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 type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 600 (estimated) |
| Sponsor | Cancer Institute and Hospital, Chinese Academy of Medical Sciences Academic / other |
| Drugs / interventions | immunotherapy |
| Locations | 1 site (Beijing) |
| Status | Recruiting |
| Started | May 1, 2026 |
| Main results expected | May 1, 2030 |
| Study ends | May 1, 2030 |
| Registry record updated | June 25, 2026 |
| Trial ID | NCT07668037 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
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.