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

Brief summary (registry, in English)

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.

What you commit to

• Study type: Observational
• Participants enrolled: 2000
• From 1 Jan 2023 to the primary completion date, 1 Feb 2026
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Age in the registry: 18 years to - · Sex: All · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (1)

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