Preliminary exploration of radiomic mammographic analysis in triple negative breast cancer related to BRCA profile

This retrospective study evaluated the applicability of radiomics analysis to mammographic images of patients with triple-negative breast cancer (TNBC) to identify features differentiating BRCA gene's mutational status. The mammographic images of 52 patients histologically diagnosed with TNBC, (13 BRCA-mutated patients and 39 BRCA wild-type ones), were included and 53 tumor lesions were manually segmented in the mammographic projection where they were better demarcable. An additional elliptical ROI of standard size was drawn in the most homogeneous area of the contralateral healthy gland, using the analogue mammographic projection of the same date or, if not available, of the corresponding bilateral mammographic investigation closer to the time of diagnosis. Lesions consisted of 36 masses, 2 pathological microcalcifications, and 15 masses with microcalcifications. Radiomic features were extracted using Pyradiomics-3D. Preliminary analysis confirmed feasibility and showed differences in texture features, particularly GLCM SumEntropy, between BRCA-mutated and non-mutated patients. Moreover, the study enhanced the role of healthy glandular tissue in distinguishing the two groups, supporting and reinforcing previous MRI-based radiomics findings in the same population. The study concludes that radiomics analysis of diagnostic mammograms in TNBC patients is feasible and may help build predictive models to discriminate between BRCA mutated and non-mutated patients.

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Journal
Scientific Reports
Published
2026-02-13
DOI
https://doi.org/10.1038/s41598-026-35774-1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Preliminary exploration of radiomic mammographic analysis in triple negative breast cancer related to BRCA profile

Annarita Pecchi, M. Dominici, Angela Toss, Erica Balboni et al.
Scientific Reports
Radiomics and Machine Learning in Medical Imaging
article

Preliminary exploration of radiomic mammographic analysis in triple negative breast cancer related to BRCA profile

Annarita Pecchi, M. Dominici, Angela Toss, Erica Balboni, Luca Nocetti, Pietro Torricelli, Cecilia Beretta, Giulia Besutti, Gabriele Guidi, Laura Cortesi, Giulia Sessa, Chiara Bozzola
article en

Abstract

This retrospective study evaluated the applicability of radiomics analysis to mammographic images of patients with triple-negative breast cancer (TNBC) to identify features differentiating BRCA gene's mutational status. The mammographic images of 52 patients histologically diagnosed with TNBC, (13 BRCA-mutated patients and 39 BRCA wild-type ones), were included and 53 tumor lesions were manually segmented in the mammographic projection where they were better demarcable. An additional elliptical ROI of standard size was drawn in the most homogeneous area of the contralateral healthy gland, using the analogue mammographic projection of the same date or, if not available, of the corresponding bilateral mammographic investigation closer to the time of diagnosis. Lesions consisted of 36 masses, 2 pathological microcalcifications, and 15 masses with microcalcifications. Radiomic features were extracted using Pyradiomics-3D. Preliminary analysis confirmed feasibility and showed differences in texture features, particularly GLCM SumEntropy, between BRCA-mutated and non-mutated patients. Moreover, the study enhanced the role of healthy glandular tissue in distinguishing the two groups, supporting and reinforcing previous MRI-based radiomics findings in the same population. The study concludes that radiomics analysis of diagnostic mammograms in TNBC patients is feasible and may help build predictive models to discriminate between BRCA mutated and non-mutated patients.

Scientific ReportsVol. 16(1)
University of Modena and Reggio Emilia (IT), Azienda Unita' Sanitaria Locale Di Modena (IT), Azienda Sanitaria Unità Locale di Reggio Emilia (IT)
European Commission
Reduced inequalities
Openalex Percentile: Top 56%
Radiomics and Machine Learning in Medical Imaging
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