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

NCT ID: NCT07287904

Last Updated: 2025-12-17

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-12-25

Study Completion Date

2027-08-31

Brief Summary

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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.

Detailed Description

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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.

Conditions

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

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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

OTHER

Sponsor Role lead

Responsible Party

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Xiaorong Dong

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Xiaorong Dong, Dr

Role: PRINCIPAL_INVESTIGATOR

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

Locations

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Wuhan Union Hospital

Wuhan, Hubei, China

Site Status

Countries

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China

Central Contacts

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Na Li, Dr

Role: CONTACT

Phone: 02785726114

Email: [email protected]

Xiaorong Dong, Dr

Role: CONTACT

Email: [email protected]

Facility Contacts

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Na Li, Dr

Role: primary

References

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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.

Reference Type RESULT
PMID: 33334576 (View on PubMed)

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.

Reference Type RESULT
PMID: 36773776 (View on PubMed)

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.

Reference Type RESULT
PMID: 39954935 (View on PubMed)

Other Identifiers

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AIEF20250825

Identifier Type: -

Identifier Source: org_study_id