BACKGROUND: Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas. METHODS: We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators. RESULTS: One hundred and seven schwannomas and 59 neurofibromas were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUCs for the Logistic Regression (AUC = 0.923) and K Nearest Neighbor (AUC = 0.923) classifiers were significantly greater than the human evaluators (AUC = 0.766; p = 0.041). CONCLUSIONS: The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.
BACKGROUND: Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas. METHODS: We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators. RESULTS: One hundred and seven schwannomas and 59 neurofibromas were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUCs for the Logistic Regression (AUC = 0.923) and K Nearest Neighbor (AUC = 0.923) classifiers were significantly greater than the human evaluators (AUC = 0.766; p = 0.041). CONCLUSIONS: The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.
Authors: J Uthoff; F A De Stefano; K Panzer; B W Darbro; T S Sato; R Khanna; D E Quelle; D K Meyerholz; J Weimer; J C Sieren Journal: J Neuroradiol Date: 2018-06-27 Impact factor: 3.447
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Authors: Alex Zwanenburg; Martin Vallières; Mahmoud A Abdalah; Hugo J W L Aerts; Vincent Andrearczyk; Aditya Apte; Saeed Ashrafinia; Spyridon Bakas; Roelof J Beukinga; Ronald Boellaard; Marta Bogowicz; Luca Boldrini; Irène Buvat; Gary J R Cook; Christos Davatzikos; Adrien Depeursinge; Marie-Charlotte Desseroit; Nicola Dinapoli; Cuong Viet Dinh; Sebastian Echegaray; Issam El Naqa; Andriy Y Fedorov; Roberto Gatta; Robert J Gillies; Vicky Goh; Michael Götz; Matthias Guckenberger; Sung Min Ha; Mathieu Hatt; Fabian Isensee; Philippe Lambin; Stefan Leger; Ralph T H Leijenaar; Jacopo Lenkowicz; Fiona Lippert; Are Losnegård; Klaus H Maier-Hein; Olivier Morin; Henning Müller; Sandy Napel; Christophe Nioche; Fanny Orlhac; Sarthak Pati; Elisabeth A G Pfaehler; Arman Rahmim; Arvind U K Rao; Jonas Scherer; Muhammad Musib Siddique; Nanna M Sijtsema; Jairo Socarras Fernandez; Emiliano Spezi; Roel J H M Steenbakkers; Stephanie Tanadini-Lang; Daniela Thorwarth; Esther G C Troost; Taman Upadhaya; Vincenzo Valentini; Lisanne V van Dijk; Joost van Griethuysen; Floris H P van Velden; Philip Whybra; Christian Richter; Steffen Löck Journal: Radiology Date: 2020-03-10 Impact factor: 29.146