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Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep
Zhizheng Zhuo1, Jie Zhang1, Yunyun Duan1
1Department of Radiology (Z.Z., J.Z., Y.D., L.Q., C.F., X.H., D.C., X.X., T.S., Y.L.), Department of Neurosurgery (Y.W., W.J.), and Center for Neurology (D.T., X.Z., F.S.), Beijing Tiantan Hospital, Capital Medical University, No. 119, West Southern 4th Ring Road, Fengtai District, Beijing 100070, People's Republic of China; BioMind, Beijing, People's Republic of China (Z.L.); Department of Medical Imaging Product, Neusoft Group, Shenyang, People's Republic of China (X. Guo, X. Gong); China National Clinical Research Center for Neurologic Diseases, Beijing, People's Republic of China (D.T., F.S.); Department of Neurology and Tianjin Neurologic Institute, Tianjin Medical University General Hospital, Tianjin, People's Republic of China (F.S.); Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland (S.H.); UCL Institutes of Neurology and Healthcare Engineering, London, England (F.B.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, the Netherlands (F.B.); and School of Information and Electronics, Beijing Institute of Technology, Beijing, People's Republic of China (C.Y.).
Abstract:
Accurate differentiation of intramedullary spinal cord tumors and inflammatory demyelinating lesions and their subtypes are warranted because of their overlapping characteristics at MRI but with different treatments and prognosis. The authors aimed to develop a pipeline for spinal cord lesion segmentation and classification using two-dimensional MultiResUNet and DenseNet121 networks based on T2-weighted images. A retrospective cohort of 490 patients (118 patients with astrocytoma, 130 with ependymoma, 101 with multiple sclerosis [MS], and 141 with neuromyelitis optica spectrum disorders [NMOSD]) was used for model development, and a prospective cohort of 157 patients (34 patients with astrocytoma, 45 with ependymoma, 33 with MS, and 45 with NMOSD) was used for model testing. In the test cohort, the model achieved Dice scores of 0.77, 0.80, 0.50, and 0.58 for segmentation of astrocytoma, ependymoma, MS, and NMOSD, respectively, against manual labeling. Accuracies of 96% (area under the receiver operating characteristic curve [AUC], 0.99), 82% (AUC, 0.90), and 79% (AUC, 0.85) were achieved for the classifications of tumor versus demyelinating lesion, astrocytoma versus ependymoma, and MS versus NMOSD, respectively. In a subset of radiologically difficult cases, the classifier showed an accuracy of 79%-95% (AUC, 0.78-0.97). The established deep learning pipeline for segmentation and classification of spinal cord lesions can support an accurate radiologic diagnosis. Supplemental material is available for this article. © RSNA, 2022 Keywords: Spinal Cord MRI, Astrocytoma, Ependymoma, Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, Deep Learning.
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