| Title: | Advancing cancer detection and treatment using longitudinal routine clinical data |
| Journal: | Cell |
| Published: | 27 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42508404/ |
| DOI: | https://doi.org/10.1016/j.cell.2026.07.009 |
Publication 21360
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Abstract
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.</p>
43 Authors
- Fei Liu
- Kai Wang
- Hui Xu
- Cheng Tang
- Xian Shen
- Meihao Wang
- Lei Yang
- Li Yang
- Li Liu
- Changxi Hu
- Li
- Wei Wu
- Zixing Zou
- Bingzhou Li
- Sian Liu
- Jin Kang
- Jungho Kong
- Ting Li
- Io Nam Wong
- Xiaoying Huang
- Gang Chen
- Wenyang Lu
- Ian Ziyar
- Charlotte L. Zhang
- Yiwen Sun
- Weihong Lin
- Caiwen Ou
- Manson Fok
- Taiwa Hou
- Winston Wang
- Kanmin Xue
- Yun Yin
- Hao Zhu
- Jonathan Gootenberg
- Omar O. Abudayyeh
- Michael Karin
- Alexandre Loupy
- John E.J. Rasko
- Trey Ideker
- Huiyan Luo
- Eric Oermann
- Kang Zhang
- International Consortium of Digital Twins in Healthcare and Medicine
Enabling scientific discoveries that improve human health