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Multimodal AI Predicts High-risk Pathology in Lung Cancer Associated With Cystic Airspaces

StatusRecruiting
PhaseNot specified
Started
View on ClinicalTrials.gov ↗

Record History

  1. Study First Submitted
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?
Trial Details
NCT Number NCT07820423
Lead Sponsor Central South University
Conditions
(from sponsor)		 Lung Cancer Associated With Cystic Airspaces
Enrollment 600 participants
Start Date
Primary Completion (estimated)
Study Completion (estimated)
Updated on ClinicalTrials.gov