHA-Dixon-TWIST-VIBE

Controlled aliasing in parallel imaging results in higher acceleration-Dixon-Time resolved imaging with interleaved stochastic trajectories-Volume interpolated body examination

ROI

Region of interest

VOI

Volume of interest

DWI

Diffusion weighted imaging

Data Availability

Our Medical Ethics Committee imposed ethical and legal restrictions on sharing a de-identified data set, because the data contain potentially identifying and sensitive patient information. Upon request, the request for data should be sent to Our Medical Ethics Committee (Email:scchec@163.com) or to the corresponding author (Jing Ren, Email: 13880611648@163.com).

Funding Statement

This work was supported by Project of Sichuan Medical Association (www.sma.org.cn) Grant# S17067 (Y.Y. L),Sichuan Science and Technology Program (http://kjt.sc.gov.cn/) Grant# 2021YFS0075 (J. R), and Sichuan Science and Technology Program(http://kjt.sc.gov.cn/) Grant# 2021YFS0225 (P. Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. XZ received support in the form of a salary from GE Healthcare. The specific roles of these authors are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Quan Jiang

17 Dec 2020

PONE-D-20-30485

Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer

PLOS ONE

Dear Dr. Ren,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

  1. Performing a careful comparison with the conventional image reading of the nodes to demonstrate how the radiomics model may help in the cases.

  2. Questions about tables.

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We look forward to receiving your revised manuscript.

Kind regards,

Quan Jiang, Ph,D.

Academic Editor

PLOS ONE

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a well written study to report the differentiation of 87 malignant and 89 benign axially lymph nodes seen in breast MRI, by using 4 different models: 1) conventional model based on image features; 2) PK-model based on pharmacokinetic parameters with their corresponding histogram features; 3) radiomic model based on all radiomic features, and 4) the combined model integrating radiomic features with conventional image features. The methods were pretty standard and well described. The results showed that the radiomics model based on texture had the best performance, and that was attributed to the heterogeneity in malignant compared to benign nodes. Although the results could achieve very high accuracy, it is still difficult to implement the complicate models in a clinical setting. Performing a careful comparison with the conventional image reading of the nodes to demonstrate how the radiomics model may help in the cases that the reading was wrong will greatly improve the value of this work. Some comments below:

1. In the manuscript that I downloaded for review, Tables 1 and 2 were not included, thus it was difficult to compare the performance (sensitivity vs. specificity) between the radiomics and conventional models, which is the most interesting part in my opinion.

2. There are well established nodal features commonly used to predict malignant vs. benign nodes, as described in this work, including the long and short axis length, short-long axis ratio, fatty hilum status, signal intensity on diffusion weighted imaging (DWI), and heterogenous enhancement feature. Please add a table to compare these features between the malignant and benign groups.

3. Please give the number of true positive, true negative, false positive, and false negative cases predicted by each of the 4 models. Then compare the results of reading and radiomics to see which cases were correctly predicted by radiomics but wrongly diagnosed by reading. Show these case examples using figures. These results will be very interesting.

4. The presence of fatty hilum is considered as an important benign feature, but it is not clear how this feature can be well appreciated on the DCE imaging sequence used in this study, that heavily focuses on a high temporal resolution (thus may sacrifice the spatial resolution). What other MR imaging sequences were considered in the reading? Please discuss.

5. After adding results in issues 2-4, please also add discussion to elaborate the value of the radiomics that may be complementary to reading. In fact, it seems all 4 models have very high accuracy; therefore, the actual value of the very complicated models may be limited. Please emphasize on the discordant cases between reading and radiomics models.

6. Which DCE frame was used for drawing the nodal ROI? Please see issue 4) and show case example to illustrate that fatty hilum was included in the analysis.

7. How were the training and validation cases separated in the analysis, also in Figure 3 and Figure 5?

Reviewer #2: I would like to thank the authors for a well written scientifically sound manuscript addressing an important topic which is being more and more explored in the current era. Hopefully more studies such as this one would help lead the community to perform bigger controlled trials and make the AI softwares vastly available in the clinic in the near future.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Mar 1;16(3):e0247074. doi: 10.1371/journal.pone.0247074.r002

Author response to Decision Letter 0

22 Jan 2021

Response to the concerns raised by the reviewers (The First Part) and the academic editor (The Second Part) (quoted in italics and numbered for ease of reference). All changes have been indicated red in the marked-up copy of revised manuscript labeled 'Revised Manuscript with Track Changes copy'.

The First Part (Response to the comments raised by reviewers):

1.Response to Reviewer #1

Response 1: We would like to thank the reviewer for the kind and helpful advice. This was a preliminary study in which we focused on the feasibility of use of radiomic features of axially lymph nodes in preoperative diagnosis of their metastatic status in breast cancer. In the study, we have used the Delong test to compare the overall diagnostic performance of different models. The result showed that the overall diagnostic performance of the combined model and the radiomic model were significantly better than that of the pharmacokinetic model and the conventional model (P<0.05). In clinical setting, it is really hard to demonstrate how the radiomics model may help in every single case in which the reading was wrong by conventional image feature. Following your helpful advice and based on this preliminary result, we will try to design another research with larger sample in order to achieve results that are more applicable in clinical settings.

Response 1: We apologize for not showing the Tables 1 and 2 in the original manuscript due to some technical problems. We have corrected the omission in the revised manuscript. Table 1 in original manuscript shows the texture features in the study. Table 2 shows the diagnostic performance (including the sensitivity and specificity) of four models for detection of metastatic axillary lymph nodes.

Response 1: As suggested, we have added a new table (Table 1 in the revised manuscript) to compare the conventional image features between the malignant and benign groups.

Response 1: Thank you for your insightful comment. In our research, the models constructed by training dataset and their overall performance were tested by the validation dataset. Thus, we cannot show which cases were correctly predicted by radiomics but wrongly diagnosed by conventional reading due to the very nature of radiomic analysis. It is hard to show which cases were correctly predicted by radiomics but wrongly diagnosed by conventional reading.

Response 1: The presence of fatty hilum was evaluated based on T2WI sequences. The MRI acquisition which has been described extensively in our previous study[1] were not specific to the current research. Therefore, we presented only a brief description of the MRI acquisition. We apologize for not clearly stating this before. We have made this clearer in the revised manuscript.

1. Luo HB, Du MY, Liu YY, Wang M, Qing HM, Wen ZP, Xu GH, Zhou P, Ren J: Differentiation between Luminal A and B Molecular Subtypes of Breast Cancer Using Pharmacokinetic Quantitative Parameters with Histogram and Texture Features on Preoperative Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Acad Radiol 2020, 27(3):e35-e44.

Response 1: As suggested, we have added the results and discussion in the revised manuscript to address this issue.

Response 1: The early stage of post-contrast image of DCE-MRI was used for drawing the whole nodal ROI. We are so sorry for not clearly stating this before, and we have made this clearer in the revised manuscript. Besides, the fatty hilum was evaluated on T2WI sequences (illustrated in our response to issue 4).

Response 1: The training and validation cases were separated at a ratio of 7 to 3 in the analysis. We have added this information in the revised manuscript and in figure legends 3 and 5.

2.Response to Reviewer #2

Response 2: Thank you very much for your kind comments. We completely agree that it is important to judge the metastatic status of lymph nodes, especially in patients with breast cancer. In clinical settings, it is really difficult to fulfill this task by conventional image features. Radiomics is a novel and prospective way to assess the tumor heterogeneity which may be the unique characteristic of malignant tumors. This was our motivation to perform this study. However, this was a preliminary research and our results need to be verified in larger multi-center studies. The results demonstrate the feasibility of utilization of radiomic features of axillary lymph nodes for diagnosing their metastatic status in patients with breast cancer.

The Second Part (Response to the points raised by the academic editor):

Response 3: We have revised our manuscript in accordance with the additional requirements of PLOS ONE.

Response 3: As suggested, the manuscript and the revised manuscript have been edited and proofread by the Medjaden Bioscience Limited, a professional medical editing company. A copy of our revised manuscript showing the changes using track changes has been uploaded as a *supporting information* file.

Response 3: As suggested, we have addressed the prompts about the data request in our revised cover letter.

Response 3: We have amended the statement in our revised cover letter: “The authors received no specific funding for this work.”

Response 3: The commercial affiliation did not play any role in our study. We have decided to remove the only author employed by the commercial company (GE Healthcare) from the author list. Therefore, there are no changes in our “Funding Statement” and “Competing Interests Statement” in the revised manuscript.

Response 3: As suggested, we have included the tables as part of the main manuscript in the revised manuscript and have removed the individual table files.

Attachment

Submitted filename: 2 Response to Reviewers.docx

Click here for additional data file. (31.5KB, docx)

Decision Letter 1

Quan Jiang

1 Feb 2021

Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer

PONE-D-20-30485R1

Dear Dr. Ren,

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

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Quan Jiang, Ph,D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: One major suggestion in the previous review is to give per-patient based diagnostic results, using both radiomics and conventional models, but the authors replied that it is not possible. This is a bit puzzling, because the developed model can give a malignancy probability, which can be used to give per-lesion diagnosis. Nonetheless, I also agree that this type of analysis can be best performed in a totally independent testing dataset. The quality of the revision has been further improved for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Acceptance letter

Quan Jiang

15 Feb 2021

PONE-D-20-30485R1

Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer

Dear Dr. Ren:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Quan Jiang

Academic Editor

PLOS ONE

Associated Data

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

    Supplementary Materials

    S1 Table. Texture features of T1 DCE-MRI in the study.

    (DOC)

    Click here for additional data file. (60.5KB, doc)
    Attachment

    Submitted filename: 2 Response to Reviewers.docx

    Click here for additional data file. (31.5KB, docx)

    Data Availability Statement

    Our Medical Ethics Committee imposed ethical and legal restrictions on sharing a de-identified data set, because the data contain potentially identifying and sensitive patient information. Upon request, the request for data should be sent to Our Medical Ethics Committee (Email:scchec@163.com) or to the corresponding author (Jing Ren, Email: 13880611648@163.com).

    Articles from PLoS ONE are provided here courtesy of PLOS

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