Keywords: Radiomics, Radiomics, Breast cancer, TNBC, TP53 mutation
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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