Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX
Ruixin Zhang, Kaiting Wang, Shiwei Wang, Chunjie Wang, Tingting Cao, Ce Ci, Maosheng Xu, Min Ge
Abstract
BACKGROUND: Proper stratification of recurrence risk in breast cancer is crucial for guiding treatment decisions. This study aims to predict the recurrence risk of breast cancer patients using a multimodal deep learning model that integrates multiple sequence MRI imaging features with clinicopathologic characteristics. METHODS: In this retrospective study, we enrolled 574 patients with non-metastatic invasive breast cancer from two Chinese institutions between September 2012 and July 2019. We developed a multimodal deep learning (MDL) model by constructing a multi-instance learning framework based on convolutional neural networks. We integrated imaging features from T2WI, DWI, and DCE-MRI sequences with clinicopathologic features for breast cancer recurrence risk stratification. Subsequently, the performance of the MDL model was evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). Survival analysis was conducted with Kaplan-Meier survival curves to stratify breast cancer patients into high and low-recurrence risk groups. Time-dependent ROC curves were used to assess 3-year, 5-year, and 7-year recurrence-free survival (RFS) for breast cancer patients. Additionally, we performed differential and enrichment analyses on Oncotype DX genes. We correlated these genes with clinicopathologic features and deep-learning radiographic features using univariate Cox regression and Pearson correlation analysis. RESULTS: The MDL model demonstrated good performance in predicting breast cancer recurrence risk and accurately differentiated between high- and low-recurrence risk groups, with an AUC as high as 0.915 (95% CI 0.8448-0.9856). The C-index of prediction models was 0.803 in the testing cohort. The AUCs for 5-year and 7-year RFS were 0.936 (95% CI 0.876-0.997) and 0.956 (95% CI 0.902-1.000) in the validation cohort. In the testing cohort, these AUCs were 0.836 (95% CI 0.763-0.909) and 0.783 (95% CI 0.676-0.891). This study found a significant correlation between Oncotype DX gene expression, clinicopathologic features, and deep-learning radiographic features (p < 0.05). CONCLUSIONS: This study validated the robust predictive accuracy of the MDL model in identifying high- and low-risk groups for recurrence. The correlations identified between Oncotype DX genes, clinicopathologic features, and deep-learning radiographic features offer novel insights for future biomarker research in breast cancer.