RADT-10. THE LOST METASTASES: DEEP LEARNING'S POTENTIAL IN RADIOSURGERY QUALITY ASSURANCE. (14th November 2022)

• Record Type: Journal Article
• Authors: Bang, Charmin; Chartrand, Gabriel; Pawlowski, Sophie; Emiliani, Ramon; Markel, Daniel; Bahig, Houda; Samak, Alexandre; Rajakesari, Selvan; Lavoie, Jérémi; Ducharme, Simon; Roberge, David
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 7
• Page Start: vii50
• Page End: vii51
• Publication Date: 2022-11-14
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac209.200
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Introduction: Identifying, segmenting, measuring, and following multiple brain metastases treated with radiosurgery can be time consuming and error prone. Machine learning has shown promise for automated detection and segmentation. Recently, a U-Net inspired model combining volume aware loss functions and volume aware sampling methods was trained in an industrial-academic partnership. A total of 530 clinically annotated T1 gadolinium MRIs were used. Initial validation showed a high sensitivity (91%) with an average of 0.66 false positives per MRI. The goal of the present work was to characterize those "false positives" which may represent clinically undetected metastases. METHODS: The images used for model development were clinically annotated for radiosurgery planning. Lesions had first been identified by a radiologist, second by clinicians during tumor board review, third by the treating radiation oncologist and the treating neurosurgeon (potentially after segmentation by a trainee) and finally by fellow radiation oncologists during quality assurance rounds. Despite these multiple checks, 10 patients (2%) had brain lesions considered potential clinical misses when all "false positives" were manually reviewed by a single investigator. Further detailed review including prior and subsequent imaging was used to arbitrate the nature of these lesions. RESULTS: Among the 10 cases, four were confirmed as undetected metastases: two lesions required subsequent radiosurgery and 2 patients died prior to further imaging. The six other lesions were adjudicated as true "false positives" (typically vascular). CONCLUSION: The multi-tier radiosurgery workflow at our institution left very few unidentified brain metastases (0.8%). Despite this low error rate, our AI algorithm still detected two lesions that required further treatment. Future investigations will focus on potential roles of AI in simplifying and accelerating our workflow. It also remains to be established if more undetected metastases would be seen in community settings where workflows include fewer sequential imaging reviews.