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Multimodal AI Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
Sponsor: Central South University
Summary
The goal of this observational study is to develop and validate an artificial intelligence (AI)-based multimodal radiomics model that integrates preoperative CT imaging features and clinical data to predict pathological high-risk features in patients with lung cancer associated with cystic airspaces (LCCA). The main questions it aims to answer are: Can an AI-based multimodal radiomics model accurately predict pathological high-risk features in LCCA before surgery? Does the integration of CT imaging features and clinical variables improve preoperative risk stratification compared with imaging or clinical information alone?
Official title: Interpretable Multimodal Artificial Intelligence Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
600
Start Date
2025-05-01
Completion Date
2026-12-31
Last Updated
2026-09-15
Healthy Volunteers
No
Locations (1)
The Second Xiangya Hospital of Central South University
Changsha, Hunan, China