A. Mazal, Liyuan Chen, F. Poh, Jing Wang, M. Folkert, O. Ashikyan, P. Pezeshk, A. Chhabra
{"title":"卷积神经网络能准确预测周围神经鞘肿瘤的良恶性","authors":"A. Mazal, Liyuan Chen, F. Poh, Jing Wang, M. Folkert, O. Ashikyan, P. Pezeshk, A. Chhabra","doi":"10.17756/jnpn.2021-038","DOIUrl":null,"url":null,"abstract":"Background: Peripheral nerve sheath tumors (PNSTs) comprise ~5-10% of soft tissue tumors encountered in the clinical setting. Benign lesions (BPNSTs), such as neurofibromas and schwannomas are often asymptomatic or cause neuropathy. Malignant peripheral nerve sheath tumors (MPNSTs) frequently exhibit rapid invasive behavior and metastatic spread. MR imaging markers do not reliably differentiate BPNSTs from MPNSTs. Convolutional neural networks employ machine learning and multi-order statistics to derive imaging signatures that could improve diagnostic assessment of PNSTs.","PeriodicalId":385711,"journal":{"name":"Journal of Neuroimaging in Psychiatry & Neurology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Convolutional Neural Networks Accurately Predict Benign versus Malignant Status Among Peripheral Nerve Sheath Tumors\",\"authors\":\"A. Mazal, Liyuan Chen, F. Poh, Jing Wang, M. Folkert, O. Ashikyan, P. Pezeshk, A. Chhabra\",\"doi\":\"10.17756/jnpn.2021-038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Peripheral nerve sheath tumors (PNSTs) comprise ~5-10% of soft tissue tumors encountered in the clinical setting. Benign lesions (BPNSTs), such as neurofibromas and schwannomas are often asymptomatic or cause neuropathy. Malignant peripheral nerve sheath tumors (MPNSTs) frequently exhibit rapid invasive behavior and metastatic spread. MR imaging markers do not reliably differentiate BPNSTs from MPNSTs. Convolutional neural networks employ machine learning and multi-order statistics to derive imaging signatures that could improve diagnostic assessment of PNSTs.\",\"PeriodicalId\":385711,\"journal\":{\"name\":\"Journal of Neuroimaging in Psychiatry & Neurology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuroimaging in Psychiatry & Neurology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17756/jnpn.2021-038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuroimaging in Psychiatry & Neurology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17756/jnpn.2021-038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Convolutional Neural Networks Accurately Predict Benign versus Malignant Status Among Peripheral Nerve Sheath Tumors
Background: Peripheral nerve sheath tumors (PNSTs) comprise ~5-10% of soft tissue tumors encountered in the clinical setting. Benign lesions (BPNSTs), such as neurofibromas and schwannomas are often asymptomatic or cause neuropathy. Malignant peripheral nerve sheath tumors (MPNSTs) frequently exhibit rapid invasive behavior and metastatic spread. MR imaging markers do not reliably differentiate BPNSTs from MPNSTs. Convolutional neural networks employ machine learning and multi-order statistics to derive imaging signatures that could improve diagnostic assessment of PNSTs.
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