The implementation of artificial intelligence in serial monitoring of post gamma knife vestibular schwannomas: A pilot study.
Authors
Affiliations (10)
Affiliations (10)
- University of Sheffield, Medical School, S10 2RX, United Kingdom. Electronic address: [email protected].
- University of Sheffield, Medical School, S10 2RX, United Kingdom. Electronic address: [email protected].
- Yale University, School of Engineering and Applied Science, New Haven, CT, USA. Electronic address: [email protected].
- Department of Mechanical Engineering, Yale University, New Haven, CT, USA. Electronic address: [email protected].
- Department of Surgery, Yale School of Medicine, USA. Electronic address: [email protected].
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA. Electronic address: [email protected].
- Department of Surgery, Yale School of Medicine, USA. Electronic address: [email protected].
- Department of Mechanical Engineering, Yale University, New Haven, CT, USA. Electronic address: [email protected].
- Department of Orthopedics, Yale School of Medicine, New Haven, CT, USA. Electronic address: [email protected].
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA. Electronic address: [email protected].
Abstract
Vestibular schwannomas (VS) are benign tumors that can lead to hearing loss, balance issues, and tinnitus. Gamma Knife Radiosurgery (GKS) is a common treatment for VS, aimed at halting tumor growth and preserving neurological function. Accurate monitoring of VS volume before and after GKS is essential for assessing treatment efficacy. To evaluate the accuracy of an artificial intelligence (AI) algorithm, originally developed to identify NF2-SWN-related VS, in segmenting non-NF2-SWN-related VS and detecting volume changes pre- and post-GKS. We hypothesize this AI algorithm, trained on NF2-SWN-related VS data, will accurately apply to non-NF2-SWN VS and VS treated with GKS. In this retrospective cohort study, we reviewed data from an established Gamma Knife database, identifying 16 patients who underwent GKS for VS and had pre- and post-GKS scans. Contrast-enhanced T1-weighted MRI scans were analyzed with both manual segmentation and the AI algorithm. DICE similarity coefficients were computed to compare AI and manual segmentations, and a paired t-test was used to assess statistical significance. Volume changes for pre- and post-GKS scans were calculated for both segmentation methods. The mean DICE score between AI and manual segmentations was 0.91 (range 0.79-0.97). Pre- and post-GKS DICE scores were 0.91 (range 0.79-0.97) and 0.92 (range 0.81-0.97), indicating high spatial overlap. AI-segmented VS volumes pre- and post-GKS were consistent with manual measurements, with high DICE scores indicating strong spatial overlap. The AI algorithm processed scans within 5 min, suggesting it offers a reliable, efficient alternative for clinical monitoring. DICE scores showed high similarity between manual and AI segmentations. The pre- and post-GKS VS volume percentage changes were also similar between manual and AI-segmented VS volumes, indicating that our AI algorithm can accurately detect changes in tumor growth.