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

NCT07287904 | Not Yet Recruiting

• 1 other identifier: AIEF20250825
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Lung Cancer (NSCLC); EGFR Activating Mutation; Adenocarcinoma Lung; Postoperative Adjuvant Therapy

Outcome Measures

Primary Outcomes (1)

• 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

Interventions

Comprehensive analysis through laboratory tests, imaging techniques, and clinical data DIAGNOSTIC_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 Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

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

Study Sites (1)

Wuhan Union Hospital

Wuhan, Hubei, 430022, China

Location

Related Publications (3)

• 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.

  PMID: 33334576 RESULT

• 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.

  PMID: 36773776 RESULT

• 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.

  PMID: 39954935 RESULT

MeSH Terms

Conditions

Lung Neoplasms; Carcinoma, Non-Small-Cell Lung; Adenocarcinoma of Lung

Interventions

Diagnostic Imaging

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Carcinoma, Bronchogenic; Bronchial Neoplasms; Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type

Intervention Hierarchy (Ancestors)

Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Xiaorong Dong, Dr

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

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Na Li, Dr

CONTACT

02785726114; ln19931020@126.com

Xiaorong Dong, Dr

CONTACT

xiaorongdong@hust.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: November 24, 2025
• First Posted: December 17, 2025
• Study Start: December 25, 2025
• Primary Completion (Estimated): July 1, 2027
• Study Completion (Estimated): August 1, 2027
• Last Updated: December 17, 2025

Record last verified: 2025-10

Locations

CN (1)