Clinical Research Directory

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

Sponsor: Ming Yang

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).

Official title: Retrospective Multicenter Study: Patient-level Noninvasive Prediction of Non-small Cell Lung Cancer Brain Metastases Based on T1CE and FLAIR Multimodal MRI Deep Learning Models, With Targeted Drivers (EGFR or ALK), External Validation, and Survival Translation Assessment.

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