Retrospective Multicenter Study of Patient-level T1CE/FLAIR MRI Deep Learning to Predict EGFR/ALK Driver Status in NSCLC Brain Metastases With External Validation and Survival Analysis
NCT07373951 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 380
Last updated 2026-01-29
Summary
This retrospective multicenter observational study aims to develop and externally validate a noninvasive deep learning model based on routine brain MRI to identify actionable driver alterations in patients with non-small cell lung cancer (NSCLC) brain metastases. The model uses contrast-enhanced T1-weighted imaging (T1CE) and FLAIR sequences to classify patients as driver-positive (EGFR mutation and/or ALK rearrangement/fusion) versus driver-negative (EGFR-negative and ALK-negative), using brain metastasis tissue next-generation sequencing as the reference standard. The development and internal validation cohorts are from the National Cancer Center (China). Two independent external test cohorts are used: one from the First Affiliated Hospital of Anhui Medical University (China) and one from a public de-identified dataset hosted by The Cancer Imaging Archive (TCIA). The primary endpoint is the patient-level area under the receiver operating characteristic curve (AUC) in the external test cohorts. Secondary analyses include model calibration and decision-curve analysis to estimate clinical utility, comparisons of 2D/2.5D/3D modeling strategies and multimodal fusion approaches, and exploratory associations between model outputs and overall survival (OS) and progression-free survival (PFS), calculated from the date of brain metastasis surgery to the event or last follow-up (data cutoff: May 1, 2026).
Conditions
Sponsors & Collaborators
-
Ming Yang
lead OTHER
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2025-11-01
- Primary Completion
- 2026-04-01
- Completion
- 2026-05-01
Countries
- China
Study Locations
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