Artificial-intelligence-driven measurements of brain metastases’ response to SRS compare favorably with current manual standards of assessment

Author:

Prezelski Kayla12ORCID,Hsu Dylan G1,del Balzo Luke34,Heller Erica1,Ma Jennifer4,Pike Luke R G45,Ballangrud Åse1,Aristophanous Michalis1

Affiliation:

1. Department of Medical Physics, Memorial Sloan Kettering Cancer Center , New York, New York , USA

2. Saint Louis University School of Medicine , St. Louis, Missouri , USA

3. Medical College of Georgia , Athens, Georgia, USA

4. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center , New York, New York , USA

5. Biomarker Development Program, Memorial Sloan Kettering Cancer Center , New York, New York , USA

Abstract

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.

Funder

National Institutes of Health

National Cancer Institute

Publisher

Oxford University Press (OUP)

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