Prediction of Targeted Therapy Efficacy in EGFR-mutant Lung Cancer Patients Using AI-based Multim… (NCT07287904) | Clinical Trial Compass
Not Yet RecruitingNot Applicable
Prediction of Targeted Therapy Efficacy in EGFR-mutant Lung Cancer Patients Using AI-based Multimodal Data
China1,000 participantsStarted 2025-12-25
Plain-language summary
The main purpose of this study is to explore the value of multimodal imaging information and models in predicting the prognosis of EGFR-positive non-small cell lung cancer patients undergoing targeted therapy, providing a basis for selecting suitable populations for precise tumor treatment and corresponding therapy. We retrospectively analyzed patient case data, extracted preoperative CT images, H\&E-stained whole-slide digital pathology images, and pre- or postoperative genetic testing reports to extract radiomic features of tumor and peritumoral regions. These features were combined with multidimensional pathological features and gene expression distribution characteristics to construct a multimodal radiopathogenomic model, offering more precise prognostic evaluation for lung cancer patients receiving targeted therapy.
Who can participate
Age range18 Years – 80 Years
SexALL
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Inclusion criteria
✓. Age 18-80 years, undergoing radical surgery for lung cancer (R0 resection);
✓. Postoperative pathological stage IB-IIIA, pathology confirmed as adenocarcinoma;