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

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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: EGFR Activating Mutation; Adenocarcinoma Lung; Lung Cancer (NSCLC); Postoperative Adjuvant Therapy
• Intervention / Treatment: Diagnostic test: Comprehensive analysis through laboratory tests, imaging techniques, and clinical data

Detailed Description

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 (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Xiaorong Dong, Dr; Email: xiaorongdong@hust.edu.cn
• Study Contact Backup: Name: Na Li, Dr; Phone Number: 02785726114; Email: ln19931020@126.com

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, 430022
      • Wuhan Union Hospital
      • Contact: Na Li, Dr; Phone Number: 02785726114; Email: ln19931020@126.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Retrospectively included data from 1000 patients diagnosed with stage IB-IIIA invasive lung adenocarcinoma with EGFR mutations who underwent radical surgery from January 2021 to December 2024.

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
DFS	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。	two years

Collaborators and Investigators

• Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
• Investigators: Principal Investigator: Xiaorong Dong, Dr, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Publications and helpful links

General Publications

• Vaidya P, Bera K, Gupta A, Wang X, Corredor G, Fu P, Beig N, Prasanna P, Patil PD, Velu PD, Rajiah P, Gilkeson R, Feldman MD, Choi H, Velcheti V, Madabhushi A. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction. Lancet Digit Health. 2020 Mar;2(3):e116-e128. doi: 10.1016/S2589-7500(20)30002-9. Epub 2020 Feb 13.
• Chen M, Lu H, Copley SJ, Han Y, Logan A, Viola P, Cortellini A, Pinato DJ, Power D, Aboagye EO. A Novel Radiogenomics Biomarker for Predicting Treatment Response and Pneumotoxicity From Programmed Cell Death Protein or Ligand-1 Inhibition Immunotherapy in NSCLC. J Thorac Oncol. 2023 Jun;18(6):718-730. doi: 10.1016/j.jtho.2023.01.089. Epub 2023 Feb 10.
• Lin H, Hua J, Gong Z, Chen M, Qiu B, Wu Y, He W, Wang Y, Feng Z, Liang Y, Long W, Li R, Kuang Q, Chen Y, Lu J, Luo S, Zhao W, Yan L, Chen X, Shi Z, Xu Z, Mo Z, Liu E, Han C, Cui Y, Yang X, Chen X, Liu J, Pan X, Madabhushi A, Lu C, Liu Z. Multimodal radiopathological integration for prognosis and prediction of adjuvant chemotherapy benefit in resectable lung adenocarcinoma: A multicentre study. Cancer Lett. 2025 Apr 28;616:217557. doi: 10.1016/j.canlet.2025.217557. Epub 2025 Feb 13.

Study record dates

Study Major Dates

• Study Start (Estimated): December 25, 2025
• Primary Completion (Estimated): July 1, 2027
• Study Completion (Estimated): August 1, 2027

Study Registration Dates

• First Submitted: November 24, 2025
• First Submitted That Met QC Criteria: December 16, 2025
• First Posted (Actual): December 17, 2025

Study Record Updates

• Last Update Posted (Actual): December 17, 2025
• Last Update Submitted That Met QC Criteria: December 16, 2025
• Last Verified: October 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Neoplasms by Histologic Type; Lung Diseases; Neoplasms, Glandular and Epithelial; Adenocarcinoma; Respiratory Tract Neoplasms; Thoracic Neoplasms; Carcinoma; Carcinoma, Bronchogenic; Bronchial Neoplasms; Adenocarcinoma of Lung; Lung Neoplasms; Carcinoma, Non-Small-Cell Lung; Diagnostic Techniques and Procedures; Diagnosis; Diagnostic Imaging
• Other Study ID Numbers: AIEF20250825

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No