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

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.

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-score normalization. The model was trained on MRI scans paired with segmentation masks, utilizing ImageNet-pretrained encoder weights and a patch-based strategy (128×128×128 voxels).

The model maintained perfect specificity and AUCs across gender and age groups, with no significant differences in other metrics, confirming false positive exclusion unaffected by demographics. By cancer type: Internal testing showed significant difference of AUC (p<0.001) between lung cancer (n=74) and other cancers (n=17). The differences of other performance metrics were not statistically significant (p>0.13), though other cancers showed higher median F1/IoU/MCC. External validation showed other cancers (n=79) had significantly higher precision than lung cancer (n=93) (p<0.05). Lung cancer AUC (0.82) was significantly lower than other cancers (0.89) (p<0.001), suggesting need for sensitivity optimization; both maintained specificity=1.0000. Model time was significantly shorter than manual annotation (internal: 69s vs 113s; external: 66s vs 96s; both p<0.001), with high agreement.

The model demonstrated strong robustness and perfect specificity across demographics. While showing cancer type dependency (requiring improved lung cancer sensitivity), its high efficiency (40%-50% time reduction) and generalization provide a solid foundation for clinical translation.