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Abstract #1049

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

Jinwoo Hwang1,2, Youn Joo Lee3, Eunju Kim2, and Sung Hun Kim4
1Biomedical Engineering, Hanyang University, Seoul, Korea, Republic of, 2Imaging Clinical Science, Philips Korea, Seoul, Korea, Republic of, 3Radiology, Daejeon St. Mary’s Hospital, The Catholic University of Korea College of Medicine, Daejeon, Korea, Republic of, 4Radiology, Seoul St. Mary’s Hospital, The Catholic University of Korea College of Medicine, Seoul, Korea, Republic of

Synopsis

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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Keywords