BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Hamilton PW, Bankhead P, Wang Y, Hutchinson R, Kieran D, McArt DG, James J, Salto-Tellez M. Digital pathology and image analysis in tissue biomarker research. Methods. 2014;70:59-73. [PMID: 25034370 DOI: 10.1016/j.ymeth.2014.06.015] [Cited by in Crossref: 106] [Cited by in F6Publishing: 90] [Article Influence: 13.3] [Reference Citation Analysis]
Number Citing Articles
1 Salto-Tellez M, Kennedy RD. Integrated molecular pathology: the Belfast model. Drug Discov Today 2015;20:1451-4. [PMID: 26499202 DOI: 10.1016/j.drudis.2015.10.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.4] [Reference Citation Analysis]
2 Barnett D, Hall J, Haab B. Automated Identification and Quantification of Signals in Multichannel Immunofluorescence Images: The SignalFinder-IF Platform. Am J Pathol 2019;189:1402-12. [PMID: 31026417 DOI: 10.1016/j.ajpath.2019.03.011] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.7] [Reference Citation Analysis]
3 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]
4 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]
5 Mungenast F, Fernando A, Nica R, Boghiu B, Lungu B, Batra J, Ecker RC. Next-Generation Digital Histopathology of the Tumor Microenvironment. Genes (Basel) 2021;12:538. [PMID: 33917241 DOI: 10.3390/genes12040538] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Fragomeni SM, Sciallis A, Jeruss JS. Molecular Subtypes and Local-Regional Control of Breast Cancer. Surg Oncol Clin N Am 2018;27:95-120. [PMID: 29132568 DOI: 10.1016/j.soc.2017.08.005] [Cited by in Crossref: 118] [Cited by in F6Publishing: 103] [Article Influence: 29.5] [Reference Citation Analysis]
7 Carleton NM, Lee G, Madabhushi A, Veltri RW. Advances in the computational and molecular understanding of the prostate cancer cell nucleus. J Cell Biochem 2018;119:7127-42. [PMID: 29923622 DOI: 10.1002/jcb.27156] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]
8 Qaiser T, Mukherjee A, Reddy Pb C, Munugoti SD, Tallam V, Pitkäaho T, Lehtimäki T, Naughton T, Berseth M, Pedraza A, Mukundan R, Smith M, Bhalerao A, Rodner E, Simon M, Denzler J, Huang C, Bueno G, Snead D, Ellis IO, Ilyas M, Rajpoot N. HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology 2018;72:227-38. [DOI: 10.1111/his.13333] [Cited by in Crossref: 42] [Cited by in F6Publishing: 26] [Article Influence: 8.4] [Reference Citation Analysis]
9 Jones JL, Oien KA, Lee JL, Salto-Tellez M. Morphomolecular pathology: setting the framework for a new generation of pathologists. Br J Cancer 2017;117:1581-2. [PMID: 29123262 DOI: 10.1038/bjc.2017.340] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 2.6] [Reference Citation Analysis]
10 Moore DA, Young CA, Morris HT, Oien KA, Lee JL, Jones JL, Salto-Tellez M. Time for change: a new training programme for morpho-molecular pathologists? J Clin Pathol 2018;71:285-90. [PMID: 29113995 DOI: 10.1136/jclinpath-2017-204821] [Cited by in Crossref: 14] [Cited by in F6Publishing: 13] [Article Influence: 2.8] [Reference Citation Analysis]
11 Giannini LAA, Xie SX, Peterson C, Zhou C, Lee EB, Wolk DA, Grossman M, Trojanowski JQ, McMillan CT, Irwin DJ. Empiric Methods to Account for Pre-analytical Variability in Digital Histopathology in Frontotemporal Lobar Degeneration. Front Neurosci 2019;13:682. [PMID: 31333403 DOI: 10.3389/fnins.2019.00682] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
12 Carleton NM, Zhu G, Miller MC, Davis C, Kulkarni P, Veltri RW. Characterization of RNA-Binding Motif 3 (RBM3) Protein Levels and Nuclear Architecture Changes in Aggressive and Recurrent Prostate Cancer. Cancer Rep (Hoboken) 2020;3:e1237. [PMID: 32587951 DOI: 10.1002/cnr2.1237] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
13 Soendergaard C, Nielsen OH, Skak K, Røpke MA, Seidelin JB, Kvist PH. Objective Quantification of Immune Cell Infiltrates and Epidermal Proliferation in Psoriatic Skin: A Comparison of Digital Image Analysis and Manual Counting. Appl Immunohistochem Mol Morphol 2016;24:453-8. [PMID: 25906125 DOI: 10.1097/PAI.0000000000000191] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 1.2] [Reference Citation Analysis]
14 Yoshikawa T, Horai Y, Asaoka Y, Sakurai T, Kikuchi S, Yamaoka M, Tanaka M. Current status of pathological image analysis technology in pharmaceutical companies: a questionnaire survey of the Japan Pharmaceutical Manufacturers Association. J Toxicol Pathol 2020;33:131-9. [PMID: 32425346 DOI: 10.1293/tox.2019-0056] [Reference Citation Analysis]
15 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]
16 Ramirez M, Courtoy G, Kharrat O, de Beukelaer M, Mourad N, Guiot Y, Bouzin C, Gianello P. Semi-automated digital quantification of cellular infiltrates for in vivo evaluation of transplanted islets of Langerhans encapsulated with bioactive materials. Xenotransplantation 2021;28:e12704. [PMID: 34218466 DOI: 10.1111/xen.12704] [Reference Citation Analysis]
17 Wu J, Liu C, Liu X, Sun W, Li L, Gao N, Zhang Y, Yang X, Zhang J, Wang H, Liu X, Huang X, Zhang Y, Cheng R, Chi K, Mao L, Zhou L, Lin D, Ling S. Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer. Mod Pathol 2021. [PMID: 34518630 DOI: 10.1038/s41379-021-00904-9] [Reference Citation Analysis]
18 Bertram CA, Klopfleisch R. The Pathologist 2.0: An Update on Digital Pathology in Veterinary Medicine. Vet Pathol 2017;54:756-66. [DOI: 10.1177/0300985817709888] [Cited by in Crossref: 30] [Cited by in F6Publishing: 23] [Article Influence: 6.0] [Reference Citation Analysis]
19 Yan C, Nakane K, Wang X, Fu Y, Lu H, Fan X, Feldman MD, Madabhushi A, Xu J. Automated gleason grading on prostate biopsy slides by statistical representations of homology profile. Comput Methods Programs Biomed 2020;194:105528. [PMID: 32470903 DOI: 10.1016/j.cmpb.2020.105528] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
20 Hofman P, Dagher G, Laurent-Puig P, Marquette CH, Barlesi F, Bibeau F, Clément B. [Tumor banks and complex data management: Current and future challenges]. Ann Pathol 2019;39:137-43. [PMID: 30819623 DOI: 10.1016/j.annpat.2019.01.017] [Reference Citation Analysis]
21 Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, McQuaid S, Gray RT, Murray LJ, Coleman HG, James JA, Salto-Tellez M, Hamilton PW. QuPath: Open source software for digital pathology image analysis. Sci Rep 2017;7:16878. [PMID: 29203879 DOI: 10.1038/s41598-017-17204-5] [Cited by in Crossref: 964] [Cited by in F6Publishing: 847] [Article Influence: 192.8] [Reference Citation Analysis]
22 Snyderman R, Spellmeyer D. Precision medicine: beyond genomics to targeted therapies. Per Med 2016;13:97-100. [PMID: 29749898 DOI: 10.2217/pme.15.48] [Reference Citation Analysis]
23 Chen J, Li Y, Xu J, Gong L, Wang L, Liu W, Liu J. Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review. Tumour Biol 2017;39:101042831769455. [DOI: 10.1177/1010428317694550] [Cited by in Crossref: 23] [Cited by in F6Publishing: 12] [Article Influence: 4.6] [Reference Citation Analysis]
24 Monferrer E, Burgos-Panadero R, Blanquer-Maceiras M, Cañete A, Navarro S, Noguera R. High Oct4 expression: implications in the pathogenesis of neuroblastic tumours. BMC Cancer 2019;19:1. [PMID: 30606139 DOI: 10.1186/s12885-018-5219-3] [Cited by in Crossref: 36] [Cited by in F6Publishing: 10] [Article Influence: 12.0] [Reference Citation Analysis]
25 Nolte S, Zlobec I, Lugli A, Hohenberger W, Croner R, Merkel S, Hartmann A, Geppert CI, Rau TT. Construction and analysis of tissue microarrays in the era of digital pathology: a pilot study targeting CDX1 and CDX2 in a colon cancer cohort of 612 patients. J Pathol Clin Res 2017;3:58-70. [PMID: 28138402 DOI: 10.1002/cjp2.62] [Cited by in Crossref: 24] [Cited by in F6Publishing: 23] [Article Influence: 4.8] [Reference Citation Analysis]
26 Loughrey MB, Bankhead P, Coleman HG, Hagan RS, Craig S, McCorry AMB, Gray RT, McQuaid S, Dunne PD, Hamilton PW, James JA, Salto-Tellez M. Validation of the systematic scoring of immunohistochemically stained tumour tissue microarrays using QuPath digital image analysis. Histopathology 2018;73:327-38. [PMID: 29575153 DOI: 10.1111/his.13516] [Cited by in Crossref: 21] [Cited by in F6Publishing: 17] [Article Influence: 5.3] [Reference Citation Analysis]
27 Bean GR, Wen KW, Horvai AE. Adipocyte size variability in benign and malignant lipomatous tumors and morphologic mimics: a quantitative definition using digital pathology. Human Pathology 2018;72:52-8. [DOI: 10.1016/j.humpath.2017.10.030] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
28 Popovici V, Budinská E, Čápková L, Schwarz D, Dušek L, Feit J, Jaggi R. Joint analysis of histopathology image features and gene expression in breast cancer. BMC Bioinformatics 2016;17:209. [PMID: 27170365 DOI: 10.1186/s12859-016-1072-z] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 2.0] [Reference Citation Analysis]
29 Himmel LE, Hackett TA, Moore JL, Adams WR, Thomas G, Novitskaya T, Caprioli RM, Zijlstra A, Mahadevan-Jansen A, Boyd KL. Beyond the H&E: Advanced Technologies for in situ Tissue Biomarker Imaging. ILAR J 2018;59:51-65. [PMID: 30462242 DOI: 10.1093/ilar/ily004] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
30 Mittal S, Wrobel TP, Walsh M, Kajdacsy-balla A, Bhargava R. Breast cancer histopathology using infrared spectroscopic imaging: The impact of instrumental configurations. Clinical Spectroscopy 2021;3:100006. [DOI: 10.1016/j.clispe.2021.100006] [Reference Citation Analysis]
31 Grosset AA, Ouellet V, Caron C, Fragoso G, Barrès V, Delvoye N, Latour M, Aprikian A, Bergeron A, Chevalier S, Fazli L, Fleshner N, Gleave M, Karakiewicz P, Lacombe L, Lattouf JB, van der Kwast T, Trudel D, Mes-Masson AM, Saad F; Canadian Prostate Cancer Biomarker Network. Validation of the prognostic value of NF-κB p65 in prostate cancer: A retrospective study using a large multi-institutional cohort of the Canadian Prostate Cancer Biomarker Network. PLoS Med 2019;16:e1002847. [PMID: 31265453 DOI: 10.1371/journal.pmed.1002847] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
32 Wagner M, Hänsel R, Reinke S, Richter J, Altenbuchinger M, Braumann UD, Spang R, Löffler M, Klapper W. Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach. Biol Proced Online 2019;21:13. [PMID: 31303867 DOI: 10.1186/s12575-019-0098-9] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
33 Mukundan R. Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides. J Imaging 2019;5:35. [PMID: 34460463 DOI: 10.3390/jimaging5030035] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
34 Kwak MS, Lee HH, Yang JM, Cha JM, Jeon JW, Yoon JY, Kim HI. Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images. Front Oncol 2020;10:619803. [PMID: 33520727 DOI: 10.3389/fonc.2020.619803] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
35 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]
36 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]
37 Aeffner F, Adissu HA, Boyle MC, Cardiff RD, Hagendorn E, Hoenerhoff MJ, Klopfleisch R, Newbigging S, Schaudien D, Turner O, Wilson K. Digital Microscopy, Image Analysis, and Virtual Slide Repository. ILAR J 2018;59:66-79. [PMID: 30535284 DOI: 10.1093/ilar/ily007] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 5.3] [Reference Citation Analysis]
38 Meyerholz DK, Beck AP. Principles and approaches for reproducible scoring of tissue stains in research. Lab Invest 2018;98:844-55. [PMID: 29849125 DOI: 10.1038/s41374-018-0057-0] [Cited by in Crossref: 63] [Cited by in F6Publishing: 64] [Article Influence: 15.8] [Reference Citation Analysis]
39 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]
40 Liu Y, Pantanowitz L. Digital pathology: Review of current opportunities and challenges for oral pathologists. J Oral Pathol Med 2019;48:263-9. [DOI: 10.1111/jop.12825] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
41 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]
42 Casiraghi E, Huber V, Frasca M, Cossa M, Tozzi M, Rivoltini L, Leone BE, Villa A, Vergani B. A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections. BMC Bioinformatics 2018;19:357. [PMID: 30367588 DOI: 10.1186/s12859-018-2302-3] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 3.3] [Reference Citation Analysis]
43 Marble HD, Huang R, Dudgeon SN, Lowe A, Herrmann MD, Blakely S, Leavitt MO, Isaacs M, Hanna MG, Sharma A, Veetil J, Goldberg P, Schmid JH, Lasiter L, Gallas BD, Abels E, Lennerz JK. A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients. J Pathol Inform 2020;11:22. [PMID: 33042601 DOI: 10.4103/jpi.jpi_27_20] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
44 Segovia-Miranda F, Morales-Navarrete H, Kücken M, Moser V, Seifert S, Repnik U, Rost F, Brosch M, Hendricks A, Hinz S, Röcken C, Lütjohann D, Kalaidzidis Y, Schafmayer C, Brusch L, Hampe J, Zerial M. Three-dimensional spatially resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression. Nat Med 2019;25:1885-93. [PMID: 31792455 DOI: 10.1038/s41591-019-0660-7] [Cited by in Crossref: 23] [Cited by in F6Publishing: 19] [Article Influence: 7.7] [Reference Citation Analysis]
45 Zuraw A, Aeffner F. Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Vet Pathol 2021;:3009858211040484. [PMID: 34521285 DOI: 10.1177/03009858211040484] [Reference Citation Analysis]
46 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]
47 Arends MJ, Salto-Tellez M. Low-contact and high-interconnectivity pathology (LC&HI Path): post-COVID19-pandemic practice of pathology. Histopathology 2020;77:518-24. [PMID: 32516836 DOI: 10.1111/his.14174] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
48 Tao W, Ahmed W, Guo M, Mohsin A, Wu B, Li R. Selection of high-producing clones by a relative titer predictive model using image analysis. Ann Transl Med 2021;9:1144. [PMID: 34430585 DOI: 10.21037/atm-21-2822] [Reference Citation Analysis]
49 López C, Bosch R, Orero G, Korzynska A, García-Rojo M, Bueno G, Fernández-Carrobles MDM, Gibert-Ramos A, Roszkowiak L, Callau C, Fontoura L, Salvadó MT, Álvaro T, Jaén J, Roso-Llorach A, Llobera M, Gil J, Onyos M, Plancoulaine B, Baucells J, Lejeune M. The Immune Response in Nonmetastatic Axillary Lymph Nodes Is Associated with the Presence of Axillary Metastasis and Breast Cancer Patient Outcome. Am J Pathol 2020;190:660-73. [PMID: 31866348 DOI: 10.1016/j.ajpath.2019.11.002] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
50 Huang F, Ma Z, Pollan S, Yuan X, Swartwood S, Gertych A, Rodriguez M, Mallick J, Bhele S, Guindi M, Dhall D, Walts AE, Bose S, de Peralta Venturina M, Marchevsky AM, Luthringer DJ, Feller SM, Berman B, Freeman MR, Alvord WG, Vande Woude G, Amin MB, Knudsen BS. Quantitative imaging for development of companion diagnostics to drugs targeting HGF/MET. J Pathol Clin Res 2016;2:210-22. [PMID: 27785366 DOI: 10.1002/cjp2.49] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 1.3] [Reference Citation Analysis]
51 Bouzin C, Saini ML, Khaing KK, Ambroise J, Marbaix E, Grégoire V, Bol V. Digital pathology: elementary, rapid and reliable automated image analysis. Histopathology 2016;68:888-96. [PMID: 26386281 DOI: 10.1111/his.12867] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 2.6] [Reference Citation Analysis]
52 Pell R, Oien K, Robinson M, Pitman H, Rajpoot N, Rittscher J, Snead D, Verrill C; UK National Cancer Research Institute (NCRI) Cellular-Molecular Pathology (CM-Path) quality assurance working group. The use of digital pathology and image analysis in clinical trials. J Pathol Clin Res 2019;5:81-90. [PMID: 30767396 DOI: 10.1002/cjp2.127] [Cited by in Crossref: 30] [Cited by in F6Publishing: 33] [Article Influence: 10.0] [Reference Citation Analysis]
53 Homeyer A, Lotz J, Schwen LO, Weiss N, Romberg D, Höfener H, Zerbe N, Hufnagl P. Artificial Intelligence in Pathology: From Prototype to Product. J Pathol Inform 2021;12:13. [PMID: 34012717 DOI: 10.4103/jpi.jpi_84_20] [Reference Citation Analysis]
54 Robinson M, James J, Thomas G, West N, Jones L, Lee J, Oien K, Freeman A, Craig C, Sloan P, Elliot P, Cheang M, Rodriguez-Justo M, Verrill C; UK National Cancer Research Institute (NCRI) Cellular-Molecular Pathology (CM-Path) clinical trials working group. Quality assurance guidance for scoring and reporting for pathologists and laboratories undertaking clinical trial work. J Pathol Clin Res 2019;5:91-9. [PMID: 30407751 DOI: 10.1002/cjp2.121] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 2.8] [Reference Citation Analysis]
55 Lewis C, McQuaid S, Hamilton PW, Salto-Tellez M, McArt D, James JA. Building a 'Repository of Science': The importance of integrating biobanks within molecular pathology programmes. Eur J Cancer 2016;67:191-9. [PMID: 27677055 DOI: 10.1016/j.ejca.2016.08.009] [Cited by in Crossref: 24] [Cited by in F6Publishing: 22] [Article Influence: 4.0] [Reference Citation Analysis]
56 Muñiz-Hernández S, Huerta-Yepez S, Hernández-Pedro N, Ramírez-Tirado LA, Aviles-Salas A, Maldonado A, Hernández-Cueto D, Baay-Guzmán G, Arrieta O. Association between nuclear expression of retinoic acid receptor alpha and beta and clinicopathological features and prognosis of advanced non-small cell lung cancer. Int J Clin Oncol 2016;21:1051-61. [PMID: 27306217 DOI: 10.1007/s10147-016-1002-0] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
57 Browning L, Colling R, Rakha E, Rajpoot N, Rittscher J, James JA, Salto-Tellez M, Snead DRJ, Verrill C. Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective. J Clin Pathol. 2020;. [PMID: 32620678 DOI: 10.1136/jclinpath-2020-206854] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
58 Goto M, Shibahara Y, Baciu C, Allison F, Yeung JC, Darling GE, Liu M. Prognostic Impact of CXCR7 and CXCL12 Expression in Patients with Esophageal Adenocarcinoma. Ann Surg Oncol 2021;28:4943-51. [PMID: 33709176 DOI: 10.1245/s10434-021-09775-5] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
59 Shakya R, Nguyen TH, Waterhouse N, Khanna R. Immune contexture analysis in immuno-oncology: applications and challenges of multiplex fluorescent immunohistochemistry. Clin Transl Immunology 2020;9:e1183. [PMID: 33072322 DOI: 10.1002/cti2.1183] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
60 Howat W, Warford A. Advancing the boundaries of molecular cellular pathology. Methods 2014;70:1-2. [PMID: 25606621 DOI: 10.1016/j.ymeth.2014.09.007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.1] [Reference Citation Analysis]
61 Dimitriou N, Arandjelović O, Caie PD. Deep Learning for Whole Slide Image Analysis: An Overview. Front Med (Lausanne) 2019;6:264. [PMID: 31824952 DOI: 10.3389/fmed.2019.00264] [Cited by in Crossref: 50] [Cited by in F6Publishing: 37] [Article Influence: 16.7] [Reference Citation Analysis]
62 Lamba Saini M, Bouzin C, Weynand B, Marbaix E. An Appraisal of Proliferation and Apoptotic Markers in Papillary Thyroid Carcinoma: An Automated Analysis. PLoS One 2016;11:e0148656. [PMID: 26863116 DOI: 10.1371/journal.pone.0148656] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 1.2] [Reference Citation Analysis]
63 Chennubhotla C, Clarke LP, Fedorov A, Foran D, Harris G, Helton E, Nordstrom R, Prior F, Rubin D, Saltz JH, Shalley E, Sharma A. An Assessment of Imaging Informatics for Precision Medicine in Cancer. Yearb Med Inform 2017;26:110-9. [PMID: 29063549 DOI: 10.15265/IY-2017-041] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]
64 Taatjes DJ, Bouffard NA, Barrow T, Devitt KA, Gardner JA, Braet F. Quantitative pixel intensity- and color-based image analysis on minimally compressed files: implications for whole-slide imaging. Histochem Cell Biol 2019;152:13-23. [PMID: 30982111 DOI: 10.1007/s00418-019-01783-7] [Reference Citation Analysis]
65 Leão T, Siqueira M, Marcondes S, Franco-Belussi L, De Oliveira C, Fernandes CE. Comparative liver morphology associated with the hepatosomatic index in five Neotropical anuran species. Anat Rec (Hoboken) 2021;304:860-71. [PMID: 33073492 DOI: 10.1002/ar.24540] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
66 Tollefsen SE, Jarmund AH, Ytterhus B, Salvesen Ø, Mjønes P, Torp SH. Somatostatin Receptors in Human Meningiomas-Clinicopathological Aspects. Cancers (Basel) 2021;13:5704. [PMID: 34830858 DOI: 10.3390/cancers13225704] [Reference Citation Analysis]
67 Humphries MP, Maxwell P, Salto-Tellez M. QuPath: The global impact of an open source digital pathology system. Comput Struct Biotechnol J 2021;19:852-9. [PMID: 33598100 DOI: 10.1016/j.csbj.2021.01.022] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
68 Zemouri R, Devalland C, Valmary-Degano S, Zerhouni N. [Neural network: A future in pathology?]. Ann Pathol 2019;39:119-29. [PMID: 30773224 DOI: 10.1016/j.annpat.2019.01.004] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
69 Mukundan R. Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring. J Imaging 2018;4:35. [DOI: 10.3390/jimaging4020035] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
70 Tollemar V, Tudzarovski N, Boberg E, Törnqvist Andrén A, Al-adili A, Le Blanc K, Garming Legert K, Bottai M, Warfvinge G, Sugars R. Quantitative chromogenic immunohistochemical image analysis in cellprofiler software: Quantitative chromogenic immunohistochemistry. Cytometry 2018;93:1051-9. [DOI: 10.1002/cyto.a.23575] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
71 Bishop DP, Westerhausen MT, Barthelemy F, Lockwood T, Cole N, Gibbs EM, Crosbie RH, Nelson SF, Miceli MC, Doble PA, Wanagat J. Quantitative immuno-mass spectrometry imaging of skeletal muscle dystrophin. Sci Rep 2021;11:1128. [PMID: 33441839 DOI: 10.1038/s41598-020-80495-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
72 Hamilton P, O’reilly P, Bankhead P, Abels E, Salto-tellez M. Digital and Computational Pathology for Biomarker Discovery. In: Badve S, Kumar GL, editors. Predictive Biomarkers in Oncology. Cham: Springer International Publishing; 2019. pp. 87-105. [DOI: 10.1007/978-3-319-95228-4_7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
73 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]
74 Warren AY, Harrison D. WHO/ISUP classification, grading and pathological staging of renal cell carcinoma: standards and controversies. World J Urol 2018;36:1913-26. [PMID: 30123932 DOI: 10.1007/s00345-018-2447-8] [Cited by in Crossref: 43] [Cited by in F6Publishing: 49] [Article Influence: 10.8] [Reference Citation Analysis]
75 Lounis MA, Ouellet V, Péant B, Caron C, Li Z, Al-Mass A, Madiraju SRM, Mes-Masson AM, Prentki M, Saad F. Elevated Expression of Glycerol-3-Phosphate Phosphatase as a Biomarker of Poor Prognosis and Aggressive Prostate Cancer. Cancers (Basel) 2021;13:1273. [PMID: 33805661 DOI: 10.3390/cancers13061273] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
76 [DOI: 10.1101/099796] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
77 Balluet M, Sizaire F, El Habouz Y, Walter T, Pont J, Giroux B, Bouchareb O, Tramier M, Pecreaux J. Neural network fast-classifies biological images through features selecting to power automated microscopy. J Microsc 2021. [PMID: 34623634 DOI: 10.1111/jmi.13062] [Reference Citation Analysis]
78 Salto-Tellez M, Maxwell P, Hamilton P. Artificial intelligence-the third revolution in pathology. Histopathology 2019;74:372-6. [PMID: 30270453 DOI: 10.1111/his.13760] [Cited by in Crossref: 51] [Cited by in F6Publishing: 44] [Article Influence: 25.5] [Reference Citation Analysis]
79 Zemouri R, Zerhouni N, Racoceanu D. Deep Learning in the Biomedical Applications: Recent and Future Status. Applied Sciences 2019;9:1526. [DOI: 10.3390/app9081526] [Cited by in Crossref: 39] [Cited by in F6Publishing: 5] [Article Influence: 13.0] [Reference Citation Analysis]
80 Ruusuvuori P, Valkonen M, Nykter M, Visakorpi T, Latonen L. Feature-based analysis of mouse prostatic intraepithelial neoplasia in histological tissue sections. J Pathol Inform 2016;7:5. [PMID: 26955503 DOI: 10.4103/2153-3539.175378] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
81 Bankhead P, Fernández JA, McArt DG, Boyle DP, Li G, Loughrey MB, Irwin GW, Harkin DP, James JA, McQuaid S, Salto-Tellez M, Hamilton PW. Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer. Lab Invest 2018;98:15-26. [PMID: 29251737 DOI: 10.1038/labinvest.2017.131] [Cited by in Crossref: 33] [Cited by in F6Publishing: 35] [Article Influence: 6.6] [Reference Citation Analysis]
82 Lawler M, Gavin A, Salto-Tellez M, Kennedy RD, Van Schaeybroeck S, Wilson RH, Harkin DP, Grayson M, Boyd RE, Hamilton PW, McArt DG, James J, Robson T, Ladner RD, Prise KM, O'Sullivan JM, Harrison T, Murray L, Johnston PG, Waugh DJ. Delivering a research-enabled multistakeholder partnership for enhanced patient care at a population level: The Northern Ireland Comprehensive Cancer Program. Cancer 2016;122:664-73. [PMID: 26695702 DOI: 10.1002/cncr.29814] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.4] [Reference Citation Analysis]
83 Hamilton PW, Wang Y, Boyd C, James JA, Loughrey MB, Hougton JP, Boyle DP, Kelly P, Maxwell P, McCleary D. Automated tumor analysis for molecular profiling in lung cancer. Oncotarget. 2015;6:27938-27952. [PMID: 26317646 DOI: 10.18632/oncotarget.4391] [Cited by in Crossref: 25] [Cited by in F6Publishing: 23] [Article Influence: 4.2] [Reference Citation Analysis]
84 Ng SB, Fan S, Choo SN, Hoppe M, Mai Phuong H, De Mel S, Jeyasekharan AD. Quantitative Analysis of a Multiplexed Immunofluorescence Panel in T-Cell Lymphoma. SLAS Technol 2018;23:252-8. [PMID: 29241019 DOI: 10.1177/2472630317747197] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 0.4] [Reference Citation Analysis]
85 Barker J, Hoogi A, Depeursinge A, Rubin DL. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med Image Anal 2016;30:60-71. [PMID: 26854941 DOI: 10.1016/j.media.2015.12.002] [Cited by in Crossref: 87] [Cited by in F6Publishing: 53] [Article Influence: 12.4] [Reference Citation Analysis]
86 Li G, Bankhead P, Dunne PD, O'Reilly PG, James JA, Salto-Tellez M, Hamilton PW, McArt DG. Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned. Brief Bioinform 2017;18:634-46. [PMID: 27255914 DOI: 10.1093/bib/bbw044] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
87 Hutchinson RA, Adams RA, McArt DG, Salto-Tellez M, Jasani B, Hamilton PW. Epidermal growth factor receptor immunohistochemistry: new opportunities in metastatic colorectal cancer. J Transl Med 2015;13:217. [PMID: 26149458 DOI: 10.1186/s12967-015-0531-z] [Cited by in Crossref: 24] [Cited by in F6Publishing: 21] [Article Influence: 3.4] [Reference Citation Analysis]
88 Um IH, Scott-Hayward L, Mackenzie M, Tan PH, Kanesvaran R, Choudhury Y, Caie PD, Tan MH, O'Donnell M, Leung S, Stewart GD, Harrison DJ. Computerized Image Analysis of Tumor Cell Nuclear Morphology Can Improve Patient Selection for Clinical Trials in Localized Clear Cell Renal Cell Carcinoma. J Pathol Inform 2020;11:35. [PMID: 33343995 DOI: 10.4103/jpi.jpi_13_20] [Reference Citation Analysis]
89 van der Graaff L, van Leenders GJLH, Boyaval F, Stallinga S. Computational imaging modalities for multi-focal whole-slide imaging systems. Appl Opt 2020;59:5967-82. [PMID: 32672740 DOI: 10.1364/AO.394290] [Reference Citation Analysis]
90 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]
91 Lujan G, Quigley JC, Hartman D, Parwani A, Roehmholdt B, Meter BV, Ardon O, Hanna MG, Kelly D, Sowards C, Montalto M, Bui M, Zarella MD, LaRosa V, Slootweg G, Retamero JA, Lloyd MC, Madory J, Bowman D. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J Pathol Inform 2021;12:17. [PMID: 34221633 DOI: 10.4103/jpi.jpi_67_20] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]