Lei Niu1, Xiaoming Zhou1, Chongfeng Duan1, Jiping Zhao1, Qinglan Sui1, Xuejun Liu2, Xuexi Zhang3. 1. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao. 2. Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao. Electronic address: bncz@sina.com. 3. GE Healthcare, Shanghai, China.
Abstract
BACKGROUND: Meningioma subtypes are one of the most common key points to the treatment and prognosis of patients. The purpose of this study was to investigate the differential diagnostic value of radiomics features on meningioma. METHODS: A total of 241 patients with meningioma who had undergone tumor resection were randomly selected including 80 with meningothelial meningioma, 80 with fibrous meningioma, and 81 with transitional meningioma. These meningiomas were divided into 4 groups including: meningothelial versus fibrous (group 1), fibrous versus transitional (group 2), meningothelial versus transitional (group 3), and meningothelial versus fibrous versus transitional (group 4). All patients were examined using the same magnetic resonance scanner (GE 3.0 T) and the preoperative contrast-enhanced T1-weighted images were available. Radiomics features from the contrast-enhanced T1-weighted images of 241 patients were evaluated by 2 experienced radiology specialists. RESULTS: A total of 385 radiomics features were extracted from the images of each patient. Several preprocessing methods were applied on the radiomics dataset to reduce the redundancy and highlight differences between different meningioma before the Fisher discrimination analysis was adopted and leave one out cross validation methods were used for the model validation. The differentiation accuracies of the Fisher discriminant analysis model for groups 1, 2, 3, and 4 were 99.4%, 98.8%, 100% and 100%, respectively; leave one out cross validation method was achieved for group 1, 2, 3, and 4 with the accuracies of 91.3%, 95.0%, 100%, and 94.2%, respectively. CONCLUSIONS: Radiomics features and the combined Fisher discriminant analysis could provide satisfactory performance in the preoperative differential diagnosis of meningioma subtypes and enable the potential ability for clinical application.
BACKGROUND:Meningioma subtypes are one of the most common key points to the treatment and prognosis of patients. The purpose of this study was to investigate the differential diagnostic value of radiomics features on meningioma. METHODS: A total of 241 patients with meningioma who had undergone tumor resection were randomly selected including 80 with meningothelial meningioma, 80 with fibrous meningioma, and 81 with transitional meningioma. These meningiomas were divided into 4 groups including: meningothelial versus fibrous (group 1), fibrous versus transitional (group 2), meningothelial versus transitional (group 3), and meningothelial versus fibrous versus transitional (group 4). All patients were examined using the same magnetic resonance scanner (GE 3.0 T) and the preoperative contrast-enhanced T1-weighted images were available. Radiomics features from the contrast-enhanced T1-weighted images of 241 patients were evaluated by 2 experienced radiology specialists. RESULTS: A total of 385 radiomics features were extracted from the images of each patient. Several preprocessing methods were applied on the radiomics dataset to reduce the redundancy and highlight differences between different meningioma before the Fisher discrimination analysis was adopted and leave one out cross validation methods were used for the model validation. The differentiation accuracies of the Fisher discriminant analysis model for groups 1, 2, 3, and 4 were 99.4%, 98.8%, 100% and 100%, respectively; leave one out cross validation method was achieved for group 1, 2, 3, and 4 with the accuracies of 91.3%, 95.0%, 100%, and 94.2%, respectively. CONCLUSIONS: Radiomics features and the combined Fisher discriminant analysis could provide satisfactory performance in the preoperative differential diagnosis of meningioma subtypes and enable the potential ability for clinical application.
Authors: Omaditya Khanna; Anahita Fathi Kazerooni; Christopher J Farrell; Michael P Baldassari; Tyler D Alexander; Michael Karsy; Benjamin A Greenberger; Jose A Garcia; Chiharu Sako; James J Evans; Kevin D Judy; David W Andrews; Adam E Flanders; Ashwini D Sharan; Adam P Dicker; Wenyin Shi; Christos Davatzikos Journal: Neurosurgery Date: 2021-10-13 Impact factor: 5.315
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