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Cited by in F6Publishing
For: Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, Mao Q, Yu H, Cai X. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020;4:14. [PMID: 32550270 DOI: 10.1038/s41698-020-0120-3] [Cited by in Crossref: 20] [Cited by in F6Publishing: 24] [Article Influence: 10.0] [Reference Citation Analysis]
Number Citing Articles
1 Chen L, Zeng H, Xiang Y, Huang Y, Luo Y, Ma X. Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma. Front Cell Dev Biol 2021;9:720110. [PMID: 34708036 DOI: 10.3389/fcell.2021.720110] [Reference Citation Analysis]
2 Pischon H, Mason D, Lawrenz B, Blanck O, Frisk AL, Schorsch F, Bertani V. Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats. Toxicol Pathol 2021;49:928-37. [PMID: 33397216 DOI: 10.1177/0192623320983244] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
3 Le Page AL, Ballot E, Truntzer C, Derangère V, Ilie A, Rageot D, Bibeau F, Ghiringhelli F. Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images. Sci Rep 2021;11:23912. [PMID: 34903781 DOI: 10.1038/s41598-021-03206-x] [Reference Citation Analysis]
4 Murchan P, Ó'Brien C, O'Connell S, McNevin CS, Baird AM, Sheils O, Ó Broin P, Finn SP. Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics. Diagnostics (Basel) 2021;11:1406. [PMID: 34441338 DOI: 10.3390/diagnostics11081406] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Bertani V, Blanck O, Guignard D, Schorsch F, Pischon H. Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models. Toxicol Pathol 2021;:1926233211052010. [PMID: 34670459 DOI: 10.1177/01926233211052010] [Reference Citation Analysis]
6 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]
7 Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 2021. [PMID: 34611303 DOI: 10.1038/s41379-021-00919-2] [Reference Citation Analysis]
8 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-80. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
9 Badea L, Stănescu E. Identifying transcriptomic correlates of histology using deep learning. PLoS One 2020;15:e0242858. [PMID: 33237966 DOI: 10.1371/journal.pone.0242858] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Tavolara TE, Niazi MKK, Gower AC, Ginese M, Beamer G, Gurcan MN. Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice. EBioMedicine 2021;67:103388. [PMID: 34000621 DOI: 10.1016/j.ebiom.2021.103388] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Aatresh AA, Alabhya K, Lal S, Kini J, Saxena PUP. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images. Int J Comput Assist Radiol Surg 2021;16:1549-63. [PMID: 34053009 DOI: 10.1007/s11548-021-02410-4] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 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]
13 Wharton KA Jr, Wood D, Manesse M, Maclean KH, Leiss F, Zuraw A. Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation. Front Mol Biosci 2021;8:672531. [PMID: 34386519 DOI: 10.3389/fmolb.2021.672531] [Reference Citation Analysis]
14 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]
15 Chen A, Li S, Yao Z, Hu J, Cao J, Topatana W, Juengpanich S, Yu H, Shen J, Chen M. Adjuvant transarterial chemoembolization to sorafenib in unresectable hepatocellular carcinoma: A meta-analysis. J Gastroenterol Hepatol 2021;36:302-10. [PMID: 32652685 DOI: 10.1111/jgh.15180] [Reference Citation Analysis]
16 Fu H, Mi W, Pan B, Guo Y, Li J, Xu R, Zheng J, Zou C, Zhang T, Liang Z, Zou J, Zou H. Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks. Front Oncol 2021;11:665929. [PMID: 34249702 DOI: 10.3389/fonc.2021.665929] [Reference Citation Analysis]
17 Jang H, Song IH, Lee SH. Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers. Applied Sciences 2021;11:808. [DOI: 10.3390/app11020808] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
18 Qu H, Zhou M, Yan Z, Wang H, Rustgi VK, Zhang S, Gevaert O, Metaxas DN. Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning. NPJ Precis Oncol 2021;5:87. [PMID: 34556802 DOI: 10.1038/s41698-021-00225-9] [Reference Citation Analysis]
19 Xu Z, Verma A, Naveed U, Bakhoum SF, Khosravi P, Elemento O. Deep learning predicts chromosomal instability from histopathology images. iScience 2021;24:102394. [PMID: 33997679 DOI: 10.1016/j.isci.2021.102394] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
20 Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021;41:1100-15. [PMID: 34613667 DOI: 10.1002/cac2.12215] [Reference Citation Analysis]
21 Wang X, Zou C, Zhang Y, Li X, Wang C, Ke F, Chen J, Wang W, Wang D, Xu X, Xie L, Zhang Y. Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images. Front Genet 2021;12:661109. [PMID: 34354733 DOI: 10.3389/fgene.2021.661109] [Reference Citation Analysis]
22 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]
23 Li LS, Guo XY, Sun K. Recent advances in blood-based and artificial intelligence-enhanced approaches for gastrointestinal cancer diagnosis. World J Gastroenterol 2021; 27(34): 5666-5681 [PMID: 34629793 DOI: 10.3748/wjg.v27.i34.5666] [Reference Citation Analysis]
24 Ballester PJ, Carmona J. Artificial intelligence for the next generation of precision oncology. NPJ Precis Oncol 2021;5:79. [PMID: 34408248 DOI: 10.1038/s41698-021-00216-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Liang CW, Fang PW, Huang HY, Lo CM. Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal Tumors. Cancers (Basel) 2021;13:5787. [PMID: 34830948 DOI: 10.3390/cancers13225787] [Reference Citation Analysis]
26 Ye T, Li S, Zhang Y. Genomic pan-cancer classification using image-based deep learning. Comput Struct Biotechnol J 2021;19:835-46. [PMID: 33598099 DOI: 10.1016/j.csbj.2021.01.010] [Reference Citation Analysis]
27 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. World J Gastroenterol 2021; 27(44): 7687-7704 [PMID: 34908807 DOI: 10.3748/wjg.v27.i44.7687] [Reference Citation Analysis]
28 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]
29 Calderaro J, Kather JN. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut. 2020;. [PMID: 33214163 DOI: 10.1136/gutjnl-2020-322880] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]