Clinical Research Directory

Prediction of Targeted Therapy Efficacy in EGFR-mutant Lung Cancer Patients Using AI-based Multimodal Data

Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

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.

Official title: A Retrospective Analysis Study on Predicting the Efficacy of Targeted Therapy in Lung Cancer Patients With EGFR Mutations Based on AI-driven Multimodal Data

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