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For: Koelzer VH, Sirinukunwattana K, Rittscher J, Mertz KD. Precision immunoprofiling by image analysis and artificial intelligence. Virchows Arch 2019;474:511-22. [PMID: 30470933 DOI: 10.1007/s00428-018-2485-z] [Cited by in Crossref: 49] [Cited by in F6Publishing: 43] [Article Influence: 12.3] [Reference Citation Analysis]
Number Citing Articles
1 Cornish TC. Clinical Application of Image Analysis in Pathology. Adv Anat Pathol 2020;27:227-35. [PMID: 32467397 DOI: 10.1097/PAP.0000000000000263] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
2 Kalra J, Baker J, Song J, Kyle A, Minchinton A, Bally M. Inter-Metastatic Heterogeneity of Tumor Marker Expression and Microenvironment Architecture in a Preclinical Cancer Model. Int J Mol Sci 2021;22:6336. [PMID: 34199298 DOI: 10.3390/ijms22126336] [Reference Citation Analysis]
3 Wu AM, Pandit-Taskar N. ImmunoPET: harnessing antibodies for imaging immune cells. Mol Imaging Biol 2021. [PMID: 34550529 DOI: 10.1007/s11307-021-01652-7] [Reference Citation Analysis]
4 Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Ther 2021;221:107753. [PMID: 33259885 DOI: 10.1016/j.pharmthera.2020.107753] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 3.5] [Reference Citation Analysis]
5 Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14(1): 124-152 [DOI: 10.4251/wjgo.v14.i1.124] [Reference Citation Analysis]
6 Capobianco E. High-dimensional role of AI and machine learning in cancer research. Br J Cancer. [DOI: 10.1038/s41416-021-01689-z] [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 Kim H, Jang J, Heo YJ, Kim B, Jung H, Jang Y, Kang SY, Kim ST, Lee J, Kang WK, Kim K. PD-L1 expression in gastric cancer determined by digital image analyses: pitfalls and correlation with pathologist interpretation. Virchows Arch 2020;476:243-50. [DOI: 10.1007/s00428-019-02653-2] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
9 Yeong J, Suteja L, Simoni Y, Lau KW, Tan AC, Li HH, Lim S, Loh JH, Wee FYT, Nerurkar SN, Takano A, Tan EH, Lim TKH, Newell EW, Tan DSW. Intratumoral CD39+CD8+ T Cells Predict Response to Programmed Cell Death Protein-1 or Programmed Death Ligand-1 Blockade in Patients With NSCLC. J Thorac Oncol 2021;16:1349-58. [PMID: 33975004 DOI: 10.1016/j.jtho.2021.04.016] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
10 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]
11 Iaccarino A, Salatiello M, Migliatico I, De Luca C, Gragnano G, Russo M, Bellevicine C, Malapelle U, Troncone G, Vigliar E. PD-L1 and beyond: Immuno-oncology in cytopathology. Cytopathology 2021;32:596-603. [PMID: 33955097 DOI: 10.1111/cyt.12982] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
12 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]
13 Abdullahi Sidi F, Bingham V, Craig SG, McQuaid S, James J, Humphries MP, Salto-Tellez M. PD-L1 Multiplex and Quantitative Image Analysis for Molecular Diagnostics. Cancers (Basel) 2020;13:E29. [PMID: 33374775 DOI: 10.3390/cancers13010029] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
14 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]
15 Sivanesan U, van der Pol CB. Editorial for "Non-Contrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in Patients With Pancreatic Ductal Adenocarcinoma". J Magn Reson Imaging 2021. [PMID: 33963800 DOI: 10.1002/jmri.27674] [Reference Citation Analysis]
16 Valla V, Alzabin S, Koukoura A, Lewis A, Nielsen AA, Vassiliadis E. Companion Diagnostics: State of the Art and New Regulations. Biomark Insights 2021;16:11772719211047763. [PMID: 34658618 DOI: 10.1177/11772719211047763] [Reference Citation Analysis]
17 Paijens ST, Vledder A, Loiero D, Duiker EW, Bart J, Hendriks AM, Jalving M, Workel HH, Hollema H, Werner N, Plat A, Wisman GBA, Yigit R, Arts H, Kruse AJ, de Lange NM, Koelzer VH, de Bruyn M, Nijman HW. Prognostic image-based quantification of CD8CD103 T cell subsets in high-grade serous ovarian cancer patients. Oncoimmunology 2021;10:1935104. [PMID: 34123576 DOI: 10.1080/2162402X.2021.1935104] [Reference Citation Analysis]
18 Li LR, Du B, Liu HQ, Chen C. Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. Front Oncol 2020;10:604051. [PMID: 33634025 DOI: 10.3389/fonc.2020.604051] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Wu JQ, Horeweg N, de Bruyn M, Nout RA, Jürgenliemk-schulz IM, Lutgens LCHW, Jobsen JJ, van der Steen-banasik EM, Nijman HW, Smit VTHBM, Bosse T, Creutzberg CL, Koelzer VH. Automated causal inference in application to randomized controlled clinical trials. Nat Mach Intell. [DOI: 10.1038/s42256-022-00470-y] [Reference Citation Analysis]
20 Hofman P, Badoual C, Henderson F, Berland L, Hamila M, Long-Mira E, Lassalle S, Roussel H, Hofman V, Tartour E, Ilié M. Multiplexed Immunohistochemistry for Molecular and Immune Profiling in Lung Cancer-Just About Ready for Prime-Time? Cancers (Basel) 2019;11:E283. [PMID: 30818873 DOI: 10.3390/cancers11030283] [Cited by in Crossref: 45] [Cited by in F6Publishing: 39] [Article Influence: 15.0] [Reference Citation Analysis]
21 Yu Y, Tse K, Lee HHY, Chow K, Tsang H, Wong RWC, Cheung ETY, Cheuk W, Lee VWK, Chan W, Wong AST, Loong HHF, Chan KKL, Ngan HYS, Cheung ANY, Ip PPC. Predictive biomarkers and tumor microenvironment in female genital melanomas: a multi-institutional study of 55 cases. Mod Pathol 2020;33:138-52. [DOI: 10.1038/s41379-019-0345-2] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
22 Lee C, Ren YJ, Marella M, Wang M, Hartke J, Couto SS. Multiplex immunofluorescence staining and image analysis assay for diffuse large B cell lymphoma. Journal of Immunological Methods 2020;478:112714. [DOI: 10.1016/j.jim.2019.112714] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 6.5] [Reference Citation Analysis]
23 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]
24 Pusztaszeri MP, Bongiovanni M, Brimo F. Do we need PD-L1 as a biomarker for thyroid cytologic and histologic specimens? Cancer Cytopathol 2020;128:160-5. [PMID: 31821734 DOI: 10.1002/cncy.22223] [Reference Citation Analysis]
25 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]
26 Belanger MC, Anbaei P, Dunn AF, Kinman AWL, Pompano RR. Spatially Resolved Analytical Chemistry in Intact, Living Tissues. Anal Chem 2020;92:15255-62. [PMID: 33201681 DOI: 10.1021/acs.analchem.0c03625] [Reference Citation Analysis]
27 Nestarenkaite A, Fadhil W, Rasmusson A, Susanti S, Hadjimichael E, Laurinaviciene A, Ilyas M, Laurinavicius A. Immuno-Interface Score to Predict Outcome in Colorectal Cancer Independent of Microsatellite Instability Status. Cancers (Basel) 2020;12:E2902. [PMID: 33050344 DOI: 10.3390/cancers12102902] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
28 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]
29 Chapman JA, Lee LMJ, Swailes NT. From Scope to Screen: The Evolution of Histology Education. Adv Exp Med Biol 2020;1260:75-107. [PMID: 33211308 DOI: 10.1007/978-3-030-47483-6_5] [Reference Citation Analysis]
30 Seyhan AA, Carini C. Are innovation and new technologies in precision medicine paving a new era in patients centric care? J Transl Med. 2019;17:114. [PMID: 30953518 DOI: 10.1186/s12967-019-1864-9] [Cited by in Crossref: 51] [Cited by in F6Publishing: 32] [Article Influence: 17.0] [Reference Citation Analysis]
31 Cheung HMC, Rubin D. Challenges and opportunities for artificial intelligence in oncological imaging. Clin Radiol 2021;76:728-36. [PMID: 33902889 DOI: 10.1016/j.crad.2021.03.009] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
32 Leong TKM, Lo WS, Lee WEZ, Tan B, Lee XZ, Lee LWJN, Lee JJ, Suresh N, Loo LH, Szu E, Yeong J. Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space. Adv Drug Deliv Rev 2021;177:113959. [PMID: 34481035 DOI: 10.1016/j.addr.2021.113959] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Baba Y, Nomoto D, Okadome K, Ishimoto T, Iwatsuki M, Miyamoto Y, Yoshida N, Baba H. Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma. Cancer Sci. 2020;111:3132-3141. [PMID: 32579769 DOI: 10.1111/cas.14541] [Cited by in Crossref: 12] [Cited by in F6Publishing: 22] [Article Influence: 6.0] [Reference Citation Analysis]
34 Ahmad Z, Rahim S, Zubair M, Abdul-Ghafar J. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagn Pathol 2021;16:24. [PMID: 33731170 DOI: 10.1186/s13000-021-01085-4] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
35 Malherbe K. Tumor Microenvironment and the Role of Artificial Intelligence in Breast Cancer Detection and Prognosis. Am J Pathol 2021;191:1364-73. [PMID: 33639101 DOI: 10.1016/j.ajpath.2021.01.014] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
36 Failmezger H, Zwing N, Tresch A, Korski K, Schmich F. Computational Tumor Infiltration Phenotypes Enable the Spatial and Genomic Analysis of Immune Infiltration in Colorectal Cancer. Front Oncol 2021;11:552331. [PMID: 33791196 DOI: 10.3389/fonc.2021.552331] [Reference Citation Analysis]
37 Vipond O, Bull JA, Macklin PS, Tillmann U, Pugh CW, Byrne HM, Harrington HA. Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors. Proc Natl Acad Sci U S A 2021;118:e2102166118. [PMID: 34625491 DOI: 10.1073/pnas.2102166118] [Reference Citation Analysis]
38 Maibach F, Sadozai H, Seyed Jafari SM, Hunger RE, Schenk M. Tumor-Infiltrating Lymphocytes and Their Prognostic Value in Cutaneous Melanoma. Front Immunol 2020;11:2105. [PMID: 33013886 DOI: 10.3389/fimmu.2020.02105] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 9.5] [Reference Citation Analysis]
39 Valous NA, Moraleda RR, Jäger D, Zörnig I, Halama N. Interrogating the microenvironmental landscape of tumors with computational image analysis approaches. Semin Immunol 2020;48:101411. [PMID: 33168423 DOI: 10.1016/j.smim.2020.101411] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
40 Wu LL, Wang JL, Huang W, Liu X, Huang YY, Zeng J, Cui CY, Lu JB, Lin P, Long H, Zhang LJ, Wei J, Lu Y, Ma GW. Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis. Front Oncol 2021;11:565755. [PMID: 33912439 DOI: 10.3389/fonc.2021.565755] [Reference Citation Analysis]
41 Biswas N, Chakrabarti S. Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer. Front Oncol 2020;10:588221. [PMID: 33154949 DOI: 10.3389/fonc.2020.588221] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
42 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]
43 Sobhani F, Robinson R, Hamidinekoo A, Roxanis I, Somaiah N, Yuan Y. Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology. Biochim Biophys Acta Rev Cancer 2021;1875:188520. [PMID: 33561505 DOI: 10.1016/j.bbcan.2021.188520] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
44 Wang N, Wang R, Zhang X, Li X, Liang Y, Ding Z. Spatially-resolved proteomics and transcriptomics: An emerging digital spatial profiling approach for tumor microenvironment. Vis Cancer Med 2021;2:1. [DOI: 10.1051/vcm/2020002] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
45 Natali EN, Babrak LM, Miho E. Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics. Front Immunol 2021;12:574411. [PMID: 34211454 DOI: 10.3389/fimmu.2021.574411] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
46 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]
47 Rafael TS, de Vries HM, Ottenhof SR, Hofland I, Broeks A, de Jong J, Bekers E, Horenblas S, de Menezes RX, Jordanova ES, Brouwer OR. Distinct Patterns of Myeloid Cell Infiltration in Patients With hrHPV-Positive and hrHPV-Negative Penile Squamous Cell Carcinoma: The Importance of Assessing Myeloid Cell Densities Within the Spatial Context of the Tumor. Front Immunol 2021;12:682030. [PMID: 34194435 DOI: 10.3389/fimmu.2021.682030] [Reference Citation Analysis]