NCT07107035

Multimodal Large Model-Driven Risk and Prognosis Assessment for Brain Metastases in Lung Cancer

Multimodal Large Model-Driven Risk and Prognosis Assessment for Brain Metastases in Lung Cancer: A Nationwide, Multicenter, Observational Study

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 20,000 (estimated)
• Sponsor: Fudan University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

The goal of this nationwide, multicenter observational study is to develop and externally validate multimodal large models that can (1) predict the risk of brain metastases and (2) estimate long-term prognosis in patients with non-small cell lung cancer (NSCLC). The main questions it aims to answer are: * Can a multimodal large model that fuses imaging, pathology, genomic, and clinical data accurately identify NSCLC patients at high risk of developing brain metastases? * Can a multimodal large model reliably forecast intracranial progression-free survival, progression-free survival, and overall survival across diverse real-world treatment settings? (ie, patients receiving distinct treatment regimens, in different treatment lines and with or without intracranial local therapies). Because this is an observational study, there are no investigational treatments; instead, researchers will compare outcomes among patients who receive standard-of-care therapies (surgery, radiotherapy, systemic therapy) to determine how well the model's predictions align with observed events. Participants will: * Allow use of their routinely collected clinical information, imaging (chest CT, brain MRI), pathology slides, and molecular test results for model training and validation * Undergo standard-of-care follow-ups * Complete optional quality-of-life questionnaires during scheduled visits

Conditions

• AI (Artificial Intelligence)
• NSCLC Brain Metastasis

Interventions

Type	Name	Description
OTHER	No Intervention: Observational Cohort	No intervention

Timeline

• Start date: 2025-09-01
• Primary completion: 2027-08-01
• Completion: 2030-07-01
• First posted: 2025-08-06
• Last updated: 2025-08-06