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

Recruiting now

Conditions studied: Non-Small Cell Carcinoma of Lung, Immunotherapy, Advanced Non-Small Cell Lung Cancer

In brief

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.

Key facts

Study ID

NCT07668037

Run by

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

People needed

600

Starts

2026-05-01

Expected to finish

2030-05-01

Last updated by the study team

2026-06-25

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• 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.

You may not qualify if…

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

Where it is running

• Cancer Hospital Chinese Academy of Medical Sciences — Beijing, China (enrolling)

Full record on ClinicalTrials.gov

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