Hairui Chu1, Xiaoqi Lin2, Jian He1, Peipei Pang3, Bing Fan4, Pinggui Lei2, Dongchuang Guo1, Chenglong Ye2. 1. Department of Radiology, Huzhou Central Hospital, Huzhou, China. 2. Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang 330006, China. 3. GE Healthcare, Hangzhou, China. 4. Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang 330006, China. Electronic address: 26171381@qq.com.
Abstract
OBJECTIVE: Different grades of meningiomas require different treatment strategies and have a different prognosis; thus, the noninvasive classification of meningiomas before surgery is of great importance. The purpose of this study was to explore the application value of magnetic resonance imaging (MRI) radiomics based on enhanced-T1-weighted (T1WI) images in the prediction of meningiomas grade. MATERIALS AND METHODS: A total of 98 patients with meningiomas who were confirmed by surgical pathology and underwent preoperative routine MRI between January 2017 and December 2019 were analyzed. There were 82 cases of low-grade meningiomas (WHO grade I) and 16 cases of high-grade meningiomas (7 cases of WHO grade II and 9 cases of WHO grade III). These patients were randomly divided into a training group and test group according to 7:3 ratio. The lesions were manually delineated using ITK-SNAP software, and radiomics analysis were performed using the Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. Next, a prediction model was constructed using the Logistic Regression method and receiver operator characteristic was used to evaluate the prediction performance of the model. RESULTS: A radiomics prediction model was constructed based on the selected nine characteristic parameters, which performed well in predicting the meningiomas grade. The accuracy rates in the training group and the test group were respectively 94.3% and 92.9%, the sensitivities were respectively 94.8%, and 91.7%, the specificities were respectively 91.7% and 100%, and the area under the curve values were respectively 0.958 and 0.948. CONCLUSION: The MRI radiomics method based on enhanced-T1WI images has a good predictive effect on the classification of meningiomas and can provide a basis for planning clinical treatment protocols.
RCT Entities:
OBJECTIVE: Different grades of meningiomas require different treatment strategies and have a different prognosis; thus, the noninvasive classification of meningiomas before surgery is of great importance. The purpose of this study was to explore the application value of magnetic resonance imaging (MRI) radiomics based on enhanced-T1-weighted (T1WI) images in the prediction of meningiomas grade. MATERIALS AND METHODS: A total of 98 patients with meningiomas who were confirmed by surgical pathology and underwent preoperative routine MRI between January 2017 and December 2019 were analyzed. There were 82 cases of low-grade meningiomas (WHO grade I) and 16 cases of high-grade meningiomas (7 cases of WHO grade II and 9 cases of WHO grade III). These patients were randomly divided into a training group and test group according to 7:3 ratio. The lesions were manually delineated using ITK-SNAP software, and radiomics analysis were performed using the Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. Next, a prediction model was constructed using the Logistic Regression method and receiver operator characteristic was used to evaluate the prediction performance of the model. RESULTS: A radiomics prediction model was constructed based on the selected nine characteristic parameters, which performed well in predicting the meningiomas grade. The accuracy rates in the training group and the test group were respectively 94.3% and 92.9%, the sensitivities were respectively 94.8%, and 91.7%, the specificities were respectively 91.7% and 100%, and the area under the curve values were respectively 0.958 and 0.948. CONCLUSION: The MRI radiomics method based on enhanced-T1WI images has a good predictive effect on the classification of meningiomas and can provide a basis for planning clinical treatment protocols.
Authors: Lara Brunasso; Gianluca Ferini; Lapo Bonosi; Roberta Costanzo; Sofia Musso; Umberto E Benigno; Rosa M Gerardi; Giuseppe R Giammalva; Federica Paolini; Giuseppe E Umana; Francesca Graziano; Gianluca Scalia; Carmelo L Sturiale; Rina Di Bonaventura; Domenico G Iacopino; Rosario Maugeri Journal: Life (Basel) Date: 2022-04-14
Authors: Norbert Galldiks; Frank Angenstein; Jan-Michael Werner; Elena K Bauer; Robin Gutsche; Gereon R Fink; Karl-Josef Langen; Philipp Lohmann Journal: Brain Pathol Date: 2022-03 Impact factor: 6.508