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

The growing population of cancer survivors faces immense monitoring burdens because of rigid follow-up guidelines, such as the intensive surveillance schedules recommended by the National Comprehensive Cancer Network. 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 (radiation therapy dose maps) to guide individualized care.

Using stage II nasopharyngeal carcinoma (N = 2148 across 5 centers) as a model, we first implemented a target trial emulation framework to confirm the safety of treatment deintensification 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 with concurrent chemoradiation therapy across all cohorts, establishing a safely deintensified 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 6 visits for high-risk individuals over a 5-year period, demonstrating a high sensitivity for detecting true failures.

This generalizable AI framework can seamlessly complement the current National Comprehensive Cancer Network guidelines, offering a transformative, data-driven solution that reduces the global monitoring burden of cancer survivorship care.