Multimodal Artificial Intelligence System for Risk-Adapted Cancer Survivorship Surveillance: A Multicenter Target Trial Emulation.

Authors

Lu-Ning Zhang, Yu-Ting Wang, Xiao-Wen Lan, Ya-Nan Zhao, Jia-Ni Liu, Dan-Yang Li, Kai-Yun You, Wei-Jun Zhang, Shao-Qiang Liang, Fang-Yun Xie, Yun He, Hong-Mei Wang, Xing-Sheng Qiu, Jian-Gui Guo, Pu-Yun OuYang

Published in

International journal of radiation oncology, biology, physics. Aug 18, 2026. Epub Aug 18, 2026.

Abstract

The growing population of cancer survivors faces immense monitoring burdens due to rigid follow-up guidelines, such as the intensive surveillance schedules recommended by the National Comprehensive Cancer Network (NCCN). To address this issue, we engineered a multimodal artificial intelligence (AI)-based decision support system that integrates biological domain data (magnetic resonance imaging) and physical treatment domain data (radiotherapy dose maps) to guide individualized care.
Using stage II nasopharyngeal carcinoma (N=2,148 across five centers) as a model, we first implemented a target trial emulation framework to confirm the safety of treatment de-intensification and establish a baseline for streamlined surveillance. We then trained a Transformer architecture to predict individualized treatment failure timing and translated these predictions into a risk-adapted surveillance strategy.
In the target trial emulation, omitting concurrent chemotherapy demonstrated comparable survival outcomes to concurrent chemoradiotherapy across all cohorts, establishing a safely de-intensified clinical baseline. Subsequently, the AI system achieved high-fidelity predictions, with an area under the curve of 0.991 internally and 0.986 in the multi-institutional external validation cohort. This AI-guided strategy substantially reduced the need for follow-up visits for over 90% of failure-free patients, while recommending a maximum of only six visits for high-risk individuals over a five-year period, demonstrating a high sensitivity for detecting true failures.
This generalizable AI framework can seamlessly complement the current NCCN guidelines, offering a transformative, data-driven solution that reduces the global monitoring burden of cancer survivorship care.

PMID:
42612835
Bibliographic data and abstract were imported from PubMed on 19 Aug 2026.

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