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For: Azer SA. Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review. World J Gastrointest Oncol 2019; 11(12): 1218-1230 [PMID: 31908726 DOI: 10.4251/wjgo.v11.i12.1218]
URL: https://www.wjgnet.com/1948-5204/full/v11/i12/1218.htm
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Number Citing Articles
1
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Huan Yu, Zhenwei Wang, Yiqing Sun, Wenwei Bo, Kai Duan, Chunhua Song, Yi Hu, Jie Zhou, Zizhang Mu, Ning Wu. Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics. Frontiers in Psychiatry 2023; 13 doi: 10.3389/fpsyt.2022.1105496
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George E Fowler, Rhiannon C Macefield, Conor Hardacre, Mark P Callaway, Neil J Smart, Natalie S Blencowe. Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review. BMJ Open 2021; 11(10) doi: 10.1136/bmjopen-2021-054411
9
Joseph C Ahn, Touseef Ahmad Qureshi, Amit G Singal, Debiao Li, Ju-Dong Yang. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World Journal of Hepatology 2021; 13(12): 2039-2051 doi: 10.4254/wjh.v13.i12.2039
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Vinícius Remus Ballotin, Lucas Goldmann Bigarella, John Soldera, Jonathan Soldera. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artificial Intelligence in Gastrointestinal Endoscopy 2021; 2(4): 127-135 doi: 10.37126/aige.v2.i4.127
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44
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45
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46
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B. Lakshmipriya, Biju Pottakkat, G. Ramkumar. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review. Artificial Intelligence in Medicine 2023; 141 doi: 10.1016/j.artmed.2023.102557
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Rayyan Azam Khan, Yigang Luo, Fang-Xiang Wu. Machine learning based liver disease diagnosis: A systematic review. Neurocomputing 2022; 468 doi: 10.1016/j.neucom.2021.08.138
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Jarrod Younger, Emily Morris, Nicholas Arnold, Chanchala Athulathmudali, Janani Pinidiyapathirage, William MacAskill. A systematic review of comparisons of AI and radiologists in the diagnosis of HCC in multiphase CT: implications for practice. Japanese Journal of Radiology 2026; 44(1) doi: 10.1007/s11604-025-01853-y
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Hajin Kim, Juho Park, Jina Shim, Youngjin Lee. Application and Optimization of a Fast Non-Local Means Noise Reduction Algorithm in Pediatric Abdominal Virtual Monoenergetic Images. Electronics 2024; 13(23) doi: 10.3390/electronics13234684
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T. K. R. Agita, M. Arun, K. Immanuvel Arokia James, S. Arthi, P. Somasundari, M. Moorthi, K. Sureshkumar. Emerging Trends in Expert Applications and Security. Lecture Notes in Networks and Systems 2023; 681 doi: 10.1007/978-981-99-1909-3_26
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Kamyab Keshtkar, Abbas Keshtkar, Alireza Safarpour. Classifying colorectal cancer or colorectal polyps in endoscopic setting using convolutional neural network: protocol for a systematic review and meta-analysis. F1000Research 2020; 9 doi: 10.12688/f1000research.25548.1
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Quirino Lai, Gabriele Spoletini, Gianluca Mennini, Zoe Larghi Laureiro, Diamantis I Tsilimigras, Timothy Michael Pawlik, Massimo Rossi. Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review. World Journal of Gastroenterology 2020; 26(42): 6679-6688 doi: 10.3748/wjg.v26.i42.6679
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Haopeng Kuang, Zhongwei Yang, Xukun Zhang, Shunli Wang, Lihua Zhang. A Review of Artificial Intelligence in Preoperative Clinical Staging of Liver Cancer. 2021 International Conference on Networking Systems of AI (INSAI) 2021;  doi: 10.1109/INSAI54028.2021.00024
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Manh-Tien Nguyen, Thai Dinh Kim, Manh-Hung Ha, Anh-Luyen Do, Lan-Anh Nguyen, Dieu-Linh Ngo. Advanced Learning-Based Segmentation of Liver and Tumor 3D Images for Early Disease Diagnosis. 2023 RIVF International Conference on Computing and Communication Technologies (RIVF) 2023;  doi: 10.1109/RIVF60135.2023.10471798
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Qingzeng Xu, Jun Ye. Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network. Computational Intelligence and Neuroscience 2022; 2022 doi: 10.1155/2022/1181189
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Thilagesh P., Anand Kumar S., Aiswarya Nair U., Rabiniraj S., Shobana P., Subramani M., Sriram K.. Artificial Intelligence (AI) and Liquid Biopsy Transforming Early Detection of Liver Metastases in Gastrointestinal Cancers. Current Cancer Drug Targets 2026; 26(3) doi: 10.2174/0115680096331238241125051307
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Jian Zhang, Shenglan Huang, Yongkang Xu, Jianbing Wu. Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.763842
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Walaa Abdelhamed, Mohamed El-Kassas. Integrating artificial intelligence into multidisciplinary evaluations of HCC: opportunities and challenges. Hepatoma Research 2025;  doi: 10.20517/2394-5079.2024.138
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Chen Chen, Cheng Chen, Mingrui Ma, Xiaojian Ma, Xiaoyi Lv, Xiaogang Dong, Ziwei Yan, Min Zhu, Jiajia Chen. Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism. BMC Medical Informatics and Decision Making 2022; 22(1) doi: 10.1186/s12911-022-01919-1
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Yingjie Tian, Minghao Liu, Yu Sun, Saiji Fu. When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects. iLIVER 2023; 2(1) doi: 10.1016/j.iliver.2023.02.002
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Keyur Radiya, Henrik Lykke Joakimsen, Karl Øyvind Mikalsen, Eirik Kjus Aahlin, Rolv-Ole Lindsetmo, Kim Erlend Mortensen. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. European Radiology 2023; 33(10) doi: 10.1007/s00330-023-09609-w
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Leonardo Di Cosmo, Filippo Emanuele Colella, Paweł Łajczak, Edoardo Schifino, Santiago Nieto Cuervo, Jad El Choueiri, Francesca Romana Centini, Francesca Pellicanò, Anna Łajczak, Elio Mazzapicchi, Marco Paolo Schiariti, Antonio Guilherme Cunha de Almeida, Ismail Zaed, Bruno Fernandes de Oliveira Santos. Factors predicting MRI glioma segmentation accuracy in deep learning models: a systematic review and meta-analysis. Journal of Neuroradiology 2026; 53(4) doi: 10.1016/j.neurad.2026.101562
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Jan Egger, Christina Gsaxner, Antonio Pepe, Kelsey L. Pomykala, Frederic Jonske, Manuel Kurz, Jianning Li, Jens Kleesiek. Medical deep learning—A systematic meta-review. Computer Methods and Programs in Biomedicine 2022; 221 doi: 10.1016/j.cmpb.2022.106874
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Elena Codruta Gheorghe, Carmen Nicolau, Adina Kamal, Anca Udristoiu, Lucian Gruionu, Adrian Saftoiu. Artificial Intelligence (AI)-Enhanced Ultrasound Techniques Used in Non-Alcoholic Fatty Liver Disease: Are They Ready for Prime Time?. Applied Sciences 2023; 13(8) doi: 10.3390/app13085080
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Qi Feng, Han Chen, Ruohan Jiang. Analysis of early warning of corporate financial risk via deep learning artificial neural network. Microprocessors and Microsystems 2021; 87 doi: 10.1016/j.micpro.2021.104387
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Mehrun Nisa, Saeed Ahmad Buzdar, Khalil Khan, Muhammad Saeed Ahmad. Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images. Symmetry 2022; 14(2) doi: 10.3390/sym14020383
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Khaled Bousabarah, Brian Letzen, Jonathan Tefera, Lynn Savic, Isabel Schobert, Todd Schlachter, Lawrence H. Staib, Martin Kocher, Julius Chapiro, MingDe Lin. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology 2021; 46(1) doi: 10.1007/s00261-020-02604-5
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