Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis
(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/23/5701 |
| _version_ | 1797400315860877312 |
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| author | Qiuxia Wei Nengren Tan Shiyu Xiong Wanrong Luo Haiying Xia Baoming Luo |
| author_facet | Qiuxia Wei Nengren Tan Shiyu Xiong Wanrong Luo Haiying Xia Baoming Luo |
| author_sort | Qiuxia Wei |
| collection | DOAJ |
| description | (1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87–91), the specificity was 90% (95% CI: 87–92), and the AUC was 0.95 (95% CI: 0.93–0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field. |
| first_indexed | 2024-03-09T01:53:46Z |
| format | Article |
| id | doaj.art-59ccac39e6634772ba9b5b525b5e75b2 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T01:53:46Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-59ccac39e6634772ba9b5b525b5e75b22023-12-08T15:12:59ZengMDPI AGCancers2072-66942023-12-011523570110.3390/cancers15235701Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-AnalysisQiuxia Wei0Nengren Tan1Shiyu Xiong2Wanrong Luo3Haiying Xia4Baoming Luo5Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, ChinaSchool of Electronic and Information Engineering, Guangxi Normal University, 15 Qixing District, Guilin 541004, ChinaDepartment of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, ChinaDepartment of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, ChinaSchool of Electronic and Information Engineering, Guangxi Normal University, 15 Qixing District, Guilin 541004, ChinaDepartment of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou 510120, China(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87–91), the specificity was 90% (95% CI: 87–92), and the AUC was 0.95 (95% CI: 0.93–0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field.https://www.mdpi.com/2072-6694/15/23/5701deep learning methodsmedical Imagehepatocellular carcinomadiagnosis |
| spellingShingle | Qiuxia Wei Nengren Tan Shiyu Xiong Wanrong Luo Haiying Xia Baoming Luo Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis Cancers deep learning methods medical Image hepatocellular carcinoma diagnosis |
| title | Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis |
| title_full | Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis |
| title_fullStr | Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis |
| title_full_unstemmed | Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis |
| title_short | Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis |
| title_sort | deep learning methods in medical image based hepatocellular carcinoma diagnosis a systematic review and meta analysis |
| topic | deep learning methods medical Image hepatocellular carcinoma diagnosis |
| url | https://www.mdpi.com/2072-6694/15/23/5701 |
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