BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Liu F, Liu D, Wang K, Xie X, Su L, Kuang M, Huang G, Peng B, Wang Y, Lin M, Tian J. Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients. Liver Cancer. 2020;9:397-413. [PMID: 32999867 DOI: 10.1159/000505694] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Reference Citation Analysis]
2 Kalejahi BK, Meshgini S, Danishvar S, Khorram S. Diagnosis of liver disease by computer- assisted imaging techniques: A literature review. IDA 2022;26:1097-114. [DOI: 10.3233/ida-216379] [Reference Citation Analysis]
3 Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021;11:1719. [PMID: 34574060 DOI: 10.3390/diagnostics11091719] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Zhang Y, Wei Q, Huang Y, Yao Z, Yan C, Zou X, Han J, Li Q, Mao R, Liao Y, Cao L, Lin M, Zhou X, Tang X, Hu Y, Li L, Wang Y, Yu J, Zhou J. Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular Carcinoma. Front Oncol 2022;12:878061. [DOI: 10.3389/fonc.2022.878061] [Reference Citation Analysis]
5 Yan BC, Ma XL, Li Y, Duan SF, Zhang GF, Qiang JW. MRI-Based Radiomics Nomogram for Selecting Ovarian Preservation Treatment in Patients With Early-Stage Endometrial Cancer. Front Oncol 2021;11:730281. [PMID: 34568064 DOI: 10.3389/fonc.2021.730281] [Reference Citation Analysis]
6 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
7 Zhang C, Liu D, Huang L, Zhao Y, Chen L, Guo Y. Classification of Thyroid Nodules by Using Deep Learning Radiomics Based on Ultrasound Dynamic Video. J Ultrasound Med 2022. [PMID: 35603714 DOI: 10.1002/jum.16006] [Reference Citation Analysis]
8 Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021;11:1194. [PMID: 34209197 DOI: 10.3390/diagnostics11071194] [Reference Citation Analysis]
9 Maruyama H, Yamaguchi T, Nagamatsu H, Shiina S. AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound. Diagnostics (Basel) 2021;11:292. [PMID: 33673229 DOI: 10.3390/diagnostics11020292] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
10 Wang Y, Xie W, Huang S, Feng M, Ke X, Zhong Z, Tang L, Magi-galluzzi C. The Diagnostic Value of Ultrasound-Based Deep Learning in Differentiating Parotid Gland Tumors. Journal of Oncology 2022;2022:1-7. [DOI: 10.1155/2022/8192999] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13(12): 2039-2051 [DOI: 10.4254/wjh.v13.i12.2039] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Mitrea D, Badea R, Mitrea P, Brad S, Nedevschi S. Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning Methods. Sensors (Basel) 2021;21:2202. [PMID: 33801125 DOI: 10.3390/s21062202] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, Jiang F, Cui XW, Dietrich CF. Artificial intelligence in liver ultrasound. World J Gastroenterol 2022; 28(27): 3398-3409 [DOI: 10.3748/wjg.v28.i27.3398] [Reference Citation Analysis]
14 Wu J, Xie F, Ji H, Zhang Y, Luo Y, Xia L, Lu T, He K, Sha M, Zheng Z, Yong J, Li X, Zhao D, Yang Y, Xia Q, Xue F. A Clinical-Radiomic Model for Predicting Indocyanine Green Retention Rate at 15 Min in Patients With Hepatocellular Carcinoma. Front Surg 2022;9:857838. [DOI: 10.3389/fsurg.2022.857838] [Reference Citation Analysis]
15 Minami Y, Kudo M. Image Guidance in Ablation for Hepatocellular Carcinoma: Contrast-Enhanced Ultrasound and Fusion Imaging. Front Oncol 2021;11:593636. [PMID: 33747913 DOI: 10.3389/fonc.2021.593636] [Reference Citation Analysis]
16 Wang J, Wu D, Sun M, Peng Z, Lin Y, Lin H, Chen J, Long T, Li Z, Xie C, Huang B, Feng S. Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma. BME Frontiers 2022;2022:1-12. [DOI: 10.34133/2022/9793716] [Reference Citation Analysis]
17 Sun K, Shi L, Qiu J, Pan Y, Wang X, Wang H. Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions. Eur J Nucl Med Mol Imaging 2022. [PMID: 35230493 DOI: 10.1007/s00259-022-05742-8] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Guo SY, Zhou P, Zhang Y, Jiang LQ, Zhao YF. Exploring the Value of Radiomics Features Based on B-Mode and Contrast-Enhanced Ultrasound in Discriminating the Nature of Thyroid Nodules. Front Oncol 2021;11:738909. [PMID: 34722288 DOI: 10.3389/fonc.2021.738909] [Reference Citation Analysis]
19 Yuan E, Ye Z, Song B. Imaging-based deep learning in liver diseases. Chin Med J (Engl) 2022. [PMID: 35837673 DOI: 10.1097/CM9.0000000000002199] [Reference Citation Analysis]
20 Nishida N, Kudo M. Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor. Front Oncol 2020;10:594580. [PMID: 33409151 DOI: 10.3389/fonc.2020.594580] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Wang H, Guo W, Yang W, Liu G, Cao K, Sun Y, Liang ZN, Bai XM, Wang S, Wu W, Yan K, Goldberg SN. Computer-Aided Color Parameter Imaging of Contrast-Enhanced Ultrasound Evaluates Hepatocellular Carcinoma Hemodynamic Features and Predicts Radiofrequency Ablation Outcome. Ultrasound Med Biol 2022;48:1555-66. [PMID: 35597704 DOI: 10.1016/j.ultrasmedbio.2022.04.002] [Reference Citation Analysis]