Advancing cancer detection and treatment using longitudinal routine clinical data.
Liu F., Wang K., Xu H., Tang C., Shen X., Wang M., Yang L., Yang L., Liu L., Hu C., Li G., Wu W., Zou Z., Li B., Liu S., Kang J., Kong J., Li T., Wong IN., Huang X., Chen G., Lu W., Ziyar I., Zhang CL., Sun Y., Lin W., Ou C., Fok M., Hou T., Wang W., Xue K., Yin Y., Zhu H., Gootenberg J., Abudayyeh OO., Karin M., Loupy A., Rasko JEJ., Ideker T., Luo H., Oermann E., Zhang K., International Consortium of Digital Twins in Healthcare and Medicine .
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.