危险分层
分层(种子)
乳腺癌
计算机科学
人工智能
癌症
机器学习
医学
内科学
生物
种子休眠
植物
发芽
休眠
作者
Hang Zhang,Fan Yang,Y Xu,Shen Zhao,Yizhou Jiang,Yi Xiao,Yi Xiao
标识
DOI:10.1016/j.xcrm.2024.101924
摘要
Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common type of breast cancer, with continuous recurrence remaining an important clinical issue. Current relapse predictive models in HR+/HER2- breast cancer patients still have limitations. The integration of multidimensional data represents a promising alternative for predicting relapse. In this study, we leverage our multi-omics cohort comprising 579 HR+/HER2- breast cancer patients (200 patients with complete data across 7 modalities) and develop a machine-learning-based model, namely CIMPTGV, which integrates clinical information, immunohistochemistry, metabolomics, pathomics, transcriptomics, genomics, and copy number variations to predict recurrence risk of HR+/HER2- breast cancer. This model achieves concordance indices (C-indices) of 0.871 and 0.869 in the train and test sets, respectively. The risk population predicted by the CIMPTGV model encompasses those identified by single-modality models. Feature analysis reveals that synergistic and complementary effects exist in different modalities. Simultaneously, we develop a simplified model with a mean area under the curve (AUC) of 0.840, presenting a useful approach for clinical applications.
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