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For: Liao H, Xiong T, Peng J, Xu L, Liao M, Zhang Z, Wu Z, Yuan K, Zeng Y. Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning. Ann Surg Oncol. 2020;27:2359-2369. [PMID: 31916093 DOI: 10.1245/s10434-019-08190-1] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Reports 2022. [DOI: 10.1016/j.jhepr.2022.100443] [Reference Citation Analysis]
2 Macias RIR, Cardinale V, Kendall TJ, Avila MA, Guido M, Coulouarn C, Braconi C, Frampton AE, Bridgewater J, Overi D, Pereira SP, Rengo M, Kather JN, Lamarca A, Pedica F, Forner A, Valle JW, Gaudio E, Alvaro D, Banales JM, Carpino G. Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions. Gut 2022:gutjnl-2022-327099. [PMID: 35580963 DOI: 10.1136/gutjnl-2022-327099] [Reference Citation Analysis]
3 Liao H, Long Y, Han R, Wang W, Xu L, Liao M, Zhang Z, Wu Z, Shang X, Li X, Peng J, Yuan K, Zeng Y. Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma. Clin Transl Med 2020;10:e102. [PMID: 32536036 DOI: 10.1002/ctm2.102] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
4 Lang Q, Zhong C, Liang Z, Zhang Y, Wu B, Xu F, Cong L, Wu S, Tian Y. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer. Artif Intell Rev 2021;54:5307-46. [DOI: 10.1007/s10462-021-10023-1] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Feng S, Yu X, Liang W, Li X, Zhong W, Hu W, Zhang H, Feng Z, Song M, Zhang J, Zhang X. Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma. Front Oncol 2021;11:762733. [DOI: 10.3389/fonc.2021.762733] [Reference Citation Analysis]
6 Chen W, Fu M, Zhang C, Xing Q, Zhou F, Lin M, Dong X, Huang J, Lin S, Hong M, Zheng Q, Pan J. Deep Learning-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond. Front Med 2022;9:853261. [DOI: 10.3389/fmed.2022.853261] [Reference Citation Analysis]
7 Karadag Soylu N. Update on Hepatocellular Carcinoma: a Brief Review from Pathologist Standpoint. J Gastrointest Cancer 2020;51:1176-86. [PMID: 32844348 DOI: 10.1007/s12029-020-00499-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
8 Shi JY, Wang X, Ding GY, Dong Z, Han J, Guan Z, Ma LJ, Zheng Y, Zhang L, Yu GZ, Wang XY, Ding ZB, Ke AW, Yang H, Wang L, Ai L, Cao Y, Zhou J, Fan J, Liu X, Gao Q. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning. Gut 2021;70:951-61. [PMID: 32998878 DOI: 10.1136/gutjnl-2020-320930] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
9 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]
10 Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2(2): 42-55 [DOI: 10.35712/aig.v2.i2.42] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Yamashita R, Long J, Saleem A, Rubin DL, Shen J. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci Rep 2021;11:2047. [PMID: 33479370 DOI: 10.1038/s41598-021-81506-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021;12:31. [PMID: 33675433 DOI: 10.1186/s13244-021-00977-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
13 Ma B, Guo Y, Hu W, Yuan F, Zhu Z, Yu Y, Zou H. Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach. Front Pharmacol. 2020;11:572372. [PMID: 33132910 DOI: 10.3389/fphar.2020.572372] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
14 Wang H, Jiang Y, Li B, Cui Y, Li D, Li R. Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes. Cancers (Basel) 2020;12:E3562. [PMID: 33260561 DOI: 10.3390/cancers12123562] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26(37): 5617-5628 [PMID: 33088156 DOI: 10.3748/wjg.v26.i37.5617] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]