NCT07463300

A Hierarchical Multi-modal AI Framework for Pathological and Genetic Subtyping of Lung Cancer Based on PET/CT Imaging

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 5,500 (estimated)
• Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

PET/CT imaging and clinical information (age, gender, smoking history, family history of cancer, history of present illness, and several tumor biomarkers, etc.) were used to establish a hierarchical multi-modal AI framework for pathological and genetic subtyping of lung cancer

Detailed description

The multi-modal AI framework is developed to facilitate a hierarchical and precise stratification process. The first level involves the accurate differentiation between small cell lung cancer and non-small cell lung cancer (NSCLC) in patients diagnosed with lung cancer. The second level entails the further categorization of NSCLC patients into adenocarcinoma, squamous cell carcinoma, and other less prevalent subtypes. The third level involves predicting the mutation status of the EGFR driver gene, which is most-commonly observed in patients with lung adenocarcinoma. The whole cohort was divided into the training cohort (retrospective), validation cohort (retrospective), test cohort (retrospective), and prospective cohort.

Conditions

• Lung Cancer

Interventions

Type	Name	Description
OTHER	PET imaging analysis, data mining, and AI model developing	PET imaging analysis, data mining, and AI model developing

Timeline

• Start date: 2024-08-01
• Primary completion: 2027-08-01
• Completion: 2027-08-01
• First posted: 2026-03-11
• Last updated: 2026-03-11

Locations

9 sites across 1 country: China