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Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study

Liver tumors constitute a major part of the global disease burden, often making regular imaging follow-up necessary. Recently, deep learning (DL) has increasingly been applied in this research area. How these methods could facilitate report writing is still a question, which our study aims to addres...

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Autores principales: Stollmayer, Róbert, Budai, Bettina Katalin, Rónaszéki, Aladár, Zsombor, Zita, Kalina, Ildikó, Hartmann, Erika, Tóth, Gábor, Szoldán, Péter, Bérczi, Viktor, Maurovich-Horvat, Pál, Kaposi, Pál Novák
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9104155/
https://www.ncbi.nlm.nih.gov/pubmed/35563862
http://dx.doi.org/10.3390/cells11091558
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author Stollmayer, Róbert
Budai, Bettina Katalin
Rónaszéki, Aladár
Zsombor, Zita
Kalina, Ildikó
Hartmann, Erika
Tóth, Gábor
Szoldán, Péter
Bérczi, Viktor
Maurovich-Horvat, Pál
Kaposi, Pál Novák
author_facet Stollmayer, Róbert
Budai, Bettina Katalin
Rónaszéki, Aladár
Zsombor, Zita
Kalina, Ildikó
Hartmann, Erika
Tóth, Gábor
Szoldán, Péter
Bérczi, Viktor
Maurovich-Horvat, Pál
Kaposi, Pál Novák
author_sort Stollmayer, Róbert
collection PubMed
description Liver tumors constitute a major part of the global disease burden, often making regular imaging follow-up necessary. Recently, deep learning (DL) has increasingly been applied in this research area. How these methods could facilitate report writing is still a question, which our study aims to address by assessing multiple DL methods using the Medical Open Network for Artificial Intelligence (MONAI) framework, which may provide clinicians with preliminary information about a given liver lesion. For this purpose, we collected 2274 three-dimensional images of lesions, which we cropped from gadoxetate disodium enhanced T1w, native T1w, and T2w magnetic resonance imaging (MRI) scans. After we performed training and validation using 202 and 65 lesions, we selected the best performing model to predict features of lesions from our in-house test dataset containing 112 lesions. The model (EfficientNetB0) predicted 10 features in the test set with an average area under the receiver operating characteristic curve (standard deviation), sensitivity, specificity, negative predictive value, positive predictive value of 0.84 (0.1), 0.78 (0.14), 0.86 (0.08), 0.89 (0.08) and 0.71 (0.17), respectively. These results suggest that AI methods may assist less experienced residents or radiologists in liver MRI reporting of focal liver lesions.
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spelling pubmed-91041552022-05-14 Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study Stollmayer, Róbert Budai, Bettina Katalin Rónaszéki, Aladár Zsombor, Zita Kalina, Ildikó Hartmann, Erika Tóth, Gábor Szoldán, Péter Bérczi, Viktor Maurovich-Horvat, Pál Kaposi, Pál Novák Cells Article Liver tumors constitute a major part of the global disease burden, often making regular imaging follow-up necessary. Recently, deep learning (DL) has increasingly been applied in this research area. How these methods could facilitate report writing is still a question, which our study aims to address by assessing multiple DL methods using the Medical Open Network for Artificial Intelligence (MONAI) framework, which may provide clinicians with preliminary information about a given liver lesion. For this purpose, we collected 2274 three-dimensional images of lesions, which we cropped from gadoxetate disodium enhanced T1w, native T1w, and T2w magnetic resonance imaging (MRI) scans. After we performed training and validation using 202 and 65 lesions, we selected the best performing model to predict features of lesions from our in-house test dataset containing 112 lesions. The model (EfficientNetB0) predicted 10 features in the test set with an average area under the receiver operating characteristic curve (standard deviation), sensitivity, specificity, negative predictive value, positive predictive value of 0.84 (0.1), 0.78 (0.14), 0.86 (0.08), 0.89 (0.08) and 0.71 (0.17), respectively. These results suggest that AI methods may assist less experienced residents or radiologists in liver MRI reporting of focal liver lesions. MDPI 2022-05-05 /pmc/articles/PMC9104155/ /pubmed/35563862 http://dx.doi.org/10.3390/cells11091558 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Stollmayer, Róbert
Budai, Bettina Katalin
Rónaszéki, Aladár
Zsombor, Zita
Kalina, Ildikó
Hartmann, Erika
Tóth, Gábor
Szoldán, Péter
Bérczi, Viktor
Maurovich-Horvat, Pál
Kaposi, Pál Novák
Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study
title Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study
title_full Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study
title_fullStr Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study
title_full_unstemmed Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study
title_short Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study
title_sort focal liver lesion mri feature identification using efficientnet and monai: a feasibility study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9104155/
https://www.ncbi.nlm.nih.gov/pubmed/35563862
http://dx.doi.org/10.3390/cells11091558
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