Multimodal Artificial Intelligence System for Risk-Adapted Cancer Survivorship Surveillance: A Multicenter Target Trial Emulation.
Authors
Affiliations (9)
Affiliations (9)
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China.
- Department of Radiation Oncology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat‑sen Memorial Hospital, Sun Yat‑sen University, Guangzhou, Guangdong, China.
- Department of Head and Neck Oncology, The Cancer Center of the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China. Electronic address: [email protected].
- Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, Guangdong, China.
- Department of Head and Neck Oncology, Foshan Key Laboratory of Precision Therapy in Oncology and Neurology, The First People's Hospital of Foshan (The Affiliated Foshan Hospital of Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, Foshan, Guangdong, China.
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China.
- Department of Breast Oncology, Foshan Key Laboratory of Precision Therapy in Oncology and Neurology, The First People's Hospital of Foshan (The Affiliated Foshan Hospital of Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, Foshan, Guangdong, China.
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China. Electronic address: [email protected].
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.