Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

作者：Zehua Wang, Ruichong Lin, Yanchun Li, Jin Zeng, Yongjian Chen, Wenhao Ouyang, Han Li, Xueyan Jia, Zijia Lai, Yunfang Yu, Herui Yao, Weifeng Su · 发表于：Precision Clinical Medicine · 年份：2024 · DOI：10.1093/pcmedi/pbae012 · 被引用次数：31 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Gene expression and cancer classification

Abstract Background The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS). Methods We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95). Result Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016–0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041–0.334, P < 0.0001), and the e...