Clinical imaging · 2025
The implementation of artificial intelligence in serial monitoring of post gamma knife vestibular schwannomas: A pilot study.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Singh Manwi, Jester Noemi, Lorr Samantha, Briano Alexis, Schwartz Nofrat, Mahajan Amit, Chiang Veronica, Tommasini Steven M, Wiznia Daniel H, Buono Frank D
Аннотация
BACKGROUND: 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.
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
CLINICAL IMPORTANCE: 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.
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
CLINICAL IMPORTANCE: 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.