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September 17, 2025

Multimodality MRI radiomics analysis for predicting TP53 mutations in triple negative breast cancer

Key Points

The SVM classifier achieved an accuracy of 0.82 in predicting tp53 mutations, indicating good predictive performance in the validation cohort.
In a retrospective study involving 105 TNBC patients, 20 relevant features were selected from a total of 3,411 mri radiomic features using sophisticated analysis methods.
This machine learning-based model highlights the potential of mri radiomics to improve treatment planning for patients with tp53 mutations in triple-negative breast cancer.
Enhancing reliability and generalizability, this approach may reduce costs compared to traditional invasive techniques, broadening the clinical applicability of predictions.

Abstract

Motivation: TP53 mutations in triple-negative breast cancer(TNBC) are linked to aggressive behavior and treatment resistance. A non-invasive detection method could improve treatment planning and prognosis. Goal(s): This study evaluates the potential of MRI-based radiomic features combined with a machine learning classifier to predict TP53 status in TNBC preoperatively. Approach: In a retrospective study of 105 TNBC patients, 20 features were selected from 3,411 MRI radiomic features using Pearson correlation and Recursive Feature Elimination(RFE). A Support Vector Machine(SVM) was then trained and evaluated. Results: The SVM classifier achieved an AUC of 0.79 and an accuracy of 0.82 in predicting TP53 mutations in the validation cohort. Impact: This machine learning-based MRI radiomics model, trained on multi-center, multi-vendor data, demonstrated strong predictive performance, enhancing reliability, generalizability, and patient convenience. It reduces costs compared to invasive methods and offers broad clinical applicability across diverse fields.

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