DCE-MRI Radiomics Analysis in Differentiating Luminal A and Luminal B Breast Cancer Molecular Subtypes

Molecular Changes in Breast Cancer Induced by Radiation Therapy

PURPOSE

Breast cancer treatments are based on prognostic clinicopathologic features that form the basis for therapeutic guidelines. Although the utilization of these guidelines has decreased breast cancer-associated mortality rates over the past three decades, they are not adequate for individualized therapy. Radiation therapy (RT) is the backbone of breast cancer treatment. Although a highly successful therapeutic modality clinically, from a biological perspective, preclinical studies have shown RT to have the potential to alter tumor cell phenotype, immunogenicity, and the surrounding microenvironment, potentially changing the behavior of cancer cells and resulting in a significant variation in RT response. This review presents the recent advances in revealing the complex molecular changes induced by RT in the treatment of breast cancer and highlights the complexities of translating this information into clinically relevant tools for improved prognostic insights and the revelation of novel approaches for optimizing RT.

METHODS AND MATERIALS

Current literature was reviewed with a focus on recent advances made in the elucidation of tumor-associated radiation-induced molecular changes across molecular, genetic, and proteomic bases. This review was structured with the aim of providing an up-to-date overview over the very broad and complex subject matter of radiation-induced molecular changes and radioresistance, familiarizing the reader with the broader issue at hand.

RESULTS

The subject of radiation-induced molecular changes in breast cancer has been broached from various physiological focal points including that of the immune system, immunogenicity and the abscopal effect, tumor hypoxia, breast cancer classification and subtyping, molecular heterogeneity, and molecular plasticity. It is becoming increasingly apparent that breast cancer clinical subtyping alone does not adequately account for variation in RT response or radioresistance. Multiple components of the tumor microenvironment and immune system, delivered RT dose and fractionation schedules, radiation-induced bystander effects, and intrinsic tumor physiology and heterogeneity all contribute to the resultant RT outcome.

CONCLUSIONS

Despite recent advances and improvements in anticancer therapies, tumor resistance remains a significant challenge. As new analytical techniques and technologies continue to provide crucial insight into the complex molecular mechanisms of breast cancer and its treatment responses, it is becoming more evident that personalized anticancer treatment regimens may be vital in overcoming radioresistance.

Radiomics Nomogram Based on Dual-Sequence MRI for Assessing Ki-67 Expression in Breast Cancer

BACKGROUND

Radiomics has been extensively applied in predicting Ki-67 in breast cancer (BC). However, this is often confined to the exploration of a single sequence, without considering the varying sensitivity and specificity among different sequences.

PURPOSE

To develop a nomogram based on dual-sequence MRI derived radiomic features combined with clinical characteristics for assessing Ki-67 expression in BC.

STUDY TYPE

Retrospective.

POPULATION

227 females (average age, 51 years) with 233 lesions and pathologically confirmed BC, which were divided into the training set (n = 163) and test set (n = 70).

FIELD STRENGTH/SEQUENCE

3.0-T, T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (EPI sequence).

ASSESSMENT

The regions of interest were manually delineated on ADC and DCE-MRI sequences. Three radiomics models of ADC, DCE-MRI, and dsMRI (combined ADC and DCE-MRI sequences) were constructed by logistic regression and the radiomics score (Radscore) of the best model was calculated. The correlation between Ki-67 expression and clinical characteristics such as receptor status, axillary lymph node (ALN) metastasis status, ADC value, and time signal intensity curve was analyzed, and the clinical model was established. The Radscore was combined with clinical predictors to construct a nomogram.

STATISTICAL TESTS

The independent sample t-test, Mann-Whitney U test, Chi-squared test, Interclass correlation coefficients (ICCs), single factor analysis, least absolute shrinkage and selection operator (LASSO), logistic regression, receiver operating characteristics, Delong test, Hosmer_Lemeshow test, calibration curve, decision curve. A P-value <0.05 was considered statistically significant.

RESULTS

In the test set, the prediction efficiency of the dsMRI model (AUC = 0.862) was higher than ADC model (AUC = 0.797) and DCE-MRI model (AUC = 0.755). With the inclusion of estrogen receptor (ER) and ALN metastasis, the nomogram displayed quality improvement (AUC = 0.876), which was superior to the clinical model (AUC = 0.787) and radiomics model.

DATA CONCLUSION

The nomogram based on dsMRI radiomic features and clinical characteristics may be able to assess Ki-67 expression in BC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 3.

Expression profile of messenger and micro RNAs related to the histaminergic system in patients with five subtypes of breast cancer

Disparities in estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor 2 (HER2), and Ki67 proliferation indices facilitate the categorization of breast cancer into four principal subtypes: luminal A, luminal B, HER2-positive, and triple-negative breast cancer (TNBC). Preclinical studies investigating the therapeutic potential of histaminergic system targeting in breast cancer have shown promising results. This study aimed to assess the expression profiles of messenger ribonucleic acid (mRNA) and micro RNA (miRNA) related to the histaminergic system in five subtypes of breast cancer among Polish women. Patients with five breast cancer subtypes were included in the study: luminal A (n = 130), luminal B (n = 196, including HER2-, n =100; HER2+, n= 96), HER2+ (n = 36), and TNBC (n = 43). They underwent surgery during which the tumor tissue was removed along with a margin of healthy tissue (control material). Molecular analysis included the determination of a microarray profile of mRNAs and miRNAs associated with the histaminergic system, real-time polymerase chain reaction preceded by reverse transcription of selected genes, and determination of histamine receptors (human histamine H1 receptor [HRH1], human histamine H2 receptor [HRH2], and human histamine H4 receptor [HRH4]) using an enzyme-linked immunosorbent assay. Statistical analysis was performed with statistical significance at p < 0.05. Nine mRNAs were significantly differentiated in breast cancer sections, regardless of subtype, compared to control samples: HRH1, HRH2, HRH4, histamine N-methyltransferase (HNMT), 5-hydroxytryptamine receptor 6 (HTR6), endothelin 1 (EDN1), endothelin receptor type A (EDNRA), adenosine deaminase (ADA), solute carrier family 22 member 3 (SLC3A2). Predictive analysis showed that hsa-miR-34a potentially regulates HRH1 expression, whereas hsa-miR-3140-5p and hsa-miR-4251 potentially affect HRH2 expression. In contrast, HRH4 and EDN1 expression were regulated by hsa-miR-1-3p. The expression of HNMT is potentially regulated by one miRNA, hsa-miR-382, whereas EDNRA expression is regulated by two miRNA molecules: hsa-miR-34a and hsa-miR-16. In contrast, hsa-miR-650 is involved in the regulation of HTR6 expression, whereas hsa-miR-1275 potentially interacts with three mRNAs: ADA, SLC23A2, and HRH1. Molecular analysis confirmed that the selected mRNA and miRNA transcripts could be promising molecular markers and therapeutic targets.

Expression Profiles of Dopamine-Related Genes and miRNAs Regulating Their Expression in Breast Cancer

This study aimed to assess the expression profile of messenger RNA (mRNA) and microRNA (miRNA) related to the dopaminergic system in five types of breast cancer in Polish women. Patients with five breast cancer subtypes were included in the study: luminal A (n = 130), luminal B (n = 196, including HER2-, n = 100; HER2+, n = 96), HER2+ (n = 36), and TNBC (n = 43); they underwent surgery, during which tumor tissue was removed along with a margin of healthy tissue (control material). The molecular analysis included a microarray profile of mRNAs and miRNAs associated with the dopaminergic system, a real-time polymerase chain reaction preceded by reverse transcription for selected genes, and determinations of their concentration using enzyme-linked immunosorbent assay (ELISA). The conducted statistical analysis showed that five mRNAs statistically significantly differentiated breast cancer sections regardless of subtype compared to control samples; these were dopamine receptor 2 (DRD2), dopamine receptor 3 (DRD3), dopamine receptor 25 (DRD5), transforming growth factor beta 2 (TGF-β-2), and caveolin 2 (CAV2). The predicted analysis showed that hsa-miR-141-3p can regulate the expression of DRD2 and TGF-β-2, whereas hsa-miR-4441 is potentially engaged in the expression regulation of DRD3 and DRD5. In addition, the expression pattern of DRD5 mRNA can also be regulated by has-miR-16-5p. The overexpression of DRD2 and DRD3, with concomitant silencing of DRD5 expression, confirms the presence of dopaminergic abnormalities in breast cancer patients. Moreover, these abnormalities may be the result of miR-141-3P, miR-16-5p, and miR-4441 activity, regulating proliferation or metastasis.

Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC.

MATERIALS AND METHODS

A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves.

RESULTS

Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation.

CONCLUSION

The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.

Application of Texture and Volume Model Analysis to Dedicated Axillary High-resolution 3D T2-weighted MR Imaging: A Novel Method for Diagnosing Lymph Node Metastasis in Patients with Clinically Node-negative Breast Cancer

PURPOSE

To evaluate the effectiveness of the texture analysis of axillary high-resolution 3D T2-weighted imaging (T2WI) in distinguishing positive and negative lymph node (LN) metastasis in patients with clinically node-negative breast cancer.

METHODS

Between December 2017 and May 2021, 242 consecutive patients underwent high-resolution 3D T2WI and were classified into the training (n = 160) and validation cohorts (n = 82). We performed manual 3D segmentation of all visible LNs in axillary level I to extract the texture features. As the additional parameters, the number of the LNs and the total volume of all LNs for each case were calculated. The least absolute shrinkage and selection operator algorithm and Random Forest were used to construct the models. We constructed the texture model using the features from the LN with the largest least axis length in the training cohort. Furthermore, we constructed the 3 models combining the selected texture features of the LN with the largest least axis length, the number of LNs, and the total volume of all LNs: texture-number model, texture-volume model, and texture-number-volume model. As a conventional method, we manually measured the largest cortical diameter. Moreover, we performed the receiver operating curve analysis in the validation cohort and compared area under the curves (AUCs) of the models.

RESULTS

The AUCs of the texture model, texture-number model, texture-volume model, texture-number-volume model, and conventional method in the validation cohort were 0.7677, 0.7403, 0.8129, 0.7448, and 0.6851, respectively. The AUC of the texture-volume model was higher than those of other models and conventional method. The sensitivity, specificity, positive predictive value, and negative predictive value of the texture-volume model were 90%, 69%, 49%, and 96%, respectively.

CONCLUSION

The texture-volume model of high-resolution 3D T2WI effectively distinguished positive and negative LN metastasis for patients with clinically node-negative breast cancer.

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.

Predictive value of triple negative breast cancer based on DCE-MRI multi-phase full-volume ROI clinical radiomics model

BACKGROUND

Since no studies compared the value of radiomics features of distinct phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting triple-negative breast cancer (TNBC).

PURPOSE

To identify the optimal phase of DCE-MRI for diagnosing TNBC and, in combination with clinical factors, to develop a clinical-radiomics model to well predict TNBC.

MATERIAL AND METHODS

This retrospective study included 158 patients with pathology-confirmed breast cancer, including 38 cases of TNBC. The patients were randomly divided into the training and validation set (7:3). Eight radiomics models were built based on eight DCE-MR phases, and their performances were evaluated using receiver operating characteristic curve (ROC) and DeLong's test. The Radscore derived from the best radiomics model was integrated with independent clinical risk factors to construct a clinical-radiomics predictive model, and evaluate its performance using ROC analysis, calibration, and decision curve analyses.

RESULTS

WHO classification, margin, and T2-weighted (T2W) imaging signals were significantly correlated with TNBC and independent risk factors for TNBC (P<0.05). The clinical model yielded areas under the curve (AUCs) of 0.867 and 0.843 in the training and validation sets, respectively. The radiomics model based on DCEphase7 achieved the highest efficacy, with an AUC of 0.818 and 0.777. The AUC of the clinical-radiomics model was 0.936 and 0.886 in the training and validation sets, respectively. The decision curve showed the clinical utility of the clinical-radiomics model.

CONCLUSION

The radiomics features of DCE-MRI had the potential to predict TNBC and could improve the performance of clinical risk factors for preoperative personalized prediction of TNBC.

Artificial intelligence in breast imaging: potentials and challenges

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.

A primary luminal/HER2 negative breast cancer patient with mismatch repair deficiency

Here, we present the case of a 47-year-old woman diagnosed with luminal B breast cancer subtype and provide an in-depth analysis of her gene mutations, chromosomal alterations, mRNA and protein expression changes. We found a point mutation in the FGFR2 gene, which is potentially hyper-activating the receptor function, along with over-expression of its ligand FGF20 due to genomic amplification. The patient also harbors somatic and germline mutations in some mismatch repair (MMR) genes, with a strong MMR mutational signature. The patient displays high microsatellite instability (MSI) and tumor mutational burden (TMB) status and increased levels of CTLA-4 and PD-1 expression. Altogether, these data strongly implicate that aberrant FGFR signaling, and defective MMR system might be involved in the development of this breast tumor. In addition, high MSI and TMB in the context of CTLA-4 and PD-L1 positivity, suggest the potential benefit of immune checkpoint inhibitors. Accurate characterization of molecular subtypes, based on gene mutational and expression profiling analyses, will be certainly helpful for individualized treatment and targeted therapy of breast cancer patients, especially for those subtypes with adverse outcome.

Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study

This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.

Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches

Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER⁺ vs. ER^-, PR⁺ vs. PR^-, HER2⁺ vs. HER2^-, and IDC vs. ILC classification tasks. The best results were obtained for the ER⁺ vs. ER^- and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers.

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer

Background

The goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer.

Methods

One hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness.

Results

Multivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78-0.93) and the testing set (AUC, 0.80; 95% CI, 0.65-0.95).

Conclusion

The DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.