Deep Learning for Adjacent Segment Disease at Preoperative MRI for Cervical Radiculopathy

作者：Caroline M.W. Goedmakers, Asad M. Lak, Akiro H. Duey, Alexander W. Senko, Omar Arnaout, Michael W. Groff, Timothy R. Smith, Carmen Vleggeert‐Lankamp, Hasan A. Zaidi, Aakanksha Rana, Alessandro Boaro · 发表于：Radiology · 年份：2021 · DOI：10.1148/radiol.2021204731 · 被引用次数：34 · 研究领域：Cervical and Thoracic Myelopathy、Spine and Intervertebral Disc Pathology、Medical Imaging and Analysis

Background Patients who undergo surgery for cervical radiculopathy are at risk for developing adjacent segment disease (ASD). Identifying patients who will develop ASD remains challenging for clinicians. Purpose To develop and validate a deep learning algorithm capable of predicting ASD by using only preoperative cervical MRI in patients undergoing single-level anterior cervical diskectomy and fusion (ACDF). Materials and Methods In this Health Insurance Portability and Accountability Act–compliant study, retrospective chart review was performed for 1244 patients undergoing single-level ACDF in two tertiary care centers. After application of inclusion and exclusion criteria, 344 patients were included, of whom 60% (n = 208) were used for training and 40% for validation (n = 43) and testing (n = 93). A deep learning–based prediction model with 48 convolutional layers was designed and trained by using preoperative T2-sagittal cervical MRI. To validate model performance, a neuroradiologist and neurosurgeon independently provided ASD predictions for the test set. Validation metrics included accuracy, areas under the curve, and F1 scores. The difference in proportion of wrongful predictions between the model and clinician was statistically tested by using the McNemar test. Results A total of 344 patients (median age, 48 years; interquartile range, 41–58 years; 182 women) were evaluated. The model predicted ASD on the 93 test images with an accuracy of 88 of 93 (95%; 95% CI: 90, 99...