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 pe...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | JHEP Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S258955592300188X |
| _version_ | 1797674933439954944 |
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| author | Sébastien Mulé Maxime Ronot Mario Ghosn Riccardo Sartoris Giuseppe Corrias Edouard Reizine Vincent Morard Ronan Quelever Laura Dumont Jorge Hernandez Londono Nicolas Coustaud Valérie Vilgrain Alain Luciani |
| author_facet | Sébastien Mulé Maxime Ronot Mario Ghosn Riccardo Sartoris Giuseppe Corrias Edouard Reizine Vincent Morard Ronan Quelever Laura Dumont Jorge Hernandez Londono Nicolas Coustaud Valérie Vilgrain Alain Luciani |
| author_sort | Sébastien Mulé |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T22:07:20Z |
| format | Article |
| id | doaj.art-d6fdfde90b3d43438f2ae30f4052d0fb |
| institution | Directory Open Access Journal |
| issn | 2589-5559 |
| language | English |
| last_indexed | 2024-03-11T22:07:20Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JHEP Reports |
| spelling | doaj.art-d6fdfde90b3d43438f2ae30f4052d0fb2023-09-25T04:12:28ZengElsevierJHEP Reports2589-55592023-10-01510100857Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective studySébastien Mulé0Maxime Ronot1Mario Ghosn2Riccardo Sartoris3Giuseppe Corrias4Edouard Reizine5Vincent Morard6Ronan Quelever7Laura Dumont8Jorge Hernandez Londono9Nicolas Coustaud10Valérie Vilgrain11Alain Luciani12Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil, France; Faculté de Santé, Université Paris Est Créteil, Créteil, France; INSERM IMRB, U 955, Equipe 18, Créteil, FranceService de Radiologie, Hôpital Beaujon, AP-HP Nord, Clichy, France; Université de Paris, CRI, INSERM U1149, Paris, France; Corresponding author. Address: Service de Radiologie, Hôpital Beaujon AP-HP Nord, 100 Bd du Général Leclerc, 92110 Clichy, FranceService d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil, France; Faculté de Santé, Université Paris Est Créteil, Créteil, FranceService de Radiologie, Hôpital Beaujon, AP-HP Nord, Clichy, FranceService de Radiologie, Hôpital Beaujon, AP-HP Nord, Clichy, FranceService d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil, France; Faculté de Santé, Université Paris Est Créteil, Créteil, France; INSERM IMRB, U 955, Equipe 18, Créteil, FranceGE Healthcare, Buc, FranceGE Healthcare, Buc, FranceGE Healthcare, Buc, FranceGE Healthcare, Buc, FranceGE Healthcare, Buc, FranceService de Radiologie, Hôpital Beaujon, AP-HP Nord, Clichy, France; Université de Paris, CRI, INSERM U1149, Paris, FranceService d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil, France; Faculté de Santé, Université Paris Est Créteil, Créteil, France; INSERM IMRB, U 955, Equipe 18, Créteil, FranceBackground & 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.http://www.sciencedirect.com/science/article/pii/S258955592300188XHepatocellular carcinomaLI-RADSMajor featuresComputed tomographyMachine learning |
| spellingShingle | Sébastien Mulé Maxime Ronot Mario Ghosn Riccardo Sartoris Giuseppe Corrias Edouard Reizine Vincent Morard Ronan Quelever Laura Dumont Jorge Hernandez Londono Nicolas Coustaud Valérie Vilgrain Alain Luciani Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study JHEP Reports Hepatocellular carcinoma LI-RADS Major features Computed tomography Machine learning |
| 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 | Hepatocellular carcinoma LI-RADS Major features Computed tomography Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S258955592300188X |
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