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For: Saillard C, Schmauch B, Laifa O, Moarii M, Toldo S, Zaslavskiy M, Pronier E, Laurent A, Amaddeo G, Regnault H, Sommacale D, Ziol M, Pawlotsky JM, Mulé S, Luciani A, Wainrib G, Clozel T, Courtiol P, Calderaro J. Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides. Hepatology 2020;72:2000-13. [PMID: 32108950 DOI: 10.1002/hep.31207] [Cited by in Crossref: 32] [Cited by in F6Publishing: 31] [Article Influence: 32.0] [Reference Citation Analysis]
Number Citing Articles
1 Calderaro J, Schmauch B, Saillard C, Courtiol P. REPLY. Hepatology 2021;73:2078-9. [PMID: 32894800 DOI: 10.1002/hep.31540] [Reference Citation Analysis]
2 Koteluk O, Wartecki A, Mazurek S, Kołodziejczak I, Mackiewicz A. How Do Machines Learn? Artificial Intelligence as a New Era in Medicine. J Pers Med 2021;11:32. [PMID: 33430240 DOI: 10.3390/jpm11010032] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
3 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]
4 Xie Q, Ju T, Zhou C, Zhai L. LncRNA RNCR3 Promotes the Progression of HCC by Activating the Akt/GSK3β Signaling Pathway. Evid Based Complement Alternat Med 2020;2020:8367454. [PMID: 33062024 DOI: 10.1155/2020/8367454] [Reference Citation Analysis]
5 Kubota N, Fujiwara N, Hoshida Y. Clinical and Molecular Prediction of Hepatocellular Carcinoma Risk. J Clin Med 2020;9:E3843. [PMID: 33256232 DOI: 10.3390/jcm9123843] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Zhen S, Cai X. Letter to the Editor: Predicting Survival After Hepatocellular Carcinoma Resection Using Deep-Learning on Histological Slides.Hepatology. 2021;73:2077-2078. [PMID: 32894573 DOI: 10.1002/hep.31543] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Reference Citation Analysis]
8 Werner J, Kronberg RM, Stachura P, Ostermann PN, Müller L, Schaal H, Bhatia S, Kather JN, Borkhardt A, Pandyra AA, Lang KS, Lang PA. Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2. Viruses 2021;13:610. [PMID: 33918368 DOI: 10.3390/v13040610] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 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]
10 Masuzaki R, Kanda T, Sasaki R, Matsumoto N, Nirei K, Ogawa M, Moriyama M. Application of artificial intelligence in hepatology: Minireview. Artif Intell Gastroenterol 2020; 1(1): 5-11 [DOI: 10.35712/aig.v1.i1.5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
11 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]
12 Kleppe A, Skrede OJ, De Raedt S, Liestøl K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 2021;21:199-211. [PMID: 33514930 DOI: 10.1038/s41568-020-00327-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
13 Lee HW, Sung JJY, Ahn SH. Artificial intelligence in liver disease. J Gastroenterol Hepatol 2021;36:539-42. [PMID: 33709605 DOI: 10.1111/jgh.15409] [Reference Citation Analysis]
14 Laleh NG, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102474] [Reference Citation Analysis]
15 Kobayashi S, Saltz JH, Yang VW. State of machine and deep learning in histopathological applications in digestive diseases. World J Gastroenterol 2021; 27(20): 2545-2575 [PMID: 34092975 DOI: 10.3748/wjg.v27.i20.2545] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Mostafa F, Hasan E, Williamson M, Khan H. Statistical Machine Learning Approaches to Liver Disease Prediction. Livers 2021;1:294-312. [DOI: 10.3390/livers1040023] [Reference Citation Analysis]
17 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]
18 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]
19 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]
20 Uegami W, Bychkov A, Ozasa M, Uehara K, Kataoka K, Johkoh T, Kondoh Y, Sakanashi H, Fukuoka J. MIXTURE of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease. Mod Pathol 2022. [PMID: 35197560 DOI: 10.1038/s41379-022-01025-7] [Reference Citation Analysis]
21 Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2(6): 141-156 [DOI: 10.35712/aig.v2.i6.141] [Reference Citation Analysis]
22 Kather JN, Calderaro J. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer. Nat Rev Gastroenterol Hepatol 2020;17:591-2. [DOI: 10.1038/s41575-020-0343-3] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
23 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: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Liu QP, Xu X, Zhu FP, Zhang YD, Liu XS. Prediction of prognostic risk factors in hepatocellular carcinoma with transarterial chemoembolization using multi-modal multi-task deep learning.EClinicalMedicine. 2020;23:100379. [PMID: 32548574 DOI: 10.1016/j.eclinm.2020.100379] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
25 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]
26 Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13(12): 1977-1990 [DOI: 10.4254/wjh.v13.i12.1977] [Reference Citation Analysis]
27 Sato M, Tateishi R, Yatomi Y, Koike K. Artificial intelligence in the diagnosis and management of hepatocellular carcinoma.J Gastroenterol Hepatol. 2021;36:551-560. [PMID: 33709610 DOI: 10.1111/jgh.15413] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
28 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] [Reference Citation Analysis]
29 Beaufrère A, Calderaro J, Paradis V. Combined hepatocellular-cholangiocarcinoma: An update. J Hepatol 2021;74:1212-24. [PMID: 33545267 DOI: 10.1016/j.jhep.2021.01.035] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
30 Coulouarn C. Artificial intelligence and omics in cancer. Artif Intell Cancer 2020; 1(1): 1-7 [DOI: 10.35713/aic.v1.i1.1] [Reference Citation Analysis]
31 Seah J, Boeken T, Sapoval M, Goh GS. Prime Time for Artificial Intelligence in Interventional Radiology. Cardiovasc Intervent Radiol. [DOI: 10.1007/s00270-021-03044-4] [Reference Citation Analysis]
32 Morilla I. Repairing the human with artificial intelligence in oncology. Artif Intell Cancer 2021; 2(5): 60-68 [DOI: 10.35713/aic.v2.i5.60] [Reference Citation Analysis]
33 Phan DV, Chan CL, Li AA, Chien TY, Nguyen VC. Liver cancer prediction in a viral hepatitis cohort: A deep learning approach. Int J Cancer 2020;147:2871-8. [PMID: 32761609 DOI: 10.1002/ijc.33245] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Chen H, Sung JJY. Potentials of AI in medical image analysis in Gastroenterology and Hepatology. J Gastroenterol Hepatol. 2021;36:31-38. [PMID: 33140875 DOI: 10.1111/jgh.15327] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
35 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
36 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]
37 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]
38 Qu W, Liu Q, Jiao X, Zhang T, Wang B, Li N, Dong T, Cui B. Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study. Front Oncol 2020;10:623818. [PMID: 33680946 DOI: 10.3389/fonc.2020.623818] [Reference Citation Analysis]
39 Gehrung M, Crispin-Ortuzar M, Berman AG, O'Donovan M, Fitzgerald RC, Markowetz F. Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat Med 2021;27:833-41. [PMID: 33859411 DOI: 10.1038/s41591-021-01287-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
40 van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021;27:775-84. [PMID: 33990804 DOI: 10.1038/s41591-021-01343-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
41 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]
42 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152. [PMID: 34579788 DOI: 10.1186/s13073-021-00968-x] [Reference Citation Analysis]
43 Zeng Q, Klein C, Caruso S, Maille P, Laleh NG, Sommacale D, Laurent A, Amaddeo G, Gentien D, Rapinat A, Regnault H, Charpy C, Nguyen CT, Tournigand C, Brustia R, Pawlotsky JM, Kather JN, Maiuri MC, Loménie N, Calderaro J. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J Hepatol 2022:S0168-8278(22)00031-9. [PMID: 35143898 DOI: 10.1016/j.jhep.2022.01.018] [Reference Citation Analysis]
44 Yousef R, Gupta G, Yousef N, Khari M. A holistic overview of deep learning approach in medical imaging. Multimedia Systems. [DOI: 10.1007/s00530-021-00884-5] [Reference Citation Analysis]
45 Lee IC, Huang JY, Chen TC, Yen CH, Chiu NC, Hwang HE, Huang JG, Liu CA, Chau GY, Lee RC, Hung YP, Chao Y, Ho SY, Huang YH. Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection. Liver Cancer 2021;10:572-82. [PMID: 34950180 DOI: 10.1159/000518728] [Reference Citation Analysis]
46 Schrammen PL, Ghaffari Laleh N, Echle A, Truhn D, Schulz V, Brinker TJ, Brenner H, Chang-Claude J, Alwers E, Brobeil A, Kloor M, Heij LR, Jäger D, Trautwein C, Grabsch HI, Quirke P, West NP, Hoffmeister M, Kather JN. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J Pathol 2021. [PMID: 34561876 DOI: 10.1002/path.5800] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]