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

Version Changes

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.

Abstract

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. ¹ According 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 , 4} Breast 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. It provides information about tumor vascularity, and enhancement curves are frequently used to increase specificity (greater than 90%) for diagnosing cancer. ^{5 – 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. These drawbacks highlight the necessity of continued research, thorough multidisciplinary approaches, and diagnostic technology developments in order to improve patient outcomes and increase the precision of IDC and ILC diagnoses. ^{9 – 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 , 14} The 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 , 16}

There 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. ¹⁷ Our 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.

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). ²⁰ Sample 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 ± 11.15 and 59 ± 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.

Table 1. Demographic and clinical characteristics of subjects (n = 58).

	Malignant breast lesion categories
	IDC	ILC
Subject (n)	30	28
Age in years (Mean± SD)	48 ± 11.15	59 ± 11
Gender	Female

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

Acquisition parameter	DCE MRI
Type	3D
Sequence	Gradient
TR (ms)	4–6
TE (ms)	1–2.5
Matrix size	320 × 320
Slice thickness (mm)	1.2
Flip angle (degree)	10
Slice spacing (mm)	5.5

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 ± 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.

Figure 1. Example of the segmentation of the lesion in axial T1 (3D) dynamic contrast enhanced sequence.

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. ²¹ Fifteen RF features were selected for the subsequent analysis ( Table 3).

Table 3. The RF selected using mRMR.

S.No.	MRI radiomic features
1	GLDM Gray Level Variance
2	GLDM Gray level Non Uniformity
3	GLDM Low gray level emphasis
4	GLCM Sum Squares
5	GLCM Difference Average
6	GLCM Cluster Tendency
7	GLCM Interquartile Range
8	First Order RMAD
9	First Order Entropy
10	First Order Variance
11	GLRLM Gray Level Variance
12	GLRLM Gray Level Non Uniformity Normalized
13	GLRLM Run Entropy
14	GLSZM Size Zone NonUniformity
15	GLSZM Zone Percentage

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 discriminative 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.

Results

In 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 Table 3. 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 ± 11.15 and 59 ± 11 years, respectively.

MRI radiomic features

For 3D DCE MRI sequence, Gray level dependence matrix (GLDM) gray level variance for IDC and ILC was 288.6 ± 136.4 and 1187.0 ± 342.5 (p < 0.001), Gray level co-occurrence matrix (GLCM) square for IDC and ILC was 118.5 ± 63.15 and 492.4 ± 277.6 (p < 0.001), GLCM difference average for IDC and ILC was 4.472 ± 2.694 and 20.45 ± 10.11 (p < 0.001), GLCM cluster tendency for IDC and ILC was 270.3 ± 81.47 and 1170.8 ± 145.5 (p < 0.001), GLCM interquartile range for IDC and ILC was 286.2 ± 52.8 and 1630.8 ± 332.9 (p < 0.001), first order robust-mean absolute deviation for IDC and ILC was 117.9 ± 18.2 and 403.5 ± 179.0 (p < 0.001), first order entropy for IDC and ILC was 4.776 ± 1.656 and 6.063 ± 1.697 (p < 0.001), first order variance for IDC and ILC was 44324.8 ± 3799.0 and 438604.1 ± 39979.8 (p < 0.001), Gray level run length matrix (GLRLM) gray level variance for IDC and ILC was 35.64 ± 31.20 and 741.40 ± 173.3 (p < 0.001) and GLRLM run entropy for IDC and ILC was 5.496 ± 1.677 and 6.814 ± 1.65 (p < 0.001) ( Table 4 and Figure 2).

Table 4. DCE-MRI based radiomic features for differentiating the malignant lesions of the breast.

MRI radiomic features	IDC (n = 30)	ILC (n = 28)	p-value
	(Mean ± SD)
DCE GLDM Gray Level Variance	288.6 ± 136.4	1187.0 ± 742.5	<0.001
DCE GLCM Sum Squares	118.5 ± 63.15	492.4 ± 277.6	<0.001
DCE GLCM Difference Average	4.472 ± 2.694	20.45 ± 10.11	<0.001
DCE GLCM Cluster Tendency	270.3 ± 81.47	1170.8 ± 745.5	<0.001
DCE GLCM Interquartile Range	286.2 ± 152.8	1630.8 ± 732.9	<0.001
DCE First Order RMAD	117.9 ± 18.2	403.5 ± 279.0	<0.001
DCE First Order Entropy	4.776 ± 1.656	6.063 ± 1.697	<0.001
DCE First Order Variance	44324.8 ± 3799.0	438604.1 ± 39979.8	<0.001
DCE GLRLM Gray Level Variance	35.64 ± 31.20	741.40 ± 473.3	<0.001
DCE GLRLM Run Entropy	5.496 ± 1.677	6.814 ± 1.65	<0.001

Figure 2. MRI-based significant radiomic features for differentiating IDC and ILC from axial T1 (3D) dynamic contrast enhanced sequence.

(a) GLDM Gray Level Variance; (b) GLCM Sum Squares; (c) GLCM Difference Average; (d) GLCM Cluster Tendency; (e) GLCM Interquartile Range; (f) First Order RMAD; (g) First Order Entropy; (h) First Order Variance; (i) GLRLM Gray Level Variance; (j) GLRLM Run Entropy.

Accuracy measures of radiomic features

The accuracy measures of RF for differentiating between IDC and ILC are listed in Table 5. For 3D DCE MRI sequence, GLRLM gray level variance at cut off value of 42, had higher sensitivity (SN 97.21%), specificity (SP 96.2%), and area under curve (AUC 0.998; GLCM interquartile range at cut off value of 357 had higher SN (95.24%), SP (97.31%), and AUC of 0.968; and GLCM difference average at a cut off value of 6.5, had higher SN 95.72%, SP 96.34% and AUC of 0.983 compared to GLDM gray level variance (SN 91.02%, SP 89.72%), GLCM sum squares (SN 85.91%, SP 82.72%), first order variance (SN 81.54%, SP 82.72%), GLCM cluster tendency (SN 81.77%, SP 80.13%), first order RMAD (SN 80.12%, SP 79.23%), first order entropy (SN 75.24%, SP 72.23%), GLRLM run entropy (SN 75.47%, SP 77.83%) ( Figure 3).

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).

	Radiomic Features	Area under the curve	Sensitivity (%)	Specificity (%)	Cut off value
DCE MRI	DCE GLDM Gray Level Variance	0.887	91.02	89.72	408.5
	DCE GLCM Sum Squares	0.877	85.91	82.72	169.5
	DCE GLCM Difference Average	0.983	95.72	96.34	6.5
	DCE GLCM Cluster Tendency	0.792	81.77	80.13	334
	DCE GLCM Interquartile Range	0.968	95.24	97.31	357
	DCE First Order RMAD	0.768	80.12	79.23	216
	DCE First Order Entropy	0.709	75.24	72.23	5.5
	DCE First Order Variance	0.805	81.54	82.72	780637
	DCE GLRLM Gray Level Variance	0.998	97.21	96.2	42
	DCE GLRLM Run Entropy	0.698	75.47	77.83	5.508

Figure 3. Receiver Operating Characteristic (ROC) curves for differentiating IDC and ILC from Axial T1 (3D) Dynamic contrast enhanced sequence.

(a) GLDM Gray Level Variance; (b) GLCM Sum Squares; (c) GLCM Difference Average; (d) GLCM Cluster Tendency; (e) GLCM Interquartile Range; (f) First Order RMAD; (g) First Order Entropy; (h) First Order Variance; (i) GLRLM Gray Level Variance; (j) GLRLM Run Entropy.

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 , 23}

Previous studies have focused on differentiating benign and malignant lesions of the breast using radiomic models in MRI. ^{24 – 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

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

Author (Year)	Our study	Fusco et al. 24 (2022)	Niu et al. 25 (2022)	Lafci et al. 29 (2022)	Militello et al. 26 (2021)	Waugh et al. 27 (2016)	Holli et al. 28 (2010)
Radiomic features (RF) obtained	107	48	105	43	107	220	300
Lesions	IDC and ILC	Benign & Malignant	Benign & Malignant	IDC (Luminal A and B)	Benign and Malignant	IDC and ILC	IDC and ILC
RF Category	Shape, GLDM, GLCM, First order, GLRLM, GLSZM, NGTDM	First and Second order features	Shape, GLDM, GLCM, First order, GLRLM, GLSZM, NGTDM	Conventional, Shape, Histogram, GLCM, GLRLM, NGLDM, GLZLM	Shape, Firstorder, GLCM, GLRLM, GLSZM, GLDM, NGTDM	Co-occurrence matrix	Co-occurrence matrix
Modality	MRI	MRI and X-ray	Digital Mammogarphy, Digital breast Tomosynthesis, MRI	MRI	MRI	MRI	MRI
Significant Radiomic features	GLDM Gray Level Variance, GLCM Sum Squares, GLCM Difference Average, GLCM Cluster Tendency, GLCM Interquartile Range, First Order RMAD, First Order Entropy, First Order Variance, GLRLM Gray Level Variance, GLRLM Run Entropy, Shape voxel volume and mess voulme	IQR, Variance, Correlation, Kurotsis, Skewness, Range, Energy, Entropy, GLN – GLRLM, GLN-GLSZM	GLSZM ZonePercentage, Firstorder Skewness, GLRLM ShortRunLowGrayLevelEmphasis, GLCM Imcl, GLCM ClusterShade, GLCM InverseVariance, Glcm MCC	Histogram: skewness, Shape: volume-ml, volume-voxel, GLCM: entropy.log10, entropy.log2, energy GLRLM: GLNU, RLNU, HGRE, NGLDM: busyness, GLZLM: GLNU, HGZE, ZLNU, SZE	GLCM Joint average, GLRLM Short run emphasis, Shape 3D Least axis length, Shape 3D Flatness, GLRLM Long run low gray level emphasis, GLCM Joint energy, Shape 3D Elongation, GLSZM Size zone non uniformity	Entropy features	GLCM based entropy features

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 ± 306.5) than for IDC (3899 ± 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.

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

Data availability

Underlying data

Duke breast cancer MRI data set used for this study are publicly available in the cancer imaging archive ^{18 , 19} at https://doi.org/10.7937/TCIA.e3sv-re93 under the terms of the http://creativecommons.org/licenses/by/3.0/ or the https://creativecommons.org/licenses/by/4.0/. There were no changes made to the dataset.

Figshare: Underlying data for ‘Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study’, ‘RF for IDC and ILC-F1000’, https://doi.org/10.6084/m9.figshare.24792693.v1. ²⁰

This project contains the following underlying data:

• -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).