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Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI

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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114468/
https://www.ncbi.nlm.nih.gov/pubmed/35600347
http://dx.doi.org/10.3389/fonc.2022.849447
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author 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
author_facet 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
author_sort Hallinan, James Thomas Patrick Decourcy
collection PubMed
description 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.
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spelling pubmed-91144682022-05-19 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 Tan, Jiong Hao Kumar, Naresh Vellayappan, Balamurugan A. Ooi, Beng Chin Quek, Swee Tian Makmur, Andrew Front Oncol Oncology 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. Frontiers Media S.A. 2022-05-04 /pmc/articles/PMC9114468/ /pubmed/35600347 http://dx.doi.org/10.3389/fonc.2022.849447 Text en Copyright © 2022 Hallinan, Zhu, Zhang, Lim, Baskar, Low, Yeong, Teo, Kumarakulasinghe, Yap, Chan, Lin, Tan, Kumar, Vellayappan, Ooi, Quek and Makmur https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
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
Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI
title Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI
title_full Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI
title_fullStr Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI
title_full_unstemmed Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI
title_short Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI
title_sort deep learning model for classifying metastatic epidural spinal cord compression on mri
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114468/
https://www.ncbi.nlm.nih.gov/pubmed/35600347
http://dx.doi.org/10.3389/fonc.2022.849447
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