Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study. Issue 3 (15th September 2021)

• Record Type: Journal Article
• Authors: Zhang, Michael; Tong, Elizabeth; Hamrick, Forrest; Lee, Edward H; Tam, Lydia T; Pendleton, Courtney; Smith, Brandon W; Hug, Nicholas F; Biswal, Sandip; Seekins, Jayne; Mattonen, Sarah A; Napel, Sandy; Campen, Cynthia J; Spinner, Robert J; Yeom, Kristen W; Wilson, Thomas J; Mahan, Mark A
• Is Part Of: Neurosurgery. Volume 89:Issue 3(2021)
• Page Start: 509
• Page End: 517
• Publication Date: 2021-09-15
• Subjects: Machine learning -- Magnetic resonance imaging -- Malignant peripheral nerve sheath tumor -- Peripheral nerve sheath tumor -- Radiomics -- Sensitivity -- Specificity; Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1093/neuros/nyab212
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract: BACKGROUND: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE: To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS: We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS: A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans ( P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was ( P = .001). CONCLUSION: Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.