Advancing cancer detection and treatment using longitudinal routine clinical data.
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Liu Fei, Wang Kai, Xu Hui, Tang Cheng, Shen Xian, Wang Meihao, Yang Lei, Yang Li, Liu Li, Hu Changxi, Li Gen, Wu Wei, Zou Zixing, Li Bingzhou, Liu Sian, Kang Jin, Kong Jungho, Li Ting, Wong Io Nam, Huang Xiaoying, Chen Gang, Lu Wenyang, Ziyar Ian, Zhang Charlotte L, Sun Yiwen, Lin Weihong, Ou Caiwen, Fok Manson, Hou Taiwa, Wang Winston, Xue Kanmin, Yin Yun, Zhu Hao, Gootenberg Jonathan, Abudayyeh Omar O, Karin Michael, Loupy Alexandre, Rasko John E J, Ideker Trey, Luo Huiyan, Oermann Eric, Zhang Kang, International Consortium of Digital Twins in Healthcare and Medicine
Аннотация
Cancer management remains fragmented across its continuum, from late-stage diagnosis and salvage therapies to non-personalized surveillance. Here, we present Oncoformer, a unified multimodal transformer model trained on the China Oncology Multimodal Prediction and Surveillance Study (COMPASS) cohort (3.67 million individuals, 17.7 million clinical visits) and validated on independent external cohorts, including the UK Biobank. Oncoformer integrates longitudinal electronic health records with chest X-ray imaging to address multiple clinical tasks: pan-cancer diagnosis (area under the receiver operating characteristic curve [AUROC] = 0.956), future cancer prediction up to 1 year before diagnosis (AUROC = 0.869), tumor stage inference (mean AUROC > 0.90), patient-specific treatment-response forecasting, and recurrence-free survival stratification across ten cancer types (all p < 0.01). Staging predictions were independently validated against postoperative pathological endpoints and shown to converge on core cancer genomic pathways. By translating routine clinical data into a dynamic view of cancer evolution, Oncoformer provides a framework for risk-informed cancer prediction and treatment stratification using routine clinical data.