A Multicenter Study on Deep Learning Model-Assisted Detection of Brain Metastases in MR Images.

RATIONALE AND

OBJECTIVES

This study aimed to develop and validate a deep learning-based brain metastasis detection model (BMDM) in magnetic resonance images for diagnosing brain metastases (BMs).

MATERIALS AND

METHODS

We retrospectively collected data from 950 patients serving as the training and test sets for developing BMDM and from an additional 423 patients as the validation set. Three reading modes were compared: radiologists only (10 total, four with ≤3 years of experience and six with >3 years of experience), BMDM only, and radiologists assisted by the BMDM. The alternative free-response receiver operating characteristic (AFROC) method was used for evaluation.

RESULTS

The reading time was reduced by 30.87%, AFROC-area under the curve improved from 0.837 to 0.954, and sensitivity increased from 0.685 to 0.916 with BMDM assistance. The improvement in sensitivity was more pronounced among less experienced radiologists (24.59% vs 22.03%). The detection sensitivity improved by 33.45% for lesions ≤3 mm and by 43.00% for insular lesions.

CONCLUSION

The results demonstrated that BMDM significantly enhanced time efficiency and diagnostic performance for BM detection, providing clinical benefits.