Artificial-intelligence-driven measurements of brain metastases’ response to SRS compare favorably with current manual standards of assessment Journal Article
| Authors: | Prezelski, K.; Hsu, D. G.; del Balzo, L.; Heller, E.; Ma, J.; Pike, L. R. G.; Ballangrud, Å; Aristophanous, M. |
| Article Title: | Artificial-intelligence-driven measurements of brain metastases’ response to SRS compare favorably with current manual standards of assessment |
| Abstract: | Background. Evaluation of treatment response for brain metastases (BMs) following stereotactic radiosurgery (SRS) becomes complex as the number of treated BMs increases.This study uses artificial intelligence (AI) to track BMs after SRS and validates its output compared with manual measurements. Methods . Patients with BMs who received at least one course of SRS and followed up with MRI scans were retrospectively identified. A tool for automated detection, segmentation, and tracking of intracranial metastases on longitudinal imaging, MEtastasis Tracking with Repeated Observations (METRO), was applied to the dataset. The longest three-dimensional (3D) diameter identified with METRO was compared with manual measurements of maximum axial BM diameter, and their correlation was analyzed. Change in size of the measured BM identified with METRO after SRS treatment was used to classify BMs as responding, or not responding, to treatment, and its accuracy was determined relative to manual measurements. Results . From 71 patients, 176 BMs were identified and measured with METRO and manual methods. Based on a one-to-one correlation analysis, the correlation coefficient was R2 = 0.76 (P = .0001). Using modified BM response classifications of BM change in size, the longest 3D diameter data identified with METRO had a sensitivity of 0.72 and a specificity of 0.95 in identifying lesions that responded to SRS, when using manual axial diameter measurements as the ground truth. Conclusions . Using AI to automatically measure and track BM volumes following SRS treatment, this study showed a strong correlation between AI-driven measurements and the current clinically used method: manual axial diameter measurements. © The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. |
| Keywords: | imaging; radiation therapy; brain metastases; deep learning; longitudinal tumor tracking |
| Journal Title: | Neuro-Oncology Advances |
| Volume: | 6 |
| Issue: | 1 |
| ISSN: | 2632-2498 |
| Publisher: | Oxford University Press |
| Date Published: | 2024-01-01 |
| Start Page: | vdae015 |
| Language: | English |
| DOI: | 10.1093/noajnl/vdae015 |
| PROVIDER: | scopus |
| PMCID: | PMC10924534 |
| PUBMED: | 38464949 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF. Corresponding MSK author is Dylan G. Hsu -- Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
Related MSK Work