Please use this identifier to cite or link to this item: https://doi.org/10.3389/fonc.2022.849447
Title: Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI
Authors: 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
Tan, Jiong Hao
Kumar, Naresh 
Vellayappan, Balamurugan A 
Ooi, Beng Chin
Quek, Swee Tian 
Makmur, Andrew
Keywords: Science & Technology
Life Sciences & Biomedicine
Oncology
deep learning model
metastatic epidural spinal cord compression
MRI
Bilsky classification
spinal metastasis classification
spinal metastatic disease
epidural spinal cord compression
LUMBAR SPINE
RELIABILITY
FEATURES
Issue Date: 4-May-2022
Publisher: FRONTIERS MEDIA SA
Citation: 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, Tan, Jiong Hao, Kumar, Naresh, Vellayappan, Balamurugan A, Ooi, Beng Chin, Quek, Swee Tian, Makmur, Andrew (2022-05-04). Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI. FRONTIERS IN ONCOLOGY 12. ScholarBank@NUS Repository. https://doi.org/10.3389/fonc.2022.849447
Abstract: Background: Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated 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 and 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 labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year 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 DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.
Source Title: FRONTIERS IN ONCOLOGY
URI: https://scholarbank.nus.edu.sg/handle/10635/228322
ISSN: 2234943X
DOI: 10.3389/fonc.2022.849447
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