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

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест 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.

Первичные конечные точки

• DFS [Срок оценки: two years]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 1 центр

• Wuhan Union Hospital — Ухань

Публикации

• 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 PMID 33334576
• 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
• 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. Can PMID 39954935

Идентификаторы

NCT: NCT07287904 · AIEF20250825

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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