Clinical trial Recruiting at 1 site Observational Ages 18 and up

Long-term survival and AI-based prediction in NSCLC patients treated with immunotherapy

Official title: 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

Run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences · NCT07668037 · Data updated 2026-11-12

This study tests whether AI analysis of routine pathology images, along with multi-omics data, can predict long-term survival for people with advanced NSCLC treated with immunotherapy.

Quick facts

Study typeObservational
Ages18 Years and up
SexAll
Enrollment600 (estimated)
SponsorCancer Institute and Hospital, Chinese Academy of Medical Sciences
Academic / other
Drugs / interventionsimmunotherapy
Locations1 site (Beijing)
StatusRecruiting
StartedMay 1, 2026
Main results expectedMay 1, 2030
Study endsMay 1, 2030
Registry record updatedJune 25, 2026
Trial IDNCT07668037 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Adults with pathologically confirmed advanced or locally advanced NSCLC who received first-line or later-line ICI therapy and have complete clinical and follow-up data from real-world sources or prior trials.

Probably not a fit

Patients whose data are incomplete or who did not receive immunotherapy would not be expected to benefit from the predictive model.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This is a retrospective, multicenter observational study of patients with advanced or locally advanced NSCLC who received checkpoint inhibitors alone or with combinations. The goal is to define real-world long-term vs short-term survival cohorts and to characterize multi-omics landscapes. The study also aims to develop and validate an AI pathologic prediction model using standard H&E images to forecast immune features and long-term outcomes after immunotherapy. Data will come from multiple centers and, when applicable, completed phase III trials. Analysis will focus on patients with complete clinical information and adequate follow-up.

Why it matters

Potential benefit: If successful, the model could help identify which patients are more likely to achieve long-term benefit from immunotherapy.

How similar studies have gone: Previous studies have explored AI and multi-omics in oncology and have shown promise in predicting outcomes, though this exact approach in NSCLC with long-term survival prediction is an active area with variable results.

Who can join: the full rules

Show full inclusion / exclusion criteria
Inclusion Criteria:

* 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 Criteria:

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

Where it's running

  • Cancer Hospital Chinese Academy of Medical Sciences
    Beijing, China
    Recruiting

Study contacts

  • Principal investigator: Jie Wang, MD, PhD — Cancer Institute and Hospital, Chinese Academy of Medical Sciences
  • Study coordinator: Jie Wang, MD,PhD
  • Email: zlhuxi@163.com
  • Phone: 8610-87788029

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.