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

N/ANot Yet RecruitingNCT07287904

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology1,000 enrolled

Overview

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.

This study is an observational study, aiming to retrospectively include data from 500 patients diagnosed with stage IB-IIIA invasive lung adenocarcinoma who underwent radical surgery at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, from January 2021 to December 2024, along with data from a total of 1,000 patients from other multi-center sites. The study will collect and record information on subjects' demographics, pathology, imaging, genetic testing, and clinical characteristics via the hospital's electronic medical record system. Patient survival status will be obtained through telephone follow-ups and home visits. Radiomic features of the tumor and peritumoral regions will be extracted from preoperative CT images, H\&E-stained digital whole-slide pathology images, and genetic testing reports. These will be combined with multi-dimensional pathological features and gene expression distribution characteristics from the patient cases to construct a multi-omics model integrating imaging, pathology, demographics, and genetics, providing a more precise prognostic assessment for targeted therapy in lung cancer patients.

Study Type

OBSERVATIONAL

Enrollment

1,000

Conditions

Lung Cancer (NSCLC)

Interventions

Comprehensive analysis through laboratory tests, imaging techniques, and clinical dataDIAGNOSTIC_TEST

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.

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Age 18-80 years, undergoing radical surgery for lung cancer (R0 resection); 2. Postoperative pathological stage IB-IIIA, pathology confirmed as adenocarcinoma; 3. EGFR gene testing positive, EGFR 19del/L858R mutation; 4. Receiving postoperative EGFR-TKI targeted adjuvant therapy; 5. Complete and clear preoperative imaging data, genetic testing report, and pathology report available. Exclusion Criteria: 1. Patients negative for EGFR; 2. Incomplete surgical resection (R1, R2); 3. Did not receive EGFR-TKI targeted therapy after surgery; 4. Recurrent or advanced stage patients; 5. Incomplete preoperative or postoperative data; 6. Patients who died within 30 days post-surgery.

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

Overview

Conditions

Interventions

Eligibility

Locations (1)

Outcomes

Primary Outcomes

Central Contacts