Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study

Abstract

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 107 schwannomas and 59 neurofibroma 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 AUC for the Logistic Regression (AUC=0.923) and K Nearest Neighbor (AUC=0.923) classifiers was 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.

Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information

“…SUVs of PET images were converted prior to feature extraction. Radiomics features (including first order features, texture features and shape features) were extracted using the Pyradiomics software package (Koyasu et al, 2020;Yu et al, 2021;Zhang et al, 2021;. Nine image filters (wavelet, lbp2D, lbp3D, Laplacian of Gaussian, square root, square, gradient, logarithm, exponential) were used to analyze high-dimensional image features.…”

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis

“…Several studies have shown the outlook of prediction for cancer outcome [ 14 ]. The radiomic-based classifiers using routine magnetic resonance imaging (MRI) sequences in differentiation of peripheral schwannomas and neurofibromas showed higher area under the curve (AUC) values on the receiver operator characteristic (ROC) curve than expert human evaluators [ 15 ] and so was the random forest model based on CT radiomics [ 16 ]. Radiomics can significantly improve the accuracy and consistency of diagnosis.…”

Commentary: Natural History of Brachial Plexus, Peripheral Nerve, and Spinal Schwannomas

“…In fact, recent studies have shown that machine learning models can surpass clinical experts in identifying benign vs malignant peripheral nerve sheath tumors. 9,10 Overall, the published work is a valuable foundational study that appropriately characterizes the natural history of extracranial schwannomas in a large cohort. Focused efforts using precision medicine techniques will be needed to translate the population-level estimates derived by the authors to the care of individual patients.…”

“…In fact, recent studies have shown that machine learning models can surpass clinical experts in identifying benign vs malignant peripheral nerve sheath tumors. 9,10…”