Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer

Abstract Background Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI...

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Autores principales: Thomas Ying, Pablo Borrelli, Lars Edenbrandt, Olof Enqvist, Reza Kaboteh, Elin Trägårdh, Johannes Ulén, Henrik Kjölhede
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Publicado: SpringerOpen 2021
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spelling oai:doaj.org-article:c37e02c68eaa4cd190b164d8fc8d67c02021-11-21T12:15:08ZAutomated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer10.1186/s41747-021-00248-82509-9280https://doaj.org/article/c37e02c68eaa4cd190b164d8fc8d67c02021-11-01T00:00:00Zhttps://doi.org/10.1186/s41747-021-00248-8https://doaj.org/toc/2509-9280Abstract Background Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI)-based image analysis tool for segmenting skeletal muscle of the torso and calculating the muscle volume. Methods All patients who have undergone radical cystectomy for urinary bladder cancer 2011–2019 at Sahlgrenska University Hospital, and who had a pre-operative computed tomography of the abdomen within 90 days of surgery were included in the study. All patients CT studies were analysed with the automated AI-based image analysis tool. Clinical data for the patients were retrieved from the Swedish National Register for Urinary Bladder Cancer. Muscle volumes dichotomised by the median for each sex were analysed with Cox regression for OS and logistic regression for 90-day high-grade complications. The study was approved by the Swedish Ethical Review Authority (2020-03985). Results Out of 445 patients who underwent surgery, 299 (67%) had CT studies available for analysis. The automated AI-based tool failed to segment the muscle volume in seven (2%) patients. Cox regression analysis showed an independent significant association with OS (HR 1.62; 95% CI 1.07–2.44; p = 0.022). Logistic regression did not show any association with high-grade complications. Conclusion The fully automated AI-based CT image analysis provides a low-cost and meaningful clinical measure that is an independent biomarker for OS following radical cystectomy.Thomas YingPablo BorrelliLars EdenbrandtOlof EnqvistReza KabotehElin TrägårdhJohannes UlénHenrik KjölhedeSpringerOpenarticleImage analysis (computer-assisted)Body compositionSarcopeniaArtificial intelligenceUrinary bladder cancerMedical physics. Medical radiology. Nuclear medicineR895-920ENEuropean Radiology Experimental, Vol 5, Iss 1, Pp 1-8 (2021)
institution DOAJ
collection DOAJ
language EN
topic Image analysis (computer-assisted)
Body composition
Sarcopenia
Artificial intelligence
Urinary bladder cancer
Medical physics. Medical radiology. Nuclear medicine
R895-920
spellingShingle Image analysis (computer-assisted)
Body composition
Sarcopenia
Artificial intelligence
Urinary bladder cancer
Medical physics. Medical radiology. Nuclear medicine
R895-920
Thomas Ying
Pablo Borrelli
Lars Edenbrandt
Olof Enqvist
Reza Kaboteh
Elin Trägårdh
Johannes Ulén
Henrik Kjölhede
Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
description Abstract Background Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI)-based image analysis tool for segmenting skeletal muscle of the torso and calculating the muscle volume. Methods All patients who have undergone radical cystectomy for urinary bladder cancer 2011–2019 at Sahlgrenska University Hospital, and who had a pre-operative computed tomography of the abdomen within 90 days of surgery were included in the study. All patients CT studies were analysed with the automated AI-based image analysis tool. Clinical data for the patients were retrieved from the Swedish National Register for Urinary Bladder Cancer. Muscle volumes dichotomised by the median for each sex were analysed with Cox regression for OS and logistic regression for 90-day high-grade complications. The study was approved by the Swedish Ethical Review Authority (2020-03985). Results Out of 445 patients who underwent surgery, 299 (67%) had CT studies available for analysis. The automated AI-based tool failed to segment the muscle volume in seven (2%) patients. Cox regression analysis showed an independent significant association with OS (HR 1.62; 95% CI 1.07–2.44; p = 0.022). Logistic regression did not show any association with high-grade complications. Conclusion The fully automated AI-based CT image analysis provides a low-cost and meaningful clinical measure that is an independent biomarker for OS following radical cystectomy.
format article
author Thomas Ying
Pablo Borrelli
Lars Edenbrandt
Olof Enqvist
Reza Kaboteh
Elin Trägårdh
Johannes Ulén
Henrik Kjölhede
author_facet Thomas Ying
Pablo Borrelli
Lars Edenbrandt
Olof Enqvist
Reza Kaboteh
Elin Trägårdh
Johannes Ulén
Henrik Kjölhede
author_sort Thomas Ying
title Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
title_short Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
title_full Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
title_fullStr Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
title_full_unstemmed Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
title_sort automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
publisher SpringerOpen
publishDate 2021
url https://doaj.org/article/c37e02c68eaa4cd190b164d8fc8d67c0
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