Objective:

To evaluate whether deep learning-based detection and quantification of brain metastases (BMs) can assist in providing treatment options for patients with BMs, thereby improving clinical outcomes.

Key Findings:

• Deep learning systems demonstrated high sensitivity (82.4%) in detecting BMs smaller than 3 mm with a low false-positive rate (0.59 per patient), indicating potential for clinical application.
• The study established a logical framework for treatment suggestions based on DLS outputs, categorizing patients into treatment groups, which could enhance treatment planning.

Interpretation:

The findings suggest that deep learning-based quantification of BMs can enhance clinical decision-making and treatment planning for patients with brain metastases.

Limitations:

• The study was retrospective, which may introduce bias.
• The sample size was limited to a single institution, affecting generalizability.
• The performance of the deep learning model requires further validation in diverse clinical settings.

Conclusion:

Deep learning-based detection and quantification of brain metastases using black-blood imaging can potentially improve treatment suggestions and patient management, highlighting the need for further research in clinical settings.