Development and validation of a deep learning model using MR imaging for predicting brain metastases: an accuracy-focused study.

ABSTRACT:

Background

Brain metastases (BM), originating from extracranial malignancies, significantly threaten patient health. Accurate BM identification is crucial but labor-intensive manually. This study developed and validated a system for BM diagnosis, assessing its performance and stability.

Methods

470 patients diagnosed with BM were divided into an 80% training set (n=379) and a 20% internal test set (n=91) using systematic sampling. An additional 172 patients were retrospectively enrolled for external validation. A comprehensive preprocessing pipeline was implemented. We developed a 3D U-Net model with a ResNet-34 backbone for BM prediction. MRI scans were resampled to 0.833 mm³ isotropic voxels, underwent skull stripping using SynthStrip, and were intensity-normalized via Z-s

SUBMITTER: Shi D 

PROVIDER: S-EPMC12500715 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature