Studies lung cancer tumor features and an AI tool to predict long survival
Looks at past immunotherapy patients with advanced lung cancer to build an AI tool.
Key facts
- Who runs it
- Cancer Institute and Hospital, Chinese Academy of Medical Sciences
- Where
- Beijing, ChinaSee the site
- Trial code
- Registry title
- Characterization of Multi-Omics Landscapes and AI Pathological Prediction Model for Long-Term Survival in NSCLC Immunotherapy
- Status
- Recruiting
- People taking part
- 600 people, planned
- Started
- 1 May 2026
- Expected to end
- 1 May 2030
- Type of study
- Observational, 2 groups
- Record updated
- 25 June 2026
Who can take part
AI summary
From the trial's eligibility criteria. Machine-read from the registry text on this page, so it can be wrong. The registry text below and the trial team decide who can take part.
The study looks at adults with advanced or locally advanced non-small cell lung cancer who had immunotherapy. It uses past records and tissue images.
- Age
- 18 years or older
- Sex
- All sexes can join
- Disease stage
- Advanced or locally advanced non-small cell lung cancer (NSCLC), confirmed by pathology
- Earlier treatment
- Had immune checkpoint inhibitor (ICI) treatment alone or in combination, as first-line or later-line treatment
Main reasons for not taking part
- Systemic therapy that did not include immunotherapy
- Lost to follow-up
- Incomplete clinical information or no follow-up data
The registry criteria, word for word
Show all 6 criteriaShow less
Who can take part
- 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
Who cannot take part
- Patients whose systemic therapy did not include an immunotherapy regimen
- Patients lost to follow-up
Where in China
- Cancer Hospital, Chinese Academy of Medical SciencesBeijing, ChinaContact: Jie Wang, MD, PhDTaking people
About the trial
What the trial wants to find out
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.
The 2 groups in the trial
- Long-term survival (LTS) group
patients with overall survival of more than 5 years after initial immunotherapy
- Short-term survival (STS) group
patients with overall survival of less than 1 year after initial immunotherapy
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Contact the study team: see the registry record → Contacts and Locations
Print a page for my doctorData and sources
- Registry record
- Listed on ClinicalTrials.gov.
- 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
- Plain-language title
- Studies lung cancer tumor features and an AI tool to predict long survivalGenerated from the registry record. The registry title is the official one.
- AI summary
- From the trial's eligibility criteria. Read by machine on 12 November 2026.
- Record updated
- 25 June 2026