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

Condition(s) studied

Investigational drug(s) / intervention(s)

Comprehensive analysis through laboratory tests, imaging techniques, and clinical data: Extract radiomics features of the tumor and peritumoral regions from preoperative CT images, H\&E-stained digital pathology whole-slide images, and genetic test reports, and integrate them with multidimensional pathological features and gene expression distribution characteristics to construct a radiopathogenomic multi-omics modality, providing more precise prognostic assessment for targeted therapy in lung cancer patients.

Study 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.

Eligibility

Primary outcome measure(s)

• DFS — two years
  The endpoint of this study was disease-free survival (DFS), defined as the time interval from surgery to the first recurrence or death,assessed up to 24 months。

Trial sites (1)

More Union Hospital, Tongji Medical College, Huazhong University of Science and Technology trials in China

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