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For: Cooper LA, Demicco EG, Saltz JH, Powell RT, Rao A, Lazar AJ. PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective. J Pathol. 2018;244:512-524. [PMID: 29288495 DOI: 10.1002/path.5028] [Cited by in Crossref: 72] [Cited by in F6Publishing: 66] [Article Influence: 18.0] [Reference Citation Analysis]
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1 Kalra S, Tizhoosh HR, Shah S, Choi C, Damaskinos S, Safarpoor A, Shafiei S, Babaie M, Diamandis P, Campbell CJV, Pantanowitz L. Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence. NPJ Digit Med 2020;3:31. [PMID: 32195366 DOI: 10.1038/s41746-020-0238-2] [Cited by in Crossref: 21] [Cited by in F6Publishing: 9] [Article Influence: 10.5] [Reference Citation Analysis]
2 Seltzer S, Corrigan M, O'Reilly S. The clinicomolecular landscape of de novo versus relapsed stage IV metastatic breast cancer. Exp Mol Pathol 2020;114:104404. [PMID: 32067942 DOI: 10.1016/j.yexmp.2020.104404] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
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7 Lu J, Wilfred P, Korbie D, Trau M. Regulation of Canonical Oncogenic Signaling Pathways in Cancer via DNA Methylation. Cancers (Basel) 2020;12:E3199. [PMID: 33143142 DOI: 10.3390/cancers12113199] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
8 Mombaerts I, Ramberg I, Coupland SE, Heegaard S. Diagnosis of orbital mass lesions: clinical, radiological, and pathological recommendations. Surv Ophthalmol 2019;64:741-56. [PMID: 31276737 DOI: 10.1016/j.survophthal.2019.06.006] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 4.7] [Reference Citation Analysis]
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10 Bogusławska J, Popławski P, Alseekh S, Koblowska M, Iwanicka-Nowicka R, Rybicka B, Kędzierska H, Głuchowska K, Hanusek K, Tański Z, Fernie AR, Piekiełko-Witkowska A. MicroRNA-Mediated Metabolic Reprograming in Renal Cancer. Cancers (Basel) 2019;11:E1825. [PMID: 31756931 DOI: 10.3390/cancers11121825] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]
11 Yang L, Han N, Zhang X, Zhou Y, Chen R, Zhang M. ZWINT: A potential therapeutic biomarker in patients with glioblastoma correlates with cell proliferation and invasion. Oncol Rep 2020;43:1831-44. [PMID: 32323832 DOI: 10.3892/or.2020.7573] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
12 Khella HWZ, Yousef GM. Translational research: Empowering the role of pathologists and cytopathologists. Cancer Cytopathol 2018;126:831-8. [PMID: 30281935 DOI: 10.1002/cncy.22046] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
13 Lewis JE, Kemp ML. Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance. Nat Commun 2021;12:2700. [PMID: 33976213 DOI: 10.1038/s41467-021-22989-1] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
14 Cho KO, Lee SH, Jang HJ. Feasibility of fully automated classification of whole slide images based on deep learning. Korean J Physiol Pharmacol. 2020;24:89-99. [PMID: 31908578 DOI: 10.4196/kjpp.2020.24.1.89] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
15 Lee SH, Song IH, Jang HJ. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 2021;149:728-40. [PMID: 33851412 DOI: 10.1002/ijc.33599] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
16 Zhang Y, Chen C, Duan M, Liu S, Huang L, Zhou F. BioDog, biomarker detection for improving identification power of breast cancer histologic grade in methylomics. Epigenomics 2019;11:1717-32. [PMID: 31625763 DOI: 10.2217/epi-2019-0230] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
17 Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J;  Cancer Genome Atlas Research Network; Shmulevich I, Rao AUK, Lazar AJ, Sharma A, Thorsson V. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 2018; 23: 181-193. e7. [PMID: 29617659 DOI: 10.1016/j.celrep.2018.03.086] [Cited by in Crossref: 278] [Cited by in F6Publishing: 239] [Article Influence: 92.7] [Reference Citation Analysis]
18 Thorsson V. Multiplatform Integrative Analysis of Immunogenomic Data for Biomarker Discovery. In: Thurin M, Cesano A, Marincola FM, editors. Biomarkers for Immunotherapy of Cancer. New York: Springer; 2020. pp. 679-98. [DOI: 10.1007/978-1-4939-9773-2_30] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
19 Lin B, Wang S, Yao Y, Shen Y, Yang H. Comprehensive co-expression analysis reveals TMC8 as a prognostic immune-associated gene in head and neck squamous cancer. Oncol Lett 2021;22:498. [PMID: 33981360 DOI: 10.3892/ol.2021.12759] [Reference Citation Analysis]
20 Wang J, Wu Y, Uddin MN, Hao JP, Chen R, Xiong DQ, Ding N, Yang JH, Wang JH, Ding XS. Identification of MiR-93-5p Targeted Pathogenic Markers in Acute Myeloid Leukemia through Integrative Bioinformatics Analysis and Clinical Validation. J Oncol 2021;2021:5531736. [PMID: 33828590 DOI: 10.1155/2021/5531736] [Reference Citation Analysis]
21 Huang ZD, Yao YY, Chen TY, Zhao YF, Zhang C, Niu YM. Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes. Front Physiol 2021;12:609770. [PMID: 33815132 DOI: 10.3389/fphys.2021.609770] [Reference Citation Analysis]
22 Wulczyn E, Steiner DF, Xu Z, Sadhwani A, Wang H, Flament-Auvigne I, Mermel CH, Chen PC, Liu Y, Stumpe MC. Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS One. 2020;15:e0233678. [PMID: 32555646 DOI: 10.1371/journal.pone.0233678] [Cited by in Crossref: 31] [Cited by in F6Publishing: 25] [Article Influence: 15.5] [Reference Citation Analysis]
23 Pantanowitz L, Sharma A, Carter AB, Kurc T, Sussman A, Saltz J. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. J Pathol Inform. 2018;9:40. [PMID: 30607307 DOI: 10.4103/jpi.jpi_69_18] [Cited by in Crossref: 47] [Cited by in F6Publishing: 41] [Article Influence: 11.8] [Reference Citation Analysis]
24 Woo Y, Behrendt CE, Yang A, Hahn M, Goel A, Li H, Yuan YC, Fong Y. Tumor Epigenetic Signature and Survival in Resected Gastric Cancer Patients. J Am Coll Surg 2021;232:483-491.e1. [PMID: 33465468 DOI: 10.1016/j.jamcollsurg.2020.12.023] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
25 Wang K, Li L, Franch-Expósito S, Le X, Tang J, Li Q, Wu Q, Bassaganyas L, Camps J, Zhang X, Li H, Foukakis T, Xiang T, Wu J, Ren G. Integrated multi-omics profiling of high-grade estrogen receptor-positive, HER2-negative breast cancer. Mol Oncol 2021. [PMID: 34146382 DOI: 10.1002/1878-0261.13043] [Reference Citation Analysis]
26 Mandel J, Wang H, Normolle DP, Chen W, Yan Q, Lucas PC, Benos PV, Prochownik EV. Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers. BMC Cancer 2019;19:686. [PMID: 31299925 DOI: 10.1186/s12885-019-5851-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
27 Landau MS, Pantanowitz L. Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape. Journal of the American Society of Cytopathology 2019;8:230-41. [DOI: 10.1016/j.jasc.2019.03.003] [Cited by in Crossref: 25] [Cited by in F6Publishing: 19] [Article Influence: 8.3] [Reference Citation Analysis]
28 Huntsman DG, Ladanyi M. The molecular pathology of cancer: from pan-genomics to post-genomics. J Pathol 2018;244:509-11. [PMID: 29436707 DOI: 10.1002/path.5057] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
29 Wu C, Tong L, Wu C, Chen D, Chen J, Li Q, Jia F, Huang Z. Two miRNA prognostic signatures of head and neck squamous cell carcinoma: A bioinformatic analysis based on the TCGA dataset. Cancer Med 2020;9:2631-42. [PMID: 32064753 DOI: 10.1002/cam4.2915] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
30 Wang HL, Liu PF, Yue J, Jiang WH, Cui YL, Ren H, Wang H, Zhuang Y, Liu Y, Jiang D, Dong Q, Zhang H, Mi JH, Xu ZM, Tian CJ, Zhang ZZ, Wang XW, Su MN, Lu W. Somatic gene mutation signatures predict cancer type and prognosis in multiple cancers with pan-cancer 1000 gene panel. Cancer Lett 2020;470:181-90. [PMID: 31765737 DOI: 10.1016/j.canlet.2019.11.022] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
31 Cheng J, Liu Y, Huang W, Hong W, Wang L, Zhan X, Han Z, Ni D, Huang K, Zhang J. Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma. Front Oncol 2021;11:623382. [PMID: 33869007 DOI: 10.3389/fonc.2021.623382] [Reference Citation Analysis]
32 Fassler DJ, Abousamra S, Gupta R, Chen C, Zhao M, Paredes D, Batool SA, Knudsen BS, Escobar-Hoyos L, Shroyer KR, Samaras D, Kurc T, Saltz J. Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images. Diagn Pathol. 2020;15:100. [PMID: 32723384 DOI: 10.1186/s13000-020-01003-0] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
33 Amgad M, Stovgaard ES, Balslev E, Thagaard J, Chen W, Dudgeon S, Sharma A, Kerner JK, Denkert C, Yuan Y, AbdulJabbar K, Wienert S, Savas P, Voorwerk L, Beck AH, Madabhushi A, Hartman J, Sebastian MM, Horlings HM, Hudeček J, Ciompi F, Moore DA, Singh R, Roblin E, Balancin ML, Mathieu MC, Lennerz JK, Kirtani P, Chen IC, Braybrooke JP, Pruneri G, Demaria S, Adams S, Schnitt SJ, Lakhani SR, Rojo F, Comerma L, Badve SS, Khojasteh M, Symmans WF, Sotiriou C, Gonzalez-Ericsson P, Pogue-Geile KL, Kim RS, Rimm DL, Viale G, Hewitt SM, Bartlett JMS, Penault-Llorca F, Goel S, Lien HC, Loibl S, Kos Z, Loi S, Hanna MG, Michiels S, Kok M, Nielsen TO, Lazar AJ, Bago-Horvath Z, Kooreman LFS, van der Laak JAWM, Saltz J, Gallas BD, Kurkure U, Barnes M, Salgado R, Cooper LAD; International Immuno-Oncology Biomarker Working Group. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ Breast Cancer 2020;6:16. [PMID: 32411818 DOI: 10.1038/s41523-020-0154-2] [Cited by in Crossref: 27] [Cited by in F6Publishing: 23] [Article Influence: 13.5] [Reference Citation Analysis]
34 Chang Z, Liu X, Zhao W, Xu Y. Identification and Characterization of the Copy Number Dosage-Sensitive Genes in Colorectal Cancer. Mol Ther Methods Clin Dev 2020;18:501-10. [PMID: 32775488 DOI: 10.1016/j.omtm.2020.06.020] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
35 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]
36 Azuaje F, Kim SY, Perez Hernandez D, Dittmar G. Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning. J Clin Med 2019;8:E1535. [PMID: 31557788 DOI: 10.3390/jcm8101535] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
37 Sailer V, Eng KW, Zhang T, Bareja R, Pisapia DJ, Sigaras A, Bhinder B, Romanel A, Wilkes D, Sticca E, Cyrta J, Rao R, Sahota S, Pauli C, Beg S, Motanagh S, Kossai M, Fontunge J, Puca L, Rennert H, Zhaoying Xiang J, Greco N, Kim R, MacDonald TY, McNary T, Blattner-Johnson M, Schiffman MH, Faltas BM, Greenfield JP, Rickman D, Andreopoulou E, Holcomb K, Vahdat LT, Scherr DS, van Besien K, Barbieri CE, Robinson BD, Fine HA, Ocean AJ, Molina A, Shah MA, Nanus DM, Pan Q, Demichelis F, Tagawa ST, Song W, Mosquera JM, Sboner A, Rubin MA, Elemento O, Beltran H. Integrative Molecular Analysis of Patients With Advanced and Metastatic Cancer. JCO Precis Oncol 2019;3. [PMID: 31592503 DOI: 10.1200/PO.19.00047] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
38 Liu C, Lin X, Sun B, Mao Z, Chen L, Qian H, Su C. PRCC reduces the sensitivity of cancer cells to DNA damage by inhibiting JNK and ATM/ATR pathways and results in a poor prognosis in hepatocellular carcinoma. Cell Biosci 2021;11:185. [PMID: 34715922 DOI: 10.1186/s13578-021-00699-x] [Reference Citation Analysis]
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40 Xu S, Lu Z, Shao W, Yu CY, Reiter JL, Feng Q, Feng W, Huang K, Liu Y. Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer. BMC Med Genomics 2020;13:195. [PMID: 33371906 DOI: 10.1186/s12920-020-00828-4] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
41 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning. World J Gastroenterol 2020; 26(40): 6207-6223 [PMID: 33177794 DOI: 10.3748/wjg.v26.i40.6207] [Cited by in CrossRef: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
42 Nagy Á, Munkácsy G, Győrffy B. Pancancer survival analysis of cancer hallmark genes. Sci Rep 2021;11:6047. [PMID: 33723286 DOI: 10.1038/s41598-021-84787-5] [Cited by in Crossref: 78] [Cited by in F6Publishing: 89] [Article Influence: 78.0] [Reference Citation Analysis]
43 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]
44 Wagner M, Reinke S, Hänsel R, Klapper W, Braumann UD. An image dataset related to automated macrophage detection in immunostained lymphoma tissue samples. Gigascience 2020;9:giaa016. [PMID: 32161948 DOI: 10.1093/gigascience/giaa016] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
45 Sun H, Cai X, Zhou H, Li X, Du Z, Zou H, Wu J, Xie L, Cheng Y, Xie W, Lu X, Xu L, Chen L, Li E, Wu B. The protein-protein interaction network and clinical significance of heat-shock proteins in esophageal squamous cell carcinoma. Amino Acids 2018;50:685-97. [PMID: 29700654 DOI: 10.1007/s00726-018-2569-8] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
46 Zhang J, Li Y, Liu Y, Xu G, Hei Y, Lu X, Liu W. Long non‑coding RNA NEAT1 regulates glioma cell proliferation and apoptosis by competitively binding to microRNA‑324‑5p and upregulating KCTD20 expression. Oncol Rep 2021;46:125. [PMID: 33982764 DOI: 10.3892/or.2021.8076] [Reference Citation Analysis]
47 Antonio K, Valdez MMN, Mercado-Asis L, Taïeb D, Pacak K. Pheochromocytoma/paraganglioma: recent updates in genetics, biochemistry, immunohistochemistry, metabolomics, imaging and therapeutic options. Gland Surg 2020;9:105-23. [PMID: 32206603 DOI: 10.21037/gs.2019.10.25] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 3.5] [Reference Citation Analysis]
48 Saidak Z, Pascual C, Bouaoud J, Galmiche L, Clatot F, Dakpé S, Page C, Galmiche A. A three-gene expression signature associated with positive surgical margins in tongue squamous cell carcinomas: Predicting surgical resectability from tumour biology? Oral Oncol 2019;94:115-20. [PMID: 31178206 DOI: 10.1016/j.oraloncology.2019.05.020] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
49 Jiang S, Zanazzi GJ, Hassanpour S. Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images. Sci Rep 2021;11:16849. [PMID: 34413349 DOI: 10.1038/s41598-021-95948-x] [Reference Citation Analysis]
50 Noorbakhsh J, Farahmand S, Foroughi Pour A, Namburi S, Caruana D, Rimm D, Soltanieh-Ha M, Zarringhalam K, Chuang JH. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun 2020;11:6367. [PMID: 33311458 DOI: 10.1038/s41467-020-20030-5] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
51 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]
52 Wang W, Zhang C, Yu Q, Zheng X, Yin C, Yan X, Liu G, Song Z. Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients. BMC Gastroenterol 2021;21:68. [PMID: 33579192 DOI: 10.1186/s12876-021-01638-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
53 Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27(21): 2818-2833 [PMID: 34135556 DOI: 10.3748/wjg.v27.i21.2818] [Reference Citation Analysis]
54 Riasatian A, Babaie M, Maleki D, Kalra S, Valipour M, Hemati S, Zaveri M, Safarpoor A, Shafiei S, Afshari M, Rasoolijaberi M, Sikaroudi M, Adnan M, Shah S, Choi C, Damaskinos S, Campbell CJ, Diamandis P, Pantanowitz L, Kashani H, Ghodsi A, Tizhoosh HR. Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides. Med Image Anal 2021;70:102032. [PMID: 33773296 DOI: 10.1016/j.media.2021.102032] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
55 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]
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57 Qi L, Yao Y, Zhang T, Feng F, Zhou C, Xu X, Sun C. A four-mRNA model to improve the prediction of breast cancer prognosis. Gene 2019;721:144100. [PMID: 31493508 DOI: 10.1016/j.gene.2019.144100] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]
58 Vu QD, Graham S, Kurc T, To MNN, Shaban M, Qaiser T, Koohbanani NA, Khurram SA, Kalpathy-Cramer J, Zhao T, Gupta R, Kwak JT, Rajpoot N, Saltz J, Farahani K. Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Front Bioeng Biotechnol 2019;7:53. [PMID: 31001524 DOI: 10.3389/fbioe.2019.00053] [Cited by in Crossref: 42] [Cited by in F6Publishing: 24] [Article Influence: 14.0] [Reference Citation Analysis]
59 Liang X, Zhou R, Li Y, Yang L, Su M, Lai KP. Clinical characterization and therapeutic targets of vitamin A in patients with hepatocholangiocarcinoma and coronavirus disease. Aging (Albany NY) 2021;13:15785-800. [PMID: 34176789 DOI: 10.18632/aging.203220] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
60 Ji J, Shen T, Li Y, Liu Y, Shang Z, Niu Y. CDCA5 promotes the progression of prostate cancer by affecting the ERK signalling pathway. Oncol Rep 2021;45:921-32. [PMID: 33650660 DOI: 10.3892/or.2021.7920] [Reference Citation Analysis]
61 Zhang Y, Yao Y, Xu Y, Li L, Gong Y, Zhang K, Zhang M, Guan Y, Chang L, Xia X, Li L, Jia S, Zeng Q. Pan-cancer circulating tumor DNA detection in over 10,000 Chinese patients. Nat Commun 2021;12:11. [PMID: 33397889 DOI: 10.1038/s41467-020-20162-8] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 10.0] [Reference Citation Analysis]
62 Gargya P, Bálint BL. Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data. Cancers (Basel) 2021;13:4348. [PMID: 34503158 DOI: 10.3390/cancers13174348] [Reference Citation Analysis]
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