| For: | 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] |
|---|---|
| URL: | https://www.wjgnet.com/1007-9327/full/v27/i35/5978.htm |
| Number | Citing Articles |
| 1 |
Zhen Wang, Jundong Yao, Xiang Jing, Kaiyan Li, ShiChun Lu, Hong Yang, Hong Ding, Kai Li, Wen Cheng, Guangzhi He, Tianan Jiang, Fangyi Liu, Jie Yu, Zhiyu Han, Zhigang Cheng, Shuilian Tan, Zhen Wang, Erpeng Qi, Shuo Wang, YiQiong Zhang, Lu Li, Xiaocong Dong, Ping Liang, Xiaoling Yu. A combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound in the Kupffer phase for the diagnosis of well-differentiated hepatocellular carcinoma and atypical focal liver lesions: a prospective, multicenter study. Abdominal Radiology 2024; 49(10) doi: 10.1007/s00261-024-04253-4
|
| 2 |
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 2023; 15(23) doi: 10.3390/cancers15235701
|
| 3 |
Jia Guo, Dong Jiang, Yi Qian, Jiao Yu, Yi-Jun Gu, Yu-Qing Zhou, Hui-Ping Zhang. Differential diagnosis of different types of solid focal liver lesions using two-dimensional shear wave elastography. World Journal of Gastroenterology 2022; 28(32): 4716-4725 doi: 10.3748/wjg.v28.i32.4716
|
| 4 |
Alessandro Martinino, Mohammad Aloulou, Surobhi Chatterjee, Juan Pablo Scarano Pereira, Saurabh Singhal, Tapan Patel, Thomas Paul-Emile Kirchgesner, Salvatore Agnes, Salvatore Annunziata, Giorgio Treglia, Francesco Giovinazzo. Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review. Journal of Clinical Medicine 2022; 11(21) doi: 10.3390/jcm11216368
|
| 5 |
Aladár David Rónaszéki, Ibolyka Dudás, Boglarka Zsély, Bettina Katalin Budai, Róbert Stollmayer, Oszkár Hahn, Barbara Csongrády, Byung-so Park, Pál Maurovich-Horvat, Gabriella Győri, Pal Novak Kaposi. Microvascular flow imaging to differentiate focal hepatic lesions: the spoke-wheel pattern as a specific sign of focal nodular hyperplasia. Ultrasonography 2023; 42(1) doi: 10.14366/usg.22028
|
| 6 |
Zhujuan Yu, Li Ke, Zhifeng Lin. Bibliometric analysis of application of radiomics and artificial intelligence integration in personalized treatment of hepatocellular carcinoma. International Journal of Surgery 2026; 112(3) doi: 10.1097/JS9.0000000000004177
|
| 7 |
Yashbir Singh, Jesper B. Andersen, Quincy Hathaway, Sudhakar K. Venkatesh, Gregory J. Gores, Bradley Erickson. Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques. Oncotarget 2025; 16(1) doi: 10.18632/oncotarget.28709
|
| 8 |
Shuangping Huang, Zinan Hong, Bianzhe Wu, Jinglin Liang, Qinghua Huang. Spatio-temporal collaborative multiple-stream transformer network for liver lesion classification on multiple-sequence magnetic resonance imaging. Engineering Applications of Artificial Intelligence 2025; 142 doi: 10.1016/j.engappai.2024.109933
|
| 9 |
Dongdong Gu, Yuzhong Chen, Xuejian Li, Xi Ouyang, Zhong Xue, Dinggang Shen. Applications of Medical Artificial Intelligence. Lecture Notes in Computer Science 2026; 16206 doi: 10.1007/978-3-032-09569-5_12
|
| 10 |
Jingwei Wei, Hanyu Jiang, Yu Zhou, Jie Tian, Felipe S. Furtado, Onofrio A. Catalano. Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma. Digestive and Liver Disease 2023; 55(7) doi: 10.1016/j.dld.2022.12.015
|
| 11 |
Ke Wang, Yuehua Liu, Hongxin Chen, Wenjin Yu, Jiayin Zhou, Xiaoying Wang. Fully automating LI-RADS on MRI with deep learning-guided lesion segmentation, feature characterization, and score inference. Frontiers in Oncology 2023; 13 doi: 10.3389/fonc.2023.1153241
|
| 12 |
Talha Waqas, Mounir Lahlouh, Sébastien Mulé, Yasmina Chenoune-Leroul. Anatomical Context Improves Multi-Class Liver Tumor Classification Under Severe Class Imbalance: A Statistical Validation Study. 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI) 2026; doi: 10.1109/ISBI61048.2026.11515593
|
| 13 |
Man Wang, Fei Yu, Yuan Zhang. Current status and prospects of artificial intelligence in liver cancer management. Intelligent Oncology 2025; 1(3) doi: 10.1016/j.intonc.2025.06.003
|
| 14 |
Andrea Chierici, Fabien Lareyre, Antonio Iannelli, Benjamin Salucki, Sébastien Goffart, Lisa Guzzi, Elise Poggi, Hervé Delingette, Juliette Raffort. Applications of artificial intelligence in liver cancer: A scoping review. Artificial Intelligence in Medicine 2025; 169 doi: 10.1016/j.artmed.2025.103244
|
| 15 |
Feifei Lu, Yao Meng, Xiaoting Song, Xiaotong Li, Zhuang Liu, Chunru Gu, Xiaojie Zheng, Yi Jing, Wei Cai, Kanokwan Pinyopornpanish, Andrea Mancuso, Fernando Gomes Romeiro, Nahum Méndez-Sánchez, Xingshun Qi. Artificial Intelligence in Liver Diseases: Recent Advances. Advances in Therapy 2024; 41(3) doi: 10.1007/s12325-024-02781-5
|
| 16 |
Benjamin Koh, Pojsakorn Danpanichkul, Meng Wang, Darren Jun Hao Tan, Cheng Han Ng. Application of artificial intelligence in the diagnosis of hepatocellular carcinoma. eGastroenterology 2023; 1(2) doi: 10.1136/egastro-2023-100002
|
| 17 |
Chengfei Du, Wenli Cao, Junwei Liu, Jie Liu, Liming Jin, Xia Feng, Chengwu Zhang, Fangqiang Wei. Utility of a novel scoring system for difficulty of pure laparoscopic hepatectomy for intrahepatic cholangiocarcinoma. Scientific Reports 2024; 14(1) doi: 10.1038/s41598-024-83413-4
|
| 18 |
Midya Yousefzamani, Farshid Babapour Mofrad. Deep learning without borders: recent advances in ultrasound image classification for liver diseases diagnosis. Expert Review of Medical Devices 2025; 22(8) doi: 10.1080/17434440.2025.2514764
|
| 19 |
Róbert Stollmayer, Bettina Katalin Budai, Aladár Rónaszéki, Zita Zsombor, Ildikó Kalina, Erika Hartmann, Gábor Tóth, Péter Szoldán, Viktor Bérczi, Pál Maurovich-Horvat, Pál Novák Kaposi. Focal Liver Lesion MRI Feature Identification Using Efficientnet and MONAI: A Feasibility Study. Cells 2022; 11(9) doi: 10.3390/cells11091558
|
| 20 |
Mingkai Li, Zhi Zhang, Zebin Chen, Xi Chen, Huaqing Liu, Yuanqiang Xiao, Haimei Chen, Xiaodan Zong, Jingbiao Chen, Jianning Chen, Xinying Wang, Xuehong Xiao, Zhiwei Yang, Lanqing Han, Jin Wang, Bin Wu. Interactive Explainable Deep Learning Model for Hepatocellular Carcinoma Diagnosis at Gadoxetic Acid–enhanced MRI: A Retrospective, Multicenter, Diagnostic Study. Radiology: Imaging Cancer 2025; 7(3) doi: 10.1148/rycan.240332
|
| 21 |
Zhuoao Li, Saya Sailike, Yuzhe Li, Xiaogang Zhang, Xuequn Shang, Bolin Chen. CGHNet: Cross-Guided 2D–3D Hybrid Network with attention mechanism for focal liver lesion classification. Computerized Medical Imaging and Graphics 2026; 132 doi: 10.1016/j.compmedimag.2026.102780
|
| 22 |
Pasquale Avella, Micaela Cappuccio, Teresa Cappuccio, Marco Rotondo, Daniela Fumarulo, Germano Guerra, Guido Sciaudone, Antonella Santone, Francesco Cammilleri, Paolo Bianco, Maria Chiara Brunese. Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives. Life 2023; 13(10) doi: 10.3390/life13102027
|
| 23 |
Atie Validad, Abolfazl Koozari, Iraj Abedi, Mohammadreza Elhaie. AI-Assisted Advanced MRI Using Machine Learning and Deep Learning for Focal Liver Lesion Diagnosis: A Systematic Review. Indian Journal of Radiology and Imaging 2026; doi: 10.1055/s-0046-1819660
|
| 24 |
Dong Li, Haoyu Wang, Fei Gao, Xifeng Fu, Junfeng Han. Artificial intelligence for enhancing decision-making in multidisciplinary tumor boards for HCC in China. Hepatoma Research 2025; doi: 10.20517/2394-5079.2025.67
|
| 25 |
Odysseas P. Chatzipanagiotou, Constantinos Loukas, Michail Vailas, Nikolaos Machairas, Stylianos Kykalos, Georgios Charalampopoulos, Dimitrios Filippiadis, Evangellos Felekouras, Dimitrios Schizas. Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature. Journal of Gastroenterology and Hepatology 2024; 39(10) doi: 10.1111/jgh.16663
|
| 26 |
Parhat Yasin, Abudouresuli Tuersun, Anuar Ashir, Yerlan Makhambetov, Jie Sheng, Xinghua Song. Comprehensive comparative analysis of explainable deep learning model for differentiation of brucellar spondylitis and tuberculous spondylitis through MRI sequences. European Journal of Medical Research 2025; 31(1) doi: 10.1186/s40001-025-03731-9
|
| 27 |
M.R. Hüseynova, N.Y. Bayramov, M.H. Məmmədova. РОЛЬ АЛГОРИТМОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ДИАГНОСТИКЕ. Azerbaijan Medical Journal 2023; (2) doi: 10.34921/amj.2023.2.026
|
| 28 |
Yazhou Chen, Carolin V. Schneider. Promise, Pitfalls and the Path Ahead for LLMs as Diagnostic Assistants for Focal Liver Lesions. Liver International 2025; 45(6) doi: 10.1111/liv.70153
|
| 29 |
Haoran Dai, Yuyao Xiao, Caixia Fu, Robert Grimm, Heinrich von Busch, Bram Stieltjes, Moon Hyung Choi, Zhoubing Xu, Guillaume Chabin, Chun Yang, Mengsu Zeng. Deep Learning–Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast‐Enhanced MRI. Journal of Magnetic Resonance Imaging 2025; 61(1) doi: 10.1002/jmri.29404
|