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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07668037

Enrollment

600

Registered

2026-06-25

Start date

2026-05-01

Completion date

2030-05-01

Last updated

2026-06-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Advanced Non-Small Cell Lung Cancer, Immunotherapy, Non-Small Cell Carcinoma of Lung

Brief 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
OS	From date of first ICI-based therapy initiation to death due to any cause, or censored at the date of last known follow-up, assessed up to 5 years (retrospectively collected from medical records).	Overall Survival

Countries

China

Contacts

CONTACTJie Wang, MD,PhD

zlhuxi@163.com8610-87788029

CONTACTJie Zhao, MD

zhaojie_12@163.com17801204737

PRINCIPAL_INVESTIGATORJie Wang, MD, PhD

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Outcome results