Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features.

Abstract

e12533 Background: Temporal radiomic features (TRF) extracted from dynamic contrast-enhanced breast MR (DCE-MR), which provide important information about tumor heterogeneity, offer a non-invasive approach to predict high-risk groups and could potentially serve as a cost-effective alternative to the genetic assay. This study explored how TRF from breast MR could be integrated to predict the high-risk group of OncotypeDX (ODX). Methods: In 173 patients with breast cancer [low-risk, 144 (83.2%); high-risk, 29 (16.8%)], TRF such as dynamic signal intensity changes and texture variations were derived from the imaging sequences. Hierarchical clustering was applied to reduce feature redundancy and identify significant predictors. Machine learning algorithms such as random forest, SVM, logistic regression, and KNN were utilized with 7-fold cross validation model. Results: Cross-validation revealed that models with TRF were consistently better than those without it across 4 different machine learning algorithms. Random forest AUC improved from 0.48 to 0.56, support vector machines from 0.46 to 0.63, logistic regression from 0.51 to 0.69, and K-nearest neighbors from 0.61 to 0.73 (Table). Conclusions: Incorporating TRF from DCE-MR images into a machine learning model improved the predictive performance for high-risk groups of ODX compared to using only the reference RF. Comparison of classifier performance using radiomic and temporal radiomic features. Random forest SVM Logistic regression KNN Features RF RF + TRF RF RF + TRF RF RF + TRF RF TRF AUC Fold1 0.38 (0.13, 0.65) 0.41 (0.14, 0.69) 0.68 (0.38, 0.94) 0.54 (0.23, 0.84) 0.39 (0.12, 0.71) 0.50 (0.18, 0.85) 0.60 (0.22, 0.90) 0.55 (0.27, 0.82) AUC Fold2 0.62 (0.35, 0.86) 0.52 (0.24, 0.85) 0.71 (0.45, 0.92) 0.65 (0.40, 0.87) 0.31 (0.09, 0.57) 0.55 (0.28, 0.84) 0.54 (0.29, 0.78) 0.69 (0.42, 0.91) AUC Fold3 0.57 (0.30, 0.82) 0.68 (0.38, 0.93) 0.46 (0.10, 0.75) 0.50 (0.12, 0.86) 0.49 (0.13, 0.83) 0.76 (0.53, 0.96) 0.43 (0.00, 0.82) 0.70 (0.48, 0.90) AUC Fold4 1.00 (1.00, 1.00) 0.83 (0.67, 0.96) 0.08 (0.00, 0.21) 0.79 (0.62, 0.96) 1.00 (1.00, 1.00) 1.00 (1.00, 1.00) 0.77 (0.61, 0.90) 0.75 (0.56, 0.92) AUC Fold5 0.23 (0.00, 0.50) 0.30 (0.00, 0.63) 0.83 (0.62, 0.98) 0.44 (0.13, 0.83) 0.36 (0.08, 0.62) 0.44 (0.13, 0.82) 0.62 (0.27, 0.89) 0.66 (0.46, 0.83) AUC Fold6 0.23 (0.00, 0.50) 0.66 (0.39, 0.89) 0.35 (0.00, 0.78) 0.76 (0.57, 0.95) 0.70 (0.44, 0.91) 0.70 (0.37, 1.00) 0.62 (0.13, 0.95) 0.85 (0.62, 1.00) AUC Fold7 0.35 (0.08, 0.67) 0.48 (0.14, 0.91) 0.11 (0.00, 0.32) 0.75 (0.53, 0.91) 0.33 (0.08, 0.67) 0.86 (0.68, 1.00) 0.70 (0.45, 0.90) 0.90 (0.77, 1.00) AUC Average 0.48 0.56 0.46 0.63 0.51 0.69 0.61 0.73 Data were expressed as Area Under the Curve (AUC) and 95% Confidence Intervals (CI). RF, Radiomic features; TRF, Temporal radiomic features; SVM, Support Vector Machine; KNN, K-Nearest Neighbors, AUC, Area Under the ROC Curve.