Stollmayer R, Budai BK, Tóth A, Kalina I, Hartmann E, Szoldán P, Bérczi V, Maurovich-Horvat P, Kaposi PN. Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging. World J Gastroenterol 2021; 27(35): 5978-5988 [PMID: 34629814 DOI: 10.3748/wjg.v27.i35.5978]
Corresponding Author of This Article
Bettina K Budai, MD, Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Korányi Sándor st. 2., Budapest 1083, Hungary. budai.bettina@med.semmelweis-univ.hu
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Sep 21, 2021; 27(35): 5978-5988 Published online Sep 21, 2021. doi: 10.3748/wjg.v27.i35.5978
Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging
Róbert Stollmayer, Bettina K Budai, Ambrus Tóth, Ildikó Kalina, Erika Hartmann, Péter Szoldán, Viktor Bérczi, Pál Maurovich-Horvat, Pál N Kaposi
Róbert Stollmayer, Bettina K Budai, Ambrus Tóth, Ildikó Kalina, Viktor Bérczi, Pál Maurovich-Horvat, Pál N Kaposi, Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest 1083, Hungary
Erika Hartmann, Department of Transplantation and Surgery, Faculty of Medicine, Semmelweis University, Budapest 1082, Hungary
Péter Szoldán, MedInnoScan Research and Development Ltd., Budapest 1112, Hungary
Author contributions: Stollmayer R designed and performed the research and wrote the paper; Budai BK and Tóth A contributed to data collection and analysis; Kalina I and Hartmann E provided clinical advice; Szoldán P contributed to the analysis; Bérczi V and Maurovich-Horvat P supervised the report; Kaposi PN designed the research, contributed to the analysis and supervised the report; all authors have read and approved the final manuscript.
Institutional review board statement: The present study has been approved by the institutional ethics committee of Semmelweis University (Semmelweis University Regional and Institutional Committee of Science and Research Ethics) according to the World Medical Association guidelines and Declaration of Helsinki, revised in 2000 in Edinburgh, No. SE-RKEB 136/2019.
Informed consent statement: As this is a retrospective study, in compliance with the Hungarian legal code, the need for written patient consent was waived by the ethics committee. Patients were not required to give informed consent to the study because the analysis used only anonymized clinical data that were obtained after each patient agreed to treatment and gave written informed consent to the MRI scan in compliance with our institutional protocol.
Conflict-of-interest statement: The authors have no financial relationships to disclose.
Data sharing statement: Additional anonymized data are available upon request from the corresponding author.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bettina K Budai, MD, Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Korányi Sándor st. 2., Budapest 1083, Hungary. budai.bettina@med.semmelweis-univ.hu
Received: April 30, 2021 Peer-review started: April 30, 2021 First decision: June 23, 2021 Revised: July 7, 2021 Accepted: August 25, 2021 Article in press: August 25, 2021 Published online: September 21, 2021
ARTICLE HIGHLIGHTS
Research background
Interest in medical applications of artificial intelligence (AI) has steeply risen in the last few years. As one of the most obvious beneficiaries of the advances in computer vision, radiology research has also put AI in a prominent position. Convolutional neural networks are the state-of-the-art methods used in computer vision. Focal liver lesions (FLLs) are common findings during imaging, which can best be evaluated via hepatocyte-specific contrast-enhanced magnetic resonance imaging (MRI).
Research motivation
Though convolutional neural networks are widely used for medical image research purposes, the effect of input, such as data dimensionality and the effect of multiple input channels, has not yet been widely examined in this area. MRI volumes presumably hold more complex information about each lesion; as such, three-dimensional inputs may be more difficult to process and properly use for classification tasks in comparison to two-dimensional axial slices. The combination of multiple MRI sequences in addition to the use of hepatocyte-specific contrast agents (HSAs) may also affect diagnostic accuracy.
Research objectives
Our research aimed to compare two- and three-dimensional DenseNets264 networks for the multi-phasic hepatocyte-specific contrast-enhanced MRI-based classification of FLLs.
Research methods
T2-weighted, arterial phase, portal venous phase, and hepatobiliary phase volumes of focal nodular hyperplasias, hepatocellular carcinomas and liver metastases were used to train the two models. Diagnostic performance was evaluated on an independent test set, based on area under the curve, positive and negative predictive values (NPVs), sensitivity, specificity and f1 score.
Research results
The study found that via the use of either two- or three-dimensional convolutional neural networks and the combination of multiple MRI sequences, the average area under the curve, sensitivity, specificity, NPV, positive predictive value and f1 scores of comparable level can be achieved.
Research conclusions
According to our findings, two- and three-dimensional networks can both be used for highly accurate differentiation of multiple classes of FLLs by combining multiple MRI phases and using HSAs.
Research perspectives
This study’s findings can help to clarify the potential applicability of two- and three-dimensional multi-channel MRI images for the convolutional neural network-based classification of FLLs using HSAs.
