Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT

Abstract

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2–7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873–0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858–0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803–0.837) and general radiologist (κ = 0.726, 95% CI 0.706–0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT

“…DL tools for spine conditions on cross-sectional imaging include MRI assessment of spinal canal stenosis ( 12 , 31 ), radiotherapy planning for vertebral tumours to exclude organs at risk including the spinal cord ( 32 ), and for differentiating benign versus malignant spinal tumours ( 33 ). DL algorithms for automated Bilsky grading of MSCC have also been explored on MRI ( 34 ) and CT ( 15 ). The preliminary DL algorithms for MSCC grading on CT showed kappas of 0.873-0.911 (p<0.001) on an internal test set and were used as the basis for this study ( 15 ).…”

“…We developed a consecutive region of interest (ROI) detector and classification/grading deep learning pipeline following the study by Hallinan and Zhu et al., 2022 ( 15 ). First, we build Faster R-CNN ( 19 ) combined with ResNet50 ( 20 ) as its backbone network architecture, which consists of 50 convolutional layers with ReLU ( 21 ) activation function for non-linearity.…”

“…The fine-tuned model is used for ROI detection. Second, the combined window learning (CWL) ( 15 ) method with average fusion is adopted for ROI classification with multi-window information due to its robustness to missing input window information and reduced model size. Specifically, we build the CWL model with a convolutional prototypical network ( 23 ) using ResNeXt50 ( 24 ) as its backbone network architecture.…”

“…Most recently, Hallinan et al. (2022) trained and tested preliminary CT deep learning algorithms for classification of MSCC ( 15 ). The DL algorithms showed good interrater variability compared to an expert radiologist (reference standard) with kappa values between 0.873 to 0.911 (p<0.001).…”

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression

“…Radiologists using dedicated CT windows showed high interobserver agreement (kappa values ranging from 0.866 to 0.947) and sensitivities (ranging from 91.51 to 98.11) for recognition of any grade of MESCC. The original radiology reports showed relatively reduced performance (kappa = 0.095, sensitivity = 44.34), suggesting that dedicated training sessions for assessment of MESCC on CT or even the use of a deep learning algorithm for automatic classification of MESCC could improve patient care [ 31 , 32 ]. Future prospective studies are planned to assess the accuracy of MESCC detection on staging CT across a more extensive range of oncological patients.…”