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For: 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]
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8 Al-Rajhi N, Soudy H, Ahmed SA, Elhassan T, Mohammed SF, Khoja HA, Ghebeh H. CD3+T-lymphocyte infiltration is an independent prognostic factor for advanced nasopharyngeal carcinoma. BMC Cancer 2020;20:240. [PMID: 32199452 DOI: 10.1186/s12885-020-06757-w] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
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11 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]
12 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]
13 Kannan S, Morgan LA, Liang B, Cheung MG, Lin CQ, Mun D, Nader RG, Belghasem ME, Henderson JM, Francis JM, Chitalia VC, Kolachalama VB. Segmentation of Glomeruli Within Trichrome Images Using Deep Learning. Kidney Int Rep 2019;4:955-62. [PMID: 31317118 DOI: 10.1016/j.ekir.2019.04.008] [Cited by in Crossref: 49] [Cited by in F6Publishing: 37] [Article Influence: 16.3] [Reference Citation Analysis]
14 Bull JA, Macklin PS, Quaiser T, Braun F, Waters SL, Pugh CW, Byrne HM. Combining multiple spatial statistics enhances the description of immune cell localisation within tumours. Sci Rep 2020;10:18624. [PMID: 33122646 DOI: 10.1038/s41598-020-75180-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
15 Yu M, Tan J, Wang J. [Research Progress of Single Cell Sequencing in Lung Cancer]. Zhongguo Fei Ai Za Zhi 2021;24:279-83. [PMID: 33910276 DOI: 10.3779/j.issn.1009-3419.2021.102.04] [Reference Citation Analysis]
16 Bremer E, Saltz J, Almeida JS. ImageBox 2 - Efficient and Rapid Access of Image Tiles from Whole-Slide Images Using Serverless HTTP Range Requests. J Pathol Inform 2020;11:29. [PMID: 33163255 DOI: 10.4103/jpi.jpi_31_20] [Reference Citation Analysis]
17 Li JJ, Tsang JY, Tse GM. Tumor Microenvironment in Breast Cancer-Updates on Therapeutic Implications and Pathologic Assessment. Cancers (Basel) 2021;13:4233. [PMID: 34439387 DOI: 10.3390/cancers13164233] [Reference Citation Analysis]
18 Ahn R, Ursini-Siegel J. Clinical Potential of Kinase Inhibitors in Combination with Immune Checkpoint Inhibitors for the Treatment of Solid Tumors. Int J Mol Sci 2021;22:2608. [PMID: 33807608 DOI: 10.3390/ijms22052608] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Shaban M, Khurram SA, Fraz MM, Alsubaie N, Masood I, Mushtaq S, Hassan M, Loya A, Rajpoot NM. A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma. Sci Rep 2019;9:13341. [PMID: 31527658 DOI: 10.1038/s41598-019-49710-z] [Cited by in Crossref: 38] [Cited by in F6Publishing: 28] [Article Influence: 12.7] [Reference Citation Analysis]
20 Lea D, Watson M, Skaland I, Hagland HR, Lillesand M, Gudlaugsson E, Søreide K. A template to quantify the location and density of CD3 + and CD8 + tumor-infiltrating lymphocytes in colon cancer by digital pathology on whole slides for an objective, standardized immune score assessment. Cancer Immunol Immunother 2021;70:2049-57. [PMID: 33439293 DOI: 10.1007/s00262-020-02834-y] [Reference Citation Analysis]
21 Jung WY, Min KW, Oh YH. Increased VEGF-A in solid type of lung adenocarcinoma reduces the patients' survival. Sci Rep 2021;11:1321. [PMID: 33446784 DOI: 10.1038/s41598-020-79907-6] [Reference Citation Analysis]
22 Asaoka M, Patnaik SK, Zhang F, Ishikawa T, Takabe K. Lymphovascular invasion in breast cancer is associated with gene expression signatures of cell proliferation but not lymphangiogenesis or immune response. Breast Cancer Res Treat 2020;181:309-22. [PMID: 32285241 DOI: 10.1007/s10549-020-05630-5] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 10.0] [Reference Citation Analysis]
23 Althobaiti MM, Almulihi A, Ashour AA, Mansour RF, Gupta D, Jain DK. Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans. Journal of Healthcare Engineering 2022;2022:1-15. [DOI: 10.1155/2022/2872461] [Reference Citation Analysis]
24 Bhuva DD, Cursons J, Smyth GK, Davis MJ. Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer. Genome Biol 2019;20:236. [PMID: 31727119 DOI: 10.1186/s13059-019-1851-8] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
25 Walens A, Olsson LT, Gao X, Hamilton AM, Kirk EL, Cohen SM, Midkiff BR, Xia Y, Sherman ME, Nikolaishvili-Feinberg N, Serody JS, Hoadley KA, Troester MA, Calhoun BC. Protein-based immune profiles of basal-like vs. luminal breast cancers. Lab Invest 2021;101:785-93. [PMID: 33623115 DOI: 10.1038/s41374-020-00506-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Lou S, Meng F, Yin X, Zhang Y, Han B, Xue Y. Comprehensive Characterization of RNA Processing Factors in Gastric Cancer Identifies a Prognostic Signature for Predicting Clinical Outcomes and Therapeutic Responses. Front Immunol 2021;12:719628. [PMID: 34413861 DOI: 10.3389/fimmu.2021.719628] [Reference Citation Analysis]
27 Schöniger S, Degner S, Zhang Q, Schandelmaier C, Aupperle-Lellbach H, Jasani B, Schoon HA. Tumor Infiltrating Lymphocytes in Pet Rabbit Mammary Carcinomas: A Study with Relevance to Comparative Pathology. Animals (Basel) 2020;10:E1437. [PMID: 32824521 DOI: 10.3390/ani10081437] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
28 Sharma A, Tarbox L, Kurc T, Bona J, Smith K, Kathiravelu P, Bremer E, Saltz JH, Prior F. PRISM: A Platform for Imaging in Precision Medicine. JCO Clin Cancer Inform 2020;4:491-9. [PMID: 32479186 DOI: 10.1200/CCI.20.00001] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
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31 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]
32 Pan X, Zhang C, Wang J, Wang P, Gao Y, Shang S, Guo S, Li X, Zhi H, Ning S. Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma. Brief Bioinform 2021:bbab481. [PMID: 34864866 DOI: 10.1093/bib/bbab481] [Reference Citation Analysis]
33 Wood-Trageser MA, Lesniak AJ, Demetris AJ. Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring. Transplantation 2019;103:1306-22. [PMID: 30768568 DOI: 10.1097/TP.0000000000002656] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
34 Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16:703-715. [PMID: 31399699 DOI: 10.1038/s41571-019-0252-y] [Cited by in Crossref: 191] [Cited by in F6Publishing: 169] [Article Influence: 63.7] [Reference Citation Analysis]
35 Rahman A, Jahangir C, Lynch SM, Alattar N, Aura C, Russell N, Lanigan F, Gallagher WM. Advances in tissue-based imaging: impact on oncology research and clinical practice. Expert Rev Mol Diagn 2020;20:1027-37. [PMID: 32510287 DOI: 10.1080/14737159.2020.1770599] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Yang F, Jia X, Lei P, He Y, Xiang Y, Jiao J, Zhou S, Qian W, Duan Q. Quantification of hepatic steatosis in histologic images by deep learning method. J Xray Sci Technol 2019;27:1033-45. [PMID: 31744039 DOI: 10.3233/XST-190570] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
37 Harmon SA, Sanford TH, Brown GT, Yang C, Mehralivand S, Jacob JM, Valera VA, Shih JH, Agarwal PK, Choyke PL, Turkbey B. Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer. JCO Clin Cancer Inform 2020;4:367-82. [PMID: 32330067 DOI: 10.1200/CCI.19.00155] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
38 Liu Y. Application of artificial intelligence in clinical non-small cell lung cancer. Artif Intell Cancer 2020; 1(1): 19-30 [DOI: 10.35713/aic.v1.i1.19] [Cited by in CrossRef: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
39 Fu Y, Jung AW, Torne RV, Gonzalez S, Vöhringer H, Shmatko A, Yates LR, Jimenez-linan M, Moore L, Gerstung M. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer 2020;1:800-10. [DOI: 10.1038/s43018-020-0085-8] [Cited by in Crossref: 55] [Cited by in F6Publishing: 19] [Article Influence: 27.5] [Reference Citation Analysis]
40 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]
41 Zheng X, Li L, Yu C, Yang J, Zhao Y, Su C, Yu J, Xu M. Establishment of a tumor immune microenvironment-based molecular classification system of breast cancer for immunotherapy. Aging (Albany NY) 2021;13:24313-38. [PMID: 34762599 DOI: 10.18632/aging.203682] [Reference Citation Analysis]
42 Hsu CL, Ou DL, Bai LY, Chen CW, Lin L, Huang SF, Cheng AL, Jeng YM, Hsu C. Exploring Markers of Exhausted CD8 T Cells to Predict Response to Immune Checkpoint Inhibitor Therapy for Hepatocellular Carcinoma. Liver Cancer 2021;10:346-59. [PMID: 34414122 DOI: 10.1159/000515305] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
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44 Peyster EG, Arabyarmohammadi S, Janowczyk A, Azarianpour-Esfahani S, Sekulic M, Cassol C, Blower L, Parwani A, Lal P, Feldman MD, Margulies KB, Madabhushi A. An automated computational image analysis pipeline for histological grading of cardiac allograft rejection. Eur Heart J 2021;42:2356-69. [PMID: 33982079 DOI: 10.1093/eurheartj/ehab241] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
45 Zafar MM, Rauf Z, Sohail A, Khan AR, Obaidullah M, Khan SH, Lee YS, Khan A. Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN. Photodiagnosis Photodyn Ther 2021;37:102676. [PMID: 34890783 DOI: 10.1016/j.pdpdt.2021.102676] [Reference Citation Analysis]
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47 Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers (Basel) 2019;11:E1673. [PMID: 31661863 DOI: 10.3390/cancers11111673] [Cited by in Crossref: 33] [Cited by in F6Publishing: 30] [Article Influence: 11.0] [Reference Citation Analysis]
48 Xu H, Liu L, Lei X, Mandal M, Lu C. An unsupervised method for histological image segmentation based on tissue cluster level graph cut. Comput Med Imaging Graph 2021;93:101974. [PMID: 34481236 DOI: 10.1016/j.compmedimag.2021.101974] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 Borowicz A, Le H, Humphries G, Nehls G, Höschle C, Kosarev V, Lynch HJ. Aerial-trained deep learning networks for surveying cetaceans from satellite imagery. PLoS One 2019;14:e0212532. [PMID: 31574136 DOI: 10.1371/journal.pone.0212532] [Cited by in Crossref: 10] [Cited by in F6Publishing: 3] [Article Influence: 3.3] [Reference Citation Analysis]
50 Zhao Y, Schaafsma E, Gorlov IP, Hernando E, Thomas NE, Shen R, Turk MJ, Berwick M, Amos CI, Cheng C. A Leukocyte Infiltration Score Defined by a Gene Signature Predicts Melanoma Patient Prognosis. Mol Cancer Res 2019;17:109-19. [PMID: 30171176 DOI: 10.1158/1541-7786.MCR-18-0173] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 4.0] [Reference Citation Analysis]
51 Bagaev A, Kotlov N, Nomie K, Svekolkin V, Gafurov A, Isaeva O, Osokin N, Kozlov I, Frenkel F, Gancharova O, Almog N, Tsiper M, Ataullakhanov R, Fowler N. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell 2021;39:845-865.e7. [PMID: 34019806 DOI: 10.1016/j.ccell.2021.04.014] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
52 Amgad M, Atteya L, Hussein H, Mohammed KH, Hafiz E, Elsebaie MAT, Mobadersany P, Manthey D, Gutman DA, Elfandy H, Cooper LAD. Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings. Bioinformatics 2021:btab670. [PMID: 34586355 DOI: 10.1093/bioinformatics/btab670] [Reference Citation Analysis]
53 Zhao B, Pritchard JR. Evolution of the nonsense-mediated decay pathway is associated with decreased cytolytic immune infiltration. PLoS Comput Biol 2019;15:e1007467. [PMID: 31658270 DOI: 10.1371/journal.pcbi.1007467] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
54 Harmon SA, Patel PG, Sanford TH, Caven I, Iseman R, Vidotto T, Picanço C, Squire JA, Masoudi S, Mehralivand S, Choyke PL, Berman DM, Turkbey B, Jamaspishvili T. High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts. Mod Pathol 2021;34:478-89. [PMID: 32884130 DOI: 10.1038/s41379-020-00674-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
55 Xu H, Cong F, Hwang TH. Machine Learning and Artificial Intelligence-driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides. Eur Urol Focus 2021;7:706-9. [PMID: 34353733 DOI: 10.1016/j.euf.2021.07.006] [Reference Citation Analysis]
56 Bera K, Katz I, Madabhushi A. Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO Clin Cancer Inform 2020;4:1039-50. [PMID: 33166198 DOI: 10.1200/CCI.20.00110] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
57 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]
58 Lu C, Koyuncu C, Corredor G, Prasanna P, Leo P, Wang X, Janowczyk A, Bera K, Lewis J Jr, Velcheti V, Madabhushi A. Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers. Med Image Anal 2021;68:101903. [PMID: 33352373 DOI: 10.1016/j.media.2020.101903] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
59 Boldrini L, Bibault JE, Masciocchi C, Shen Y, Bittner MI. Deep Learning: A Review for the Radiation Oncologist. Front Oncol. 2019;9:977. [PMID: 31632910 DOI: 10.3389/fonc.2019.00977] [Cited by in Crossref: 30] [Cited by in F6Publishing: 29] [Article Influence: 10.0] [Reference Citation Analysis]
60 Kanavati F, Toyokawa G, Momosaki S, Takeoka H, Okamoto M, Yamazaki K, Takeo S, Iizuka O, Tsuneki M. A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images. Sci Rep 2021;11:8110. [PMID: 33854137 DOI: 10.1038/s41598-021-87644-7] [Reference Citation Analysis]
61 Min KW, Kim WS, Kim DH, Son BK, Oh YH, Kwon MJ, Lee HS, Lee SE, Kim IA, Moon JY, Kim KY, Park JH. High polymerase ε expression associated with increased CD8+T cells improves survival in patients with non-small cell lung cancer. PLoS One 2020;15:e0233066. [PMID: 32433714 DOI: 10.1371/journal.pone.0233066] [Reference Citation Analysis]
62 Allam M, Cai S, Coskun AF. Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics. NPJ Precis Oncol 2020;4:11. [PMID: 32377572 DOI: 10.1038/s41698-020-0114-1] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
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