Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study

Background Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC). Methods This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF categories such as shape based, Gray level dependence matrix (GLDM), Gray level co-occurrence matrix (GLCM), First order, Gray level run length matrix (GLRLM), Gray level size zone matrix (GLSZM), NGTDM (Neighbouring gray tone difference matrix) were extracted from the DCE-MRI sequence using a 3D slicer. The maximum relevance and minimum redundancy (mRMR) was applied using Google Colab for identifying the top fifteen relevant radiomic features. The Mann-Whitney U test was performed to identify significant RF for differentiating IDC and ILC. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC. Results Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC. Conclusions MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.


Introduction
Breast cancer (BC) is the most frequently diagnosed cancer among women worldwide.With an expected 2.3 million new cases, or 11.7% of all cancer cases worldwide in 2020, lung cancer has surpassed lung cancer as the most common cause of cancer incidence. 1According to epidemiological studies, by 2030, there will likely be roughly 2 million BC patients worldwide, according to epidemiological studies. 2 Early and precise identification and characterization of cancers are crucial because of their incidence and therapeutic significance.
Mammography and Ultrasonography are frequently used for breast lesion detection, screening and diagnostic purposes. 3,4Breast Magnetic Resonance Imaging (MRI) is performed regularly to better detect primary and recurrent tumors, characterize them, and assess the patient's response to therapy.Dynamic contrast-enhanced (DCE) MRI is an important component of the MRI-Breast protocol, which involves serial capture of strong T1 weighted images during intravenous administration of contrast.6][7][8] Accurately diagnosing invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) presents significant challenges, primarily due to overlapping clinical, radiological, and histological features.Diagnostic doubts are exacerbated by mammographic ambiguities, equivocal biopsy results, and challenges in differentiating between the two subtypes.The distinctive marker for ILC, E-cadherin, can be stained with immunohistochemically, but the results can be ambiguous.Additionally, the genetic and molecular variability within each subtype makes classification even more difficult.Furthermore, the whole extent of the tumor may not always be captured by the diagnostic techniques used today, such as mammography, ultrasound, and biopsy, which could result in sample errors and incomplete assessments.[11][12] Radiomics is a popular area of study in the processing and analysis of medical images.Radiomics provides more information than the visual and qualitative patterns that radiologists can see with their unaided eyes by extracting a large number of quantitative imaging features from medical images.On routine imaging examinations performed in cancer patients, radiomic feature (RF) characteristics can non-invasively evaluate intratumoral variability. 13,14The use of RF in patients with BC is a novel and developing area of translational research.RF in BC has been extensively employed in research settings with the hope that it may eventually improve diagnosis and characterization. 15,16ere are significant methodological variations in research involving RF and artificial intelligence (AI) techniques, such as machine learning (ML) based on radiomics, and there is potential for methodological advancement and standardization to improve study quality in BC.There is a lack of reproducibility and validation in radiomic studies. 17Our literature review revealed a few studies that used RF from the DCE sequence of breast MRI for differentiating malignant lesions such as invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC).Hence, our study investigated whether RF obtained from DCE-MRI would aid in the differentiation of IDC and ILC.

Methods
First, this was a retrospective study.The study was commenced upon approval from the Institutional Ethical Committee of Kasturba Medical College and Hospital, Manipal, India on 6 th August 2022 (IEC 202/2022).Informed consent was waived since the data was collected from a publicly available database.

REVISED Amendments from Version 1
There are no changes in title, authors list, figures, and data.In abstract, the reviewer asked to explain the radiomic features analyzed and statistical methods used, the same is added and concise to <300 words.In Introduction, the reviewer suggested explaining the challenges in accurately diagnosing Invasive ductal carcinoma (IDC) and Invasive lobular carcinoma (ILC) and discuss the limitations of current diagnostic methods, the same is added.In methods, the reviewer suggested including rationale for the sample size and radiomic feature selection criteria and asked to provide detail on statistical analysis methods used for ROC curve analysis, the same is added.In results, the reviewer suggested including specific quantitative results such as effect size, confidence intervals and p-values, the same is provided in table 4 and 5.In discussion, the reviewer suggested addressing the limitations of the study and discussing the potential biases, reproducibility issues and future research directions in more detail.Hence, the same is added in discussion.In the conclusion, the reviewer suggested adding the clinical significance of the results and suggesting potential complications for breast cancer diagnosis and treatment and providing a brief summary of the study's limitations and future directions in the conclusion section, hence the same is added in the conclusion.
Any further responses from the reviewers can be found at the end of the article

Eligibility criteria
Patients with confirmed histopathology diagnosis of IDC and ILC were included.The cases with artifacts on MRI images were excluded.

MRI scanning
Patients with confirmed histopathological reports of IDC (30) and ILC (28) from the Duke breast cancer MRI data set of the cancer imaging archive (TCIA) were included in this study 18,19 (underlying data). 20Sample size for the study was calculated using the formula of sensitivity and specificity for diagnostic test accuracy based on sensitivity and specificity and area under the curve.
The MRI breast dataset (IDC and ILC) used for this study is publicly available online at https://wiki.cancerimagingarchive.net/;https://doi.org/10.7937/TCIA.e3sv-re93).The Duke breast cancer dataset is composed of a retrospectively collected cohort of 922 biopsy-confirmed invasive breast cancer patients from a single institution (Duke Hospital, Durham, North Carolina, USA) with preoperative MRI from January 1, 2000 to March 23, 2014.The images were acquired using 1.5 Tesla GE (Signa Excite, Signa HDxt) and Siemens (Avanto) MRI scanners.The mean age (years) of the patients with IDC and ILC was 48 AE 11.15 and 59 AE 11 years, respectively.Demographic characteristics are shown in Table 1.Axial T1 dynamic post-contrast (DCE) sequence (gadolinium-based contrast of 15-20 ml) was performed using the image acquisition parameters listed in Table 2.

Image segmentation and RF extraction
The DICOM MRI images of the Axial T1 DCE were uploaded into 3D slicer (version 4.10.2) and the regions of tumor were manually (slice by-slice) delineated by radiologist who had experience more than 10 years (Figure 1).The RF was extracted from the axial T1 dynamic contrast-enhanced sequence.ROIs were defined across the entire tumor using DCE-MRI images with the strongest enhancement phase.The observer specifically chose phase (3.92 AE 1.22-on average), to segment the analyzed lesions, out of the six or seven phases offered by the Axial T1 Dynamic contrast enhanced sequences, across different patients, where the breast mass was more visible than in the backdrop.The radiologist was blinded to histopathological reports.

Feature selection
High-dimensional radiomic feature sets can lead to overfitting and increased computational complexity.Dimensionality reduction technique, such as maximum relevance and minimum redundancy (mRMR) is employed to retain the most informative features while reducing the risk of model overfitting.To determine which RF was the most pertinent and least redundant, the maximum relevance and minimum redundancy (mRMR) approach was used. 21Fifteen RF features were selected for the subsequent analysis (Table 3).

Statistical analysis
Statistical analyses were performed using Statistical Package for Social Sciences (SPSS-20.0)software.The maximum relevance and minimum redundancy (mRMR) approach was applied using Google Colab.The Mann-Whitney U-test was performed to identify significant features for differentiating IDC and ILC on Axial T1 dynamic contrast.Receiver Operating Curve (ROC) analysis was performed to determine the accuracy of RF in differentiating between IDC and ILC.Receiver Operating Characteristic (ROC) curve analysis, statistical methods were employed to evaluate the diagnostic performance/accuracy of RF in differentiating between IDC and ILC.Area Under the Curve (AUC), quantifies the        2).

Accuracy measures of radiomic features
The accuracy measures of RF for differentiating between IDC and ILC are listed in  3).

Discussion
In the current study, we explored the usefulness of DCE-based RF compared to MRI for differentiating malignant lesions of the breast, such as IDC and ILC.Breast MRI is superior to mammography and ultrasonography for early detection of BC.IDC and ILC are the most common subtypes of malignant cancer.Differentiation of ILC from IDC is quite difficult, as there are very few differences between them.The morphological and dynamic contrast kinetic characteristics of IDC and ILC did not differ considerably from each other.The correct identification of ductal and lobular carcinoma helps in the improved management and overall survival analysis of the patient. 22,235][26] However, few studies have been conducted to differentiate invasive carcinomas of the breast, such as IDC and ILC, using RF from dynamic contrast sequences.The integration of radiomic models in normal radiological practice will be extremely beneficial for non-invasive diagnosis and clinical management of invasive BC in the future.
Our study found ten RF useful in differentiating IDC and ILC.We noticed that categories such as GLDM, GLCM, first order, and GLRLM showed significant differences in differentiating ductal and lobular carcinoma of the breast.Of these categories, GLRLM (Gray level variance AUC 0.998) and GLCM difference average) and interquartile range features were the best predictors for differentiation between IDC and ILC.GLCM features consider how pixels are arranged in space.These features describe the texture of a picture by calculating the frequency of pixel pairings with distinct values and a specific spatial relationship.GLRLM features are used to describe the length of successive pixels with the same grey level value in terms of pixels.GLCM and GLRLM are important markers for assessing tumor heterogeneity and for better characterization of malignant subtypes.
Waugh et al. 27 in their study noticed that all co-occurrence RF had higher accuracy (71.4% and AUC -0.745) in differentiating ductal and lobular carcinoma than entropy features (64.7% and AUROC -0.632).Holli et al. 28 noticed that the co-occurrence RF of subtraction first images was more statistically significant than that of other features (Table 6).They also achieved a classification accuracy of 100% using first subtraction and contrast series using nonlinear and linear discriminant analyses.Our study also noticed that co-occurrence matrix features, such as sum squares, difference average, and cluster tendency, exhibited good accuracy in differentiating ductal and lobular carcinomas.In addition to co-occurrence matrix features, we also noticed additional categories showing statistically significant differences, which were not reported by Holli et al. 28 and Waugh et al. 27 A study by Fusco et al. 24 observed that kurtosis and skewness (AUC = 0.71) in X-ray mammography and range, energy, entropy, and gray-level non-uniformity (GLN) of the GLRLM from DCE-MRI were the best predictors for differentiating benign and malignant lesions.Niu et al. 25 they studied the accuracy of RF extracted from digital mammography (DM), digital breast tomosynthesis (DBT), diffusion (DWI), and DCE MRI for the characterization of breast lesions and noticed that the RF extracted from DWI and DCE MRI yielded higher AUC, SN, and SP with DCE having the upper hand compared to DWI, and lower AUC, SN, and SP were noted from DM. Militello et al. 26 reported that shape-based features such as least axis length, flatness, and elongation, GLCM-based features such as joint energy, GLRLM features such as short run emphasis, and Gray level size zone (GLSZM) of size-non-uniformity from DCE-MRI exhibited the highest SN and SP in characterizing breast lesions.
A study by Lafci et al. 29 noticed that Gray level zone length matrix (GLZLM) features had the highest accuracy (AUC = 0.718) in distinguishing Luminal A and B types of ductal carcinoma.They also noted that Luminal B tumors had a larger volume than luminal A tumors as they are aggressive and require intense chemotherapy, and observed shape-based features such as voxel volume showed significant differences between A and B. In our study, we also noted a larger voxel volume for ILC (5144 AE 306.5) than for IDC (3899 AE 684.3); however, we did not notice a statistically significant difference because of the smaller sample size.Literature suggests that ILC has a larger volume and is more aggressive compared to IDC. 30 Our study has the following limitations.First, we did not utilize machine learning and deep learning classifiers for the prediction of ductal and lobular carcinomas due to the small sample size.Second, longitudinal studies with large sample sizes and machine learning methods were used to further validate the results.Third, as the RF is obtained using manual segmentation, it is time-consuming and subjective.
The limited sample size of a pilot study may introduce biases into the study, which examined the use of MRI radiomic characteristics to distinguish between invasive ductal and lobular cancer of the breast.This would restrict the applicability of the findings.There is a chance that overfitting and inflated diagnostic accuracy will occur, so interpretation should be done carefully.Variations in imaging protocols, equipment, and radiomic feature extraction techniques between different institutions can lead to problems with reproducibility.Larger, multicenter studies should be prioritized in future research initiatives to improve generalizability and sample variety.The study's dependability will be increased by addressing potential biases through stringent statistical validation, using standardized imaging techniques, and using external validation datasets.Additionally, examining the influence of biological and molecular factors on radiomic features could lead to a more thorough comprehension of the distinction between invasive ductal and lobular carcinoma based on imaging, which could improve the accuracy and clinical applicability of breast cancer diagnosis.

Conclusions
Classification of BC into histological subgroups is a dynamic process.Our study suggested an RF-based method for ductal and lobular carcinoma characterization using T1 dynamic contrast sequence, which might give radiologists additional value for decision making in a noninvasive method and could be utilized clinically for malignant BC classification.Due to potential biases and limited application, the pilot study's small sample size investigating MRI radiomic characteristics for differentiating between IDC and ILC cancer may be problematic.Cautious interpretation is necessary because of potential problems including overfitting and overstated diagnostic accuracy.Larger, multicenter studies are crucial for better generalizability since variations in imaging methods throughout institutions may present reproducibility issues.Future studies should focus on external validation datasets, standardized imaging methods, and rigorous statistical validation to improve reliability.They should also look into the influence of biological components to gain a more thorough understanding of breast cancer diagnosis.

Title and Abstract:
The title effectively summarizes the main objective of the study.

○
The abstract provides a clear overview of the study's background, methods, results, and conclusions.It effectively communicates the main findings.However, it lacks specific details on the radiomic features analyzed and the statistical methods used.

Introduction:
The introduction provides relevant background information on breast cancer and the importance of early and precise identification of breast lesions.

○
It effectively sets the stage for the study and highlights the potential clinical significance of the research.However, it could be improved by providing more context on the challenges in accurately diagnosing invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) and discussing the limitations of current diagnostic methods.

Methods:
The methods section provides a detailed description of the study design, patient population, MRI imaging protocol, and radiomic feature extraction methods.

○
The inclusion and exclusion criteria are clearly stated, and the data source is properly cited.However, the rationale for the sample size and radiomic feature selection criteria should be provided.

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The use of the mRMR approach and Mann-Whitney U-test for feature selection and statistical analysis is appropriate.However, more details on the statistical analysis methods used for ROC curve analysis would be beneficial.

Results:
The results section presents the findings of the study, demonstrating significant differences in MRI radiomic features between IDC and ILC.

○
The results are clearly presented, but more specific quantitative results, such as effect sizes, confidence intervals, and p-values, would enhance the interpretation of the findings.

Discussion:
The discussion interprets the findings in the context of existing literature and ○

5.
highlights the potential clinical implications of using MRI radiomic features to differentiate between IDC and ILC.The discussion acknowledges limitations of the study but could be expanded to discuss potential biases, reproducibility issues, and future research directions in more detail.

Conclusion:
The conclusion summarizes the key findings of the study but could be strengthened by emphasizing the clinical significance of the results and suggesting potential implications for breast cancer diagnosis and treatment.

○
It would be beneficial to provide a brief summary of the study's limitations and future research directions in the conclusion section.○ 6.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?Yes Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Avantsa Rohini
Radiodiagnosis and Imaging, MNR Medical college and Hospital, Sangareddy, Telangana, India This is a very article.The concept and idea of differentiating invasive ductal carcinoma and invasive lobular carcinoma prior to invasive test based on MRI radiomic features is interesting, and encouraging.The results of the study might encourage future studies and help in reducing the time gap between diagnosis and treatment of breast cancer.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?I cannot comment.A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.

Reviewer Expertise: RADIO DIAGNOSIS AND IMAGING
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
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Figure 1 .
Figure 1.Example of the segmentation of the lesion in axial T1 (3D) dynamic contrast enhanced sequence.

1 Report 28
Demographic characteristics and RF of IDC and ILC (Spread Sheet) -MRI images (DICOM) Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).expertise to confirm that it is of an acceptable scientific standard.Version February 2024 https://doi.org/10.5256/f1000research.160089.r243255© 2024 Mustapha M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Table 2 .
MRI acquisition parameters of axial T1 dynamic contrast enhancement sequence.

Table 3 .
The RF selected using mRMR.ability of the RF for IDC and ILC.Sensitivity and Specificity, represents the true positive and true negative rates of IDC and ILC using RF.Statistical significance was set at p < 0.001.ResultsIn our study, we analyzed the RF extracted from DCE-MRI.Our study extracted 107 features from the DCE-MRI sequence for each subject.The mRMR technique identified 15 RF features that were relevant for differentiating between IDC and ILC Table3.Of the 15 selected features, 10 DCE MRI-based RF were significant for differentiating between IDC and ILC.A total of 58 cases were included in the study.The mean age (years) of the patients with IDC and ILC was 48 AE 11.15 and 59 AE 11 years, respectively. discriminative

Table 5 .
Area under curve, sensitivity, specificity, and cut-off value for MRI based Radiomic features for differentiating malignant lesions of breast (IDC and ILC).

Table 6 .
Comparison of radiomic features based breast lesion classification among various studies.