Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma
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ORIGINAL ARTICLE
Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma Hua Zhang 1,2,3 & Jiajie Mo 1,2,3 & Han Jiang 4 & Zhuyun Li 4,5 & Wenhan Hu 1,2,3 & Chao Zhang 1,2,3 & Yao Wang 1,2,3 & Xiu Wang 1,2,3 & Chang Liu 1,2,3 & Baotian Zhao 1,2,3 & Jianguo Zhang 1,2,3 & Kai Zhang 1,2,3 Accepted: 18 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Meningiomas . Deep learning . PSPNet . Delineation . Grade classification
Background Meningiomas, neoplasms of mesodermal-arachnoid origin, are the most common primary intracranial and spinal tumors
(Viaene et al. 2019). Approximately 20% of all primary intracranial tumors are meningiomas (Nanda et al. 2011). According to the World Health Organization (WHO) classification system, meningiomas are classified as grade I, II, and
Hua Zhang and Jiajie Mo contributed equally to this work. Significance 1. The deep learning model improves the accuracy of meningioma detection and segmentation. 2. The deep learning model allows accurate meningioma grading in presurgical evaluation. 3. The utility of deep learning can offer clinicians auxiliary information for decision-making. * Jianguo Zhang [email protected]
2
Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical
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