Systematic Reviews
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Dec 15, 2019; 11(12): 1218-1230
Published online Dec 15, 2019. doi: 10.4251/wjgo.v11.i12.1218
Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review
Samy A Azer
Samy A Azer, Department of Medical Education, King Saud University College of Medicine, Riyadh 11461, Saudi Arabia
Author contributions: The author SAA created the idea of the review, generated the rationale and the research question, designed the project, searched the databases, analysed the findings, created the tables, wrote the manuscript, and approved the final manuscript.
Supported by the College of Medicine Research Centre, Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia.
Conflict-of-interest statement: The author declares that he has no competing interests.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: 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:
Corresponding author: Samy A Azer, FACG, Professor of Medical Education, Gastroenterologist, Department of Medical Education, King Saud University College of Medicine, P O Box 2925, Riyadh 11461, Saudi Arabia.
Telephone: +966-11-8066393 Fax: +966-11-4699174
Received: March 2, 2019
Peer-review started: March 4, 2019
First decision: June 5, 2019
Revised: July 9, 2019
Accepted: October 3, 2019
Article in press: October 3, 2019
Published online: December 15, 2019

This study highlighted several aspects related to convolutional neural networks (CNNs). First, CNNs have potential use in identifying HCC and differentiating HCC from other liver masses with high accuracy. Second, CNNs can offer several functions concerning liver cancer, including lesion detection, classification, and segmentation. Third, the use of CNN in liver cancer is not limited to radiological images, but it is of value in pathological and cellular studies. However, the study identified several limitations in the literature in this area, mainly the smaller number of studies on the topic and the lack of studies from multi-centres as well as the lack of longitudinal liver computed tomography (CT) scan studies that can enable comparing outcomes with existing stand-alone and follow-up methods. These longitudinal studies could allow researchers to compare changes with the baseline scan and thus could offer better detection of new small liver tumours.

Research background

Recently, an increasing interest in the use of deep learning has emerged in research, particularly CNNs, a class of artificial intelligence that has been widely used in biomedical research. This study reviews the current literature on the use of CNNs in assessing hepatocellular carcinoma (HCC) and liver masses and how such advanced technology can help improve clinical diagnosis.

Research motivation

While the study focuses on an evolving field in gastroenterology and oncology with promising outcomes, several researchers reported difficulties in obtaining images, which make the direct application of machine learning algorithms inappropriate for medical datasets and hence affect the capacity to conduct image classification with high accuracy. Therefore, improvement in the design of CNNs and multi-institute and multi-centre collaborations with a large number of patients with cirrhosis due to different pathological causes and patients with HCC on top of cirrhosis or liver secondaries is needed.

Research objectives

The study aimed at assessing the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of the CNNs and their performance.

Research methods

Several databases, including PubMed, EMBASE, and Web of Science, were systematically searched for studies that covered pathological anatomy, cellular, and radiological images on HCC or liver masses using the CNNs. The data were extracted as per a predefined extraction protocol, and the accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed. The primary outcomes of the study were investigating the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.

Research results

A small number of studies were identified. The studies demonstrated the ability to differentiate liver masses, differentiate HCC from other liver lesions, and differentiate HCC from cirrhosis or development of new tumours. Two studies focused on HCC nuclei grading or segmentation. In these studies, the CNNs showed satisfactory levels of accuracy. The studies aimed at detecting lesions, classification, and segmentation. Several methods were used to assess the accuracy of CNN models used.

Research conclusions

While the current studies have covered liver cancers, the number of studies conducted so far is small and limited, and more research is needed to answer questions about the accuracy and sensitivity of the CNN algorithms. The CNNs demonstrated abilities in segmentation, classification, and lesion detection in radiological and anatomical pathology images of common cancers. However, several deficiencies in current studies were observed.

Research perspectives

A large multi-centre trial is needed to evaluate carefully the use of CNNs and their clinical applications in HCC and liver masses. Differentiation between primary and secondary (metastases in the liver) or other liver masses is hard based on radiological imaging. The studies included in this review showed that CT-based deep learning methods could enable the categorisation of liver metastases from primary liver cancers. Future studies should give more attention to the assessment of accuracy and sensitivity of the CNNs in evaluating the performance of systems and calculating the positive predictive values.