Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI - Hallinan, James Thomas Patrick Decourcy; Zhu, Lei; Zhang, Wenqiao; Lim, Desmond Shi Wei; Baskar, Sangeetha; Low, Xi Zhen; Yeong, Kuan Yuen; Teo, Ee Chin; Kumarakulasinghe, Nesaretnam Barr; Yap, Qai Ven; Chan, Yiong Huak; Lin, Shuxun; et al. (Frontiers in Oncology, 2022)
Background Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated Bilsky MESCC classification on MRI could aid earlier diagnosis and referral. Purpose To develop a DL model for automated classification of MESCC on MRI. Materials and methods Patients with known MESCC, diagnosed on MRI, between September 2007 to September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non- thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labelled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-years-experience) and a neuroradiologist (5-years- experience). These labels were used to train a DL model utilising a prototypical convolutional neural network. Internal and external test sets were labelled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labelled independently by the neuroradiologist (5- years-experience), a spine surgeon (5-years-experience), and a radiation oncologist (11-years- experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated. Results Overall, 215 MRI spine studies were analyzed (164 patients, mean age=62±12[SD]) with 177(82%) for training/validation and 38(18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas=0.92-0.98,p<0.001) for dichotomous Bilsky classification (low versus high-grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas=0.94- 0.95,p<0.001) compared to the reference standard. Conclusion A deep learning model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression, and could optimise earlier diagnosis and surgical referral.