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Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study

BACKGROUND & AIMS: Assessment of computed tomography (CT)/magnetic resonance imaging Liver Imaging Reporting and Data System (LI-RADS) v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. We assessed the perfor...

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Autores principales: Mulé, Sébastien, Ronot, Maxime, Ghosn, Mario, Sartoris, Riccardo, Corrias, Giuseppe, Reizine, Edouard, Morard, Vincent, Quelever, Ronan, Dumont, Laura, Hernandez Londono, Jorge, Coustaud, Nicolas, Vilgrain, Valérie, Luciani, Alain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522871/
https://www.ncbi.nlm.nih.gov/pubmed/37771548
http://dx.doi.org/10.1016/j.jhepr.2023.100857
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author Mulé, Sébastien
Ronot, Maxime
Ghosn, Mario
Sartoris, Riccardo
Corrias, Giuseppe
Reizine, Edouard
Morard, Vincent
Quelever, Ronan
Dumont, Laura
Hernandez Londono, Jorge
Coustaud, Nicolas
Vilgrain, Valérie
Luciani, Alain
author_facet Mulé, Sébastien
Ronot, Maxime
Ghosn, Mario
Sartoris, Riccardo
Corrias, Giuseppe
Reizine, Edouard
Morard, Vincent
Quelever, Ronan
Dumont, Laura
Hernandez Londono, Jorge
Coustaud, Nicolas
Vilgrain, Valérie
Luciani, Alain
author_sort Mulé, Sébastien
collection PubMed
description BACKGROUND & AIMS: Assessment of computed tomography (CT)/magnetic resonance imaging Liver Imaging Reporting and Data System (LI-RADS) v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. We assessed the performance and added-value of a machine learning (ML)-based algorithm in assessing CT LI-RADS major features and categorisation of liver observations compared with qualitative assessment performed by a panel of radiologists. METHODS: High-risk patients as per LI-RADS v2018 with pathologically proven liver lesions who underwent multiphase contrast-enhanced CT at diagnosis between January 2015 and March 2019 in seven centres in five countries were retrospectively included and randomly divided into a training set (n = 84 lesions) and a test set (n = 345 lesions). An ML algorithm was trained to classify non-rim arterial phase hyperenhancement, washout, and enhancing capsule as present, absent, or of uncertain presence. LI-RADS major features and categories were compared with qualitative assessment of two independent readers. The performance of a sequential use of the ML algorithm and independent readers were also evaluated in a triage and an add-on scenario in LR-3/4 lesions. The combined evaluation of three other senior readers was used as reference standard. RESULTS: A total of 318 patients bearing 429 lesions were included. Sensitivity and specificity for LR-5 in the test set were 0.67 (95% CI, 0.62–0.72) and 0.91 (95% CI, 0.87–0.96) respectively, with 242 (70.1%) lesions accurately categorised. Using the ML algorithm in a triage scenario improved the overall performance for LR-5. (0.86 and 0.93 sensitivity, 0.82 and 0.76 specificity, 78% and 82.3% accuracy for the two independent readers). CONCLUSIONS: Quantitative assessment of CT LI-RADS v2018 major features is feasible and diagnoses LR-5 observations with high performance especially in combination with the radiologist’s visual analysis in patients at high-risk for HCC. IMPACT AND IMPLICATIONS: Assessment of CT/MRI LI-RADS v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. Rather than replacing radiologists, our results highlight the potential benefit from the radiologist–artificial intelligence interaction in improving focal liver lesions characterisation by using the developed algorithm as a triage tool to the radiologist’s visual analysis. Such an AI-enriched diagnostic pathway may help standardise and improve the quality of analysis of liver lesions in patients at high risk for HCC, especially in non-expert centres in liver imaging. It may also impact the clinical decision-making and guide the clinician in identifying the lesions to be biopsied, for instance in patients with multiple liver focal lesions.
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spelling pubmed-105228712023-09-28 Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study Mulé, Sébastien Ronot, Maxime Ghosn, Mario Sartoris, Riccardo Corrias, Giuseppe Reizine, Edouard Morard, Vincent Quelever, Ronan Dumont, Laura Hernandez Londono, Jorge Coustaud, Nicolas Vilgrain, Valérie Luciani, Alain JHEP Rep Research Article BACKGROUND & AIMS: Assessment of computed tomography (CT)/magnetic resonance imaging Liver Imaging Reporting and Data System (LI-RADS) v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. We assessed the performance and added-value of a machine learning (ML)-based algorithm in assessing CT LI-RADS major features and categorisation of liver observations compared with qualitative assessment performed by a panel of radiologists. METHODS: High-risk patients as per LI-RADS v2018 with pathologically proven liver lesions who underwent multiphase contrast-enhanced CT at diagnosis between January 2015 and March 2019 in seven centres in five countries were retrospectively included and randomly divided into a training set (n = 84 lesions) and a test set (n = 345 lesions). An ML algorithm was trained to classify non-rim arterial phase hyperenhancement, washout, and enhancing capsule as present, absent, or of uncertain presence. LI-RADS major features and categories were compared with qualitative assessment of two independent readers. The performance of a sequential use of the ML algorithm and independent readers were also evaluated in a triage and an add-on scenario in LR-3/4 lesions. The combined evaluation of three other senior readers was used as reference standard. RESULTS: A total of 318 patients bearing 429 lesions were included. Sensitivity and specificity for LR-5 in the test set were 0.67 (95% CI, 0.62–0.72) and 0.91 (95% CI, 0.87–0.96) respectively, with 242 (70.1%) lesions accurately categorised. Using the ML algorithm in a triage scenario improved the overall performance for LR-5. (0.86 and 0.93 sensitivity, 0.82 and 0.76 specificity, 78% and 82.3% accuracy for the two independent readers). CONCLUSIONS: Quantitative assessment of CT LI-RADS v2018 major features is feasible and diagnoses LR-5 observations with high performance especially in combination with the radiologist’s visual analysis in patients at high-risk for HCC. IMPACT AND IMPLICATIONS: Assessment of CT/MRI LI-RADS v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. Rather than replacing radiologists, our results highlight the potential benefit from the radiologist–artificial intelligence interaction in improving focal liver lesions characterisation by using the developed algorithm as a triage tool to the radiologist’s visual analysis. Such an AI-enriched diagnostic pathway may help standardise and improve the quality of analysis of liver lesions in patients at high risk for HCC, especially in non-expert centres in liver imaging. It may also impact the clinical decision-making and guide the clinician in identifying the lesions to be biopsied, for instance in patients with multiple liver focal lesions. Elsevier 2023-07-22 /pmc/articles/PMC10522871/ /pubmed/37771548 http://dx.doi.org/10.1016/j.jhepr.2023.100857 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Mulé, Sébastien
Ronot, Maxime
Ghosn, Mario
Sartoris, Riccardo
Corrias, Giuseppe
Reizine, Edouard
Morard, Vincent
Quelever, Ronan
Dumont, Laura
Hernandez Londono, Jorge
Coustaud, Nicolas
Vilgrain, Valérie
Luciani, Alain
Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study
title Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study
title_full Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study
title_fullStr Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study
title_full_unstemmed Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study
title_short Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study
title_sort automated ct li-rads v2018 scoring of liver observations using machine learning: a multivendor, multicentre retrospective study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522871/
https://www.ncbi.nlm.nih.gov/pubmed/37771548
http://dx.doi.org/10.1016/j.jhepr.2023.100857
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