Early experience with an artificial intelligence-based module for brain metastasis detection and segmentation

• Published in: Journal of neuro-oncology 2025, Vol.171 (2), p.365-372
• Main Authors: Madhugiri, Venkatesh S., Prasad, Dheerendra
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - secondary; Brain Neoplasms - surgery; Early experience; Female; Humans; Image Processing, Computer-Assisted - methods; Lesions; Magnetic Resonance Imaging - methods; Male; Medicine; Medicine & Public Health; Metastases; Metastasis; Middle Aged; Neurology; Oncology; Radiosurgery; Radiosurgery - methods; Segmentation; Volumetric analysis
• ISSN: 0167-594X; 1573-7373; 1573-7373
• DOI: 10.1007/s11060-024-04851-8

Introduction – Accurate detection, segmentation, and volumetric analysis of brain lesions are essential in neuro-oncology. Artificial intelligence (AI)-based models have improved the efficiency of these processes. This study evaluated an AI-based module for detecting and segmenting brain metastases, comparing it with manual detection and segmentation. Methods – MRIs from 51 patients treated with Gamma Knife radiosurgery for brain metastases were analyzed. Manual lesion identification and contouring on Leksell Gamma Plan at the time of treatment served as the gold standard. The same MRIs were processed through an AI-based module (Brainlab Smart Brush), and lesion detection and volumes were compared. Discrepancies were analyzed to identify possible sources of error. Results – Among 51 patients, 359 brain metastases were identified. The AI module achieved a sensitivity of 79.2% and a positive predictive value of 95.6%, compared to a 93.3% sensitivity for manual detection. However, for lesions > 0.1 cc, the AI’s sensitivity rose to 97.5%, surpassing manual detection at 93%. Volumetric agreement between AI and manual segmentations was high (Spearman’s ρ = 0.997, p