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For: Kather JN, Heij LR, Grabsch HI, Loeffler C, Echle A, Muti HS, Krause J, Niehues JM, Sommer KAJ, Bankhead P, Kooreman LFS, Schulte JJ, Cipriani NA, Buelow RD, Boor P, Ortiz-Brüchle NN, Hanby AM, Speirs V, Kochanny S, Patnaik A, Srisuwananukorn A, Brenner H, Hoffmeister M, van den Brandt PA, Jäger D, Trautwein C, Pearson AT, Luedde T. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer 2020;1:789-99. [PMID: 33763651 DOI: 10.1038/s43018-020-0087-6] [Cited by in Crossref: 52] [Cited by in F6Publishing: 42] [Article Influence: 26.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 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]
3 Foersch S, Eckstein M, Wagner DC, Gach F, Woerl AC, Geiger J, Glasner C, Schelbert S, Schulz S, Porubsky S, Kreft A, Hartmann A, Agaimy A, Roth W. Deep learning for diagnosis and survival prediction in soft tissue sarcoma. Ann Oncol 2021;32:1178-87. [PMID: 34139273 DOI: 10.1016/j.annonc.2021.06.007] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Bian C, Wang Y, Lu Z, An Y, Wang H, Kong L, Du Y, Tian J. ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment. Cancers (Basel) 2021;13:1659. [PMID: 33916145 DOI: 10.3390/cancers13071659] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Meuten DJ, Moore FM, Donovan TA, Bertram CA, Klopfleisch R, Foster RA, Smedley RC, Dark MJ, Milovancev M, Stromberg P, Williams BH, Aubreville M, Avallone G, Bolfa P, Cullen J, Dennis MM, Goldschmidt M, Luong R, Miller AD, Miller MA, Munday JS, Roccabianca P, Salas EN, Schulman FY, Laufer-Amorim R, Asakawa MG, Craig L, Dervisis N, Esplin DG, George JW, Hauck M, Kagawa Y, Kiupel M, Linder K, Meichner K, Marconato L, Oblak ML, Santos RL, Simpson RM, Tvedten H, Whitley D. International Guidelines for Veterinary Tumor Pathology: A Call to Action. Vet Pathol 2021;:3009858211013712. [PMID: 34282984 DOI: 10.1177/03009858211013712] [Reference Citation Analysis]
6 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]
7 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]
8 Speirs V. Quality Considerations When Using Tissue Samples for Biomarker Studies in Cancer Research. Biomark Insights 2021;16:11772719211009513. [PMID: 33958852 DOI: 10.1177/11772719211009513] [Reference Citation Analysis]
9 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]
10 Goertzen N, Pappesch R, Fassunke J, Brüning T, Ko YD, Schmidt J, Großerueschkamp F, Buettner R, Gerwert K. Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma. Am J Pathol 2021;191:1269-80. [PMID: 34004158 DOI: 10.1016/j.ajpath.2021.04.013] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Kuntz S, Krieghoff-Henning E, Kather JN, Jutzi T, Höhn J, Kiehl L, Hekler A, Alwers E, von Kalle C, Fröhling S, Utikal JS, Brenner H, Hoffmeister M, Brinker TJ. Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review. Eur J Cancer 2021;155:200-15. [PMID: 34391053 DOI: 10.1016/j.ejca.2021.07.012] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
12 Bilal M, Raza SEA, Azam A, Graham S, Ilyas M, Cree IA, Snead D, Minhas F, Rajpoot NM. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit Health 2021;3:e763-72. [PMID: 34686474 DOI: 10.1016/S2589-7500(21)00180-1] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Howard FM, Dolezal J, Kochanny S, Schulte J, Chen H, Heij L, Huo D, Nanda R, Olopade OI, Kather JN, Cipriani N, Grossman RL, Pearson AT. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat Commun 2021;12:4423. [PMID: 34285218 DOI: 10.1038/s41467-021-24698-1] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
14 Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 2021;5:555-70. [PMID: 33649564 DOI: 10.1038/s41551-020-00682-w] [Cited by in Crossref: 9] [Cited by in F6Publishing: 12] [Article Influence: 9.0] [Reference Citation Analysis]
15 Laury AR, Blom S, Ropponen T, Virtanen A, Carpén OM. Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone. Sci Rep 2021;11:19165. [PMID: 34580357 DOI: 10.1038/s41598-021-98480-0] [Reference Citation Analysis]
16 Dolezal JM, Trzcinska A, Liao CY, Kochanny S, Blair E, Agrawal N, Keutgen XM, Angelos P, Cipriani NA, Pearson AT. Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features. Mod Pathol 2021;34:862-74. [PMID: 33299111 DOI: 10.1038/s41379-020-00724-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Maron RC, Haggenmüller S, von Kalle C, Utikal JS, Meier F, Gellrich FF, Hauschild A, French LE, Schlaak M, Ghoreschi K, Kutzner H, Heppt MV, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Hekler A, Krieghoff-Henning E, Kather JN, Fröhling S, Lipka DB, Brinker TJ. Robustness of convolutional neural networks in recognition of pigmented skin lesions. Eur J Cancer 2021;145:81-91. [PMID: 33423009 DOI: 10.1016/j.ejca.2020.11.020] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
18 Kers J, Bülow RD, Klinkhammer BM, Breimer GE, Fontana F, Abiola AA, Hofstraat R, Corthals GL, Peters-Sengers H, Djudjaj S, von Stillfried S, Hölscher DL, Pieters TT, van Zuilen AD, Bemelman FJ, Nurmohamed AS, Naesens M, Roelofs JJTH, Florquin S, Floege J, Nguyen TQ, Kather JN, Boor P. Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study. Lancet Digit Health 2022;4:e18-26. [PMID: 34794930 DOI: 10.1016/S2589-7500(21)00211-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Kather JN, Calderaro J. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer. Nat Rev Gastroenterol Hepatol 2020;17:591-2. [DOI: 10.1038/s41575-020-0343-3] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
20 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]
21 Zeng T, Yu X, Chen Z. Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review. J Gastroenterol Hepatol 2021;36:832-40. [PMID: 33880762 DOI: 10.1111/jgh.15503] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Velmahos CS, Badgeley M, Lo YC. Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images. Cancer Med 2021;10:4805-13. [PMID: 34114376 DOI: 10.1002/cam4.4044] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Chen SB, Novoa RA. Artificial intelligence for dermatopathology: Current trends and the road ahead. Seminars in Diagnostic Pathology 2022. [DOI: 10.1053/j.semdp.2022.01.003] [Reference Citation Analysis]
24 Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer 2021. [PMID: 34663944 DOI: 10.1038/s41568-021-00408-3] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
25 Ringborg U, Berns A, Celis JE, Heitor M, Tabernero J, Schüz J, Baumann M, Henrique R, Aapro M, Basu P, Beets-Tan R, Besse B, Cardoso F, Carneiro F, van den Eede G, Eggermont A, Fröhling S, Galbraith S, Garralda E, Hanahan D, Hofmarcher T, Jönsson B, Kallioniemi O, Kásler M, Kondorosi E, Korbel J, Lacombe D, Carlos Machado J, Martin-Moreno JM, Meunier F, Nagy P, Nuciforo P, Oberst S, Oliveiera J, Papatriantafyllou M, Ricciardi W, Roediger A, Ryll B, Schilsky R, Scocca G, Seruca R, Soares M, Steindorf K, Valentini V, Voest E, Weiderpass E, Wilking N, Wren A, Zitvogel L. The Porto European Cancer Research Summit 2021. Mol Oncol 2021;15:2507-43. [PMID: 34515408 DOI: 10.1002/1878-0261.13078] [Reference Citation Analysis]
26 Sadhwani A, Chang HW, Behrooz A, Brown T, Auvigne-Flament I, Patel H, Findlater R, Velez V, Tan F, Tekiela K, Wulczyn E, Yi ES, Mermel CH, Hanks D, Chen PC, Kulig K, Batenchuk C, Steiner DF, Cimermancic P. Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images. Sci Rep 2021;11:16605. [PMID: 34400666 DOI: 10.1038/s41598-021-95747-4] [Reference Citation Analysis]
27 Zhang B, Yao K, Xu M, Wu J, Cheng C. Deep Learning Predicts EBV Status in Gastric Cancer Based on Spatial Patterns of Lymphocyte Infiltration. Cancers (Basel) 2021;13:6002. [PMID: 34885112 DOI: 10.3390/cancers13236002] [Reference Citation Analysis]
28 Wessels F, Schmitt M, Krieghoff-Henning E, Jutzi T, Worst TS, Waldbillig F, Neuberger M, Maron RC, Steeg M, Gaiser T, Hekler A, Utikal JS, von Kalle C, Fröhling S, Michel MS, Nuhn P, Brinker TJ. Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. BJU Int 2021;128:352-60. [PMID: 33706408 DOI: 10.1111/bju.15386] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Lafarge MW, Koelzer VH. Towards computationally efficient prediction of molecular signatures from routine histology images. Lancet Digit Health 2021;3:e752-3. [PMID: 34686475 DOI: 10.1016/S2589-7500(21)00232-6] [Reference Citation Analysis]
30 Galbraith NJ, Wood C, Steele CW. Targeting Metastatic Colorectal Cancer with Immune Oncological Therapies. Cancers (Basel) 2021;13:3566. [PMID: 34298779 DOI: 10.3390/cancers13143566] [Reference Citation Analysis]
31 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]
32 [DOI: 10.1101/2020.07.02.183814] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
33 Kacew AJ, Strohbehn GW, Saulsberry L, Laiteerapong N, Cipriani NA, Kather JN, Pearson AT. Artificial Intelligence Can Cut Costs While Maintaining Accuracy in Colorectal Cancer Genotyping. Front Oncol 2021;11:630953. [PMID: 34168975 DOI: 10.3389/fonc.2021.630953] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Muti HS, Heij LR, Keller G, Kohlruss M, Langer R, Dislich B, Cheong JH, Kim YW, Kim H, Kook MC, Cunningham D, Allum WH, Langley RE, Nankivell MG, Quirke P, Hayden JD, West NP, Irvine AJ, Yoshikawa T, Oshima T, Huss R, Grosser B, Roviello F, d'Ignazio A, Quaas A, Alakus H, Tan X, Pearson AT, Luedde T, Ebert MP, Jäger D, Trautwein C, Gaisa NT, Grabsch HI, Kather JN. Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. Lancet Digit Health 2021;3:e654-64. [PMID: 34417147 DOI: 10.1016/S2589-7500(21)00133-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
35 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]
36 Hong R, Liu W, DeLair D, Razavian N, Fenyö D. Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Rep Med 2021;2:100400. [PMID: 34622237 DOI: 10.1016/j.xcrm.2021.100400] [Reference Citation Analysis]
37 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]
38 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]
39 Gehrung M, Crispin-Ortuzar M, Berman AG, O'Donovan M, Fitzgerald RC, Markowetz F. Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat Med 2021;27:833-41. [PMID: 33859411 DOI: 10.1038/s41591-021-01287-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
40 Kochanny SE, Pearson AT. Academics as leaders in the cancer artificial intelligence revolution. Cancer 2021;127:664-71. [PMID: 33119903 DOI: 10.1002/cncr.33284] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
41 Puladi B, Ooms M, Kintsler S, Houschyar KS, Steib F, Modabber A, Hölzle F, Knüchel-Clarke R, Braunschweig T. Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma. Cancers (Basel) 2021;13:4409. [PMID: 34503218 DOI: 10.3390/cancers13174409] [Reference Citation Analysis]
42 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]
43 Howard FM, Villamar D, He G, Pearson AT, Nanda R. The emerging role of immune checkpoint inhibitors for the treatment of breast cancer. Expert Opin Investig Drugs 2021;:1-18. [PMID: 34569400 DOI: 10.1080/13543784.2022.1986002] [Reference Citation Analysis]
44 Coudray N, Tsirigos A. Deep learning links histology, molecular signatures and prognosis in cancer. Nat Cancer 2020;1:755-7. [DOI: 10.1038/s43018-020-0099-2] [Cited by in Crossref: 9] [Article Influence: 4.5] [Reference Citation Analysis]
45 Chauhan C, Gullapalli RR. Ethics of AI in Pathology: Current Paradigms and Emerging Issues. Am J Pathol 2021:S0002-9440(21)00303-5. [PMID: 34252382 DOI: 10.1016/j.ajpath.2021.06.011] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
46 Hunter MV, Moncada R, Weiss JM, Yanai I, White RM. Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface. Nat Commun 2021;12:6278. [PMID: 34725363 DOI: 10.1038/s41467-021-26614-z] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
47 [DOI: 10.1101/610311] [Cited by in Crossref: 13] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
48 Moxley-wyles B, Colling R, Verrill C. Artificial intelligence in pathology: an overview. Diagnostic Histopathology 2020;26:513-20. [DOI: 10.1016/j.mpdhp.2020.08.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
49 van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021;27:775-84. [PMID: 33990804 DOI: 10.1038/s41591-021-01343-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
50 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]
51 Loeffler CML, Ortiz Bruechle N, Jung M, Seillier L, Rose M, Laleh NG, Knuechel R, Brinker TJ, Trautwein C, Gaisa NT, Kather JN. Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing? Eur Urol Focus 2021:S2405-4569(21)00113-9. [PMID: 33895087 DOI: 10.1016/j.euf.2021.04.007] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
52 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]
53 Bustos A, Payá A, Torrubia A, Jover R, Llor X, Bessa X, Castells A, Carracedo Á, Alenda C. xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer. Biomolecules 2021;11:1786. [DOI: 10.3390/biom11121786] [Reference Citation Analysis]
54 Brück OE, Lallukka-Brück SE, Hohtari HR, Ianevski A, Ebeling FT, Kovanen PE, Kytölä SI, Aittokallio TA, Ramos PM, Porkka KV, Mustjoki SM. Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS. Blood Cancer Discov 2021;2:238-49. [PMID: 34661156 DOI: 10.1158/2643-3230.BCD-20-0162] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
55 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152. [PMID: 34579788 DOI: 10.1186/s13073-021-00968-x] [Reference Citation Analysis]
56 Rindal DB, Mabry PL. Leveraging Clinical Decision Support and Integrated Medical-Dental Electronic Health Records to Implementing Precision in Oral Cancer Risk Assessment and Preventive Intervention. J Pers Med 2021;11:832. [PMID: 34575609 DOI: 10.3390/jpm11090832] [Reference Citation Analysis]
57 Desbois M, Wang Y. Cancer-associated fibroblasts: Key players in shaping the tumor immune microenvironment. Immunol Rev 2021;302:241-58. [PMID: 34075584 DOI: 10.1111/imr.12982] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]