The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene parsing network (PSPNet) was trained to automatically detect and delineate the meningiomas. The results were compared to manual segmentations by evaluating the mean intersection over union (mIoU). The performance of grade classification was evaluated by accuracy. For the automated detection and segmentation of meningiomas, the mean pixel accuracy, tumor accuracy, background accuracy and mIoU were 99.68%, 81.36%, 99.88% and 81.36% for all patients; 99.52%, 84.86%, 99.93% and 84.86% for grade I meningiomas; 99.57%, 80.11%, 99.92% and 80.12% for grade II meningiomas; and 99.75%, 78.40%, 99.99% and 78.40% for grade III meningiomas, respectively. For grade classification, the accuracy values of the training and test datasets were 99.93% and 81.52% for all patients; 99.98% and 98.51% for grade I meningiomas; 99.91% and 66.67% for grade II meningiomas; and 99.88% and 73.91% for grade III meningiomas, respectively. The automated detection, segmentation and grade classification of meningiomas based on deep learning were accurate and reliable and may improve the monitoring and treatment of this frequently occurring tumor entity. Furthermore, the method could function as a useful tool for preassessment and preselection for radiologists, offering auxiliary information for clinical decision making in presurgical evaluation.

手术前脑膜瘤的体积评估和准确分级与治疗计划和预后预测高度相关。本研究旨在设计一种深度学习算法并评估检测脑膜瘤病变和分级分类的性能。总共有 5088 名经组织病理学证实的脑膜瘤患者被回顾性纳入。训练金字塔场景解析网络 (PSPNet) 以自动检测和描绘脑膜瘤。通过评估联合的平均交集 (mIoU)，将结果与手动分割进行了比较。等级分类的性能通过准确度来评价。对于脑膜瘤的自动检测和分割，所有患者的平均像素准确度、肿瘤准确度、背景准确度和mIoU分别为99.68%、81.36%、99.88%和81.36%；99.52%, I级脑膜瘤分别为84.86%、99.93%和84.86%；II级脑膜瘤为99.57%、80.11%、99.92%和80.12%；III级脑膜瘤分别为99.75%、78.40%、99.99%和78.40%。对于等级分类，所有患者的训练和测试数据集的准确率分别为 99.93% 和 81.52%；I级脑膜瘤为99.98%和98.51%；II级脑膜瘤为99.91%和66.67%；III 级脑膜瘤分别为 99.88% 和 73.91%。基于深度学习的脑膜瘤自动检测、分割和分级分类准确可靠，可能会改善对这种频繁发生的肿瘤实体的监测和治疗。此外，该方法可以作为放射科医师预评估和预选的有用工具，