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RecruitingNCT07668037

Characterization of Multi-Omics Landscapes and AI Pathological Prediction Model for Long-Term Survival in NSCLC Immunotherapy

Characterization of Multi-Omics Landscapes in Long-Term Survival Following Immunotherapy and Development of an AI Pathological Prediction Model for Long-Term Survival Based on H&E-Stained Images in Advanced Non-Small Cell Lung Cancer

Sponsor

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Enrollment

600 participants

Start Date

May 1, 2026

Study Type

OBSERVATIONAL

Conditions

Non-Small Cell Carcinoma of LungImmunotherapyAdvanced Non-Small Cell Lung Cancer

Summary

This study is a retrospective, multicenter, observational cohort study in patients with advanced or locally advanced non-small cell lung cancer (NSCLC). The aim of this study was to establish a long-term survival (LTS) versus short-term survival (STS) real-world cohort, to systematically characterize the multi-omics landscapes, and to develop and validate an artificial intelligence (AI) pathological prediction model based on routine H\&E-stained images for predicting immune microenvironment features and long-term survival outcomes following immunotherapy.

Eligibility

Min Age: 18 Years

Inclusion Criteria4

  • Patients with pathologically confirmed advanced or locally advanced non-small cell lung cancer (NSCLC).
  • Patients derived from real-world data of multiple centers (including Cancer Hospital, Chinese Academy of Medical Sciences; Cancer Hospital of Shanxi, Chinese Academy of Medical Sciences \[Shanxi Cancer Hospital\]; and other participating centers) or from completed phase III clinical trials (e.g., Choice-01, Rationale-307, Rationale-304).
  • Patients who received first-line or later-line immune checkpoint inhibitor (ICI) monotherapy or ICI-based combination therapy.
  • Patients with complete clinical information and available follow-up data.

Exclusion Criteria2

  • Patients whose systemic therapy did not include an immunotherapy regimen.
  • Patients lost to follow-up.

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Locations(1)

Cancer Hospital Chinese Academy of Medical Sciences

Beijing, China

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NCT07668037

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