A Retrospective Analysis Study on Predicting the Efficacy of Targeted Therapy in Lung Cancer Patients With EGFR Mutations Based on AI-driven Multimodal Data
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- DFS
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age 18-80 years, undergoing radical surgery for lung cancer (R0 resection);
- •Postoperative pathological stage IB-IIIA, pathology confirmed as adenocarcinoma;
- •EGFR gene testing positive, EGFR 19del/L858R mutation;
- •Receiving postoperative EGFR-TKI targeted adjuvant therapy;
- •Complete and clear preoperative imaging data, genetic testing report, and pathology report available.
排除标准
- •Patients negative for EGFR;
- •Incomplete surgical resection (R1, R2);
- •Did not receive EGFR-TKI targeted therapy after surgery;
- •Recurrent or advanced stage patients;
- •Incomplete preoperative or postoperative data;
- •Patients who died within 30 days post-surgery.
结局指标
主要结局
DFS
时间窗: 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。
次要结局
未报告次要终点
研究者
Xiaorong Dong
Professor
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology