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Multimodal CT-Based Risk Stratification for Postoperative Local Recurrence in NSCLC: A Multicenter Study

NCT07552532 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2026-04-27

No results posted yet for this study

Summary

This multicenter retrospective study is designed to develop and validate a CT-based multimodal risk stratification approach for postoperative local recurrence after curative-intent resection of non-small cell lung cancer (NSCLC). The approach integrates clinicopathologic variables, intratumoral and peritumoral radiomics, tumor-based 2.5D deep learning features, whole-lung deep learning features, and operative text features to capture complementary information related to tumor phenotype, pulmonary background, and surgical findings. Predictive performance and clinical utility will be evaluated in internal and external validation cohorts using the concordance index, time-dependent area under the receiver operating characteristic curve, decision curve analysis, and risk reclassification analyses. The objective of this study is to assess whether multimodal CT-based risk stratification may improve postoperative risk assessment and support individualized surveillance and management strategies.

Conditions

  • Lung Cancer (NSCLC)

Sponsors & Collaborators

  • Guangming Lu

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-01-01
Primary Completion
2026-02-01
Completion
2026-06-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07552532 on ClinicalTrials.gov