Machine learning-based models in prediction of the radiological outcomes of vestibular schwannoma following stereotactic radiosurgery: a systematic review and meta-analysis.

IF 2.2 3区 医学 Q3 CLINICAL NEUROLOGY
Bardia Hajikarimloo, Mohammad Ali Nazari, Mohammad Amin Habibi, Pourya Taghipour, Seyyed-Ali Alaei, Amirreza Khalaji, Rana Hashemi, Ibrahim Mohammadzadeh, Salem M Tos
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引用次数: 0

Abstract

Background: Prediction of the radiological outcomes of the vestibular schwannomas (VSs) following stereotactic radiosurgery (SRS) is critical in the management of these lesions. Predictions of tumor control can optimize therapeutic strategies and enhance treatment outcomes. Significant advancements in machine learning (ML) have led to the development of models to predict the radiological outcomes after SRS in VS individuals. This study evaluated the role of ML-based models in predicting the radiological outcomes of SRS in the setting of VS.

Methods: On December 12, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated the performance outcomes of the ML-based predictive models were included. The pooled sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratio (DOR) were calculated through the R program. The hierarchical summary receiver operating characteristic (HSROC) model was utilized to form a summary ROC (SROC) curve.

Results: Nine studies with 1095 patients were included. Most of the best performance models were ML-based (88.9 8/9). The most frequent algorithm was the support vector machine (SVM) (44.4%, 4/9). The meta-analysis revealed a pooled sensitivity rate of 86% (95%CI: 83-89%), a specificity rate of 78% (95%CI: 62-89%), and a DOR of 19.8 (95%CI: 9.12-42.9). The SROC curve exhibited an AUC of 0.845 for tumor response prediction.

Conclusion: Clinical application of ML-based predictive models can optimize the therapeutic strategy and enhance the outcomes for patients with VS.

基于机器学习的模型预测立体定向放射手术后前庭神经鞘瘤的放射预后:系统回顾和荟萃分析。
背景:立体定向放射治疗(SRS)后前庭神经鞘瘤(VSs)的放射学预后预测对这些病变的治疗至关重要。肿瘤控制的预测可以优化治疗策略,提高治疗效果。机器学习(ML)的重大进步导致了预测VS个体SRS后放射学结果的模型的发展。方法:于2024年12月12日,系统检索Pubmed、Embase、Scopus和Web of Science 4个电子数据库。包括评估基于ml的预测模型的性能结果的研究。通过R程序计算合并敏感性、特异性、曲线下面积(AUC)和诊断优势比(DOR)。采用分层汇总接收者工作特征(HSROC)模型,形成汇总ROC曲线。结果:纳入9项研究,共1095例患者。大多数性能最好的模型是基于ml的模型(88.9 /9)。最常用的算法是支持向量机(SVM)(44.4%, 4/9)。荟萃分析显示,合并敏感性为86% (95%CI: 83-89%),特异性为78% (95%CI: 62-89%), DOR为19.8 (95%CI: 9.12-42.9)。SROC曲线预测肿瘤反应的AUC为0.845。结论:临床应用基于ml的预测模型可以优化VS患者的治疗策略,提高预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Neurology
BMC Neurology 医学-临床神经学
CiteScore
4.20
自引率
0.00%
发文量
428
审稿时长
3-8 weeks
期刊介绍: BMC Neurology is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of neurological disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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