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For: Sha L, Osinski BL, Ho IY, Tan TL, Willis C, Weiss H, Beaubier N, Mahon BM, Taxter TJ, Yip SSF. Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images. J Pathol Inform 2019;10:24. [PMID: 31523482 DOI: 10.4103/jpi.jpi_24_19] [Cited by in Crossref: 24] [Cited by in F6Publishing: 23] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, Di Tommaso L. Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology. Cells 2021;10:787. [PMID: 33918173 DOI: 10.3390/cells10040787] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Holland L, Wei D, Olson KA, Mitra A, Graff JP, Jones AD, Durbin-Johnson B, Mitra AD, Rashidi HH. Limited Number of Cases May Yield Generalizable Models, a Proof of Concept in Deep Learning for Colon Histology. J Pathol Inform 2020;11:5. [PMID: 32175170 DOI: 10.4103/jpi.jpi_49_19] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Diao JA, Wang JK, Chui WF, Mountain V, Gullapally SC, Srinivasan R, Mitchell RN, Glass B, Hoffman S, Rao SK, Maheshwari C, Lahiri A, Prakash A, McLoughlin R, Kerner JK, Resnick MB, Montalto MC, Khosla A, Wapinski IN, Beck AH, Elliott HL, Taylor-Weiner A. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat Commun 2021;12:1613. [PMID: 33712588 DOI: 10.1038/s41467-021-21896-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
4 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]
5 Huang Z, Chen L, Lv L, Fu C, Jin Y, Zheng Q, Wang B, Ye Q, Fang Q, Li Y. A New AI-assisted Scoring System for PD-L1 expression in NSCLC. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.106829] [Reference Citation Analysis]
6 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]
7 Gonzalez D, Dietz RL, Pantanowitz L. Feasibility of a deep learning algorithm to distinguish large cell neuroendocrine from small cell lung carcinoma in cytology specimens. Cytopathology 2020;31:426-31. [PMID: 32246504 DOI: 10.1111/cyt.12829] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
8 Chen Y, Yang H, Cheng Z, Chen L, Peng S, Wang J, Yang M, Lin C, Chen Y, Wang Y, Huang L, Chen Y, Li W, Ke Z. A whole-slide image (WSI)-based immunohistochemical feature prediction system improves the subtyping of lung cancer. Lung Cancer 2022;165:18-27. [DOI: 10.1016/j.lungcan.2022.01.005] [Reference Citation Analysis]
9 Pagni F, Malapelle U, Doglioni C, Fontanini G, Fraggetta F, Graziano P, Marchetti A, Guerini Rocco E, Pisapia P, Vigliar EV, Buttitta F, Jaconi M, Fusco N, Barberis M, Troncone G. Digital Pathology and PD-L1 Testing in Non Small Cell Lung Cancer: A Workshop Record. Cancers (Basel) 2020;12:E1800. [PMID: 32635634 DOI: 10.3390/cancers12071800] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
10 Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, Lennerz J, Eschrich J, Kazdal D, Schirmacher P, Wagner AH, Tacke F, Capper D, Müller KR, Klauschen F. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 2021:S1044-579X(21)00034-1. [PMID: 33631297 DOI: 10.1016/j.semcancer.2021.02.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
11 Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer 2021;124:686-96. [PMID: 33204028 DOI: 10.1038/s41416-020-01122-x] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 10.0] [Reference Citation Analysis]
12 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]
13 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]
14 Zhu Y, Liu YL, Feng Y, Yang XY, Zhang J, Chang DD, Wu X, Tian X, Tang KJ, Xie CM, Guo YB, Feng ST, Ke ZF. A CT-derived deep neural network predicts for programmed death ligand-1 expression status in advanced lung adenocarcinomas. Ann Transl Med 2020;8:930. [PMID: 32953730 DOI: 10.21037/atm-19-4690] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
15 Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020;40:154-166. [PMID: 32277744 DOI: 10.1002/cac2.12012] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
16 Lara H, Li Z, Abels E, Aeffner F, Bui MM, ElGabry EA, Kozlowski C, Montalto MC, Parwani AV, Zarella MD, Bowman D, Rimm D, Pantanowitz L. Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association. Appl Immunohistochem Mol Morphol 2021;29:479-93. [PMID: 33734106 DOI: 10.1097/PAI.0000000000000930] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
17 Hildebrand LA, Pierce CJ, Dennis M, Paracha M, Maoz A. Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer. Cancers (Basel). 2021;13. [PMID: 33494280 DOI: 10.3390/cancers13030391] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
18 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]
19 Laleh NG, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102474] [Reference Citation Analysis]
20 Nagy M, Radakovich N, Nazha A. Machine Learning in Oncology: What Should Clinicians Know? JCO Clin Cancer Inform 2020;4:799-810. [PMID: 32926637 DOI: 10.1200/CCI.20.00049] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
21 Loeffler CML, Gaisa NT, Muti HS, van Treeck M, Echle A, Ghaffari Laleh N, Trautwein C, Heij LR, Grabsch HI, Ortiz Bruechle N, Kather JN. Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types. Front Genet 2022;12:806386. [DOI: 10.3389/fgene.2021.806386] [Reference Citation Analysis]
22 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]
23 Sakamoto T, Furukawa T, Lami K, Pham HHN, Uegami W, Kuroda K, Kawai M, Sakanashi H, Cooper LAD, Bychkov A, Fukuoka J. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res 2020;9:2255-76. [PMID: 33209648 DOI: 10.21037/tlcr-20-591] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
24 Yang H, Chen L, Cheng Z, Yang M, Wang J, Lin C, Wang Y, Huang L, Chen Y, Peng S, Ke Z, Li W. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med 2021;19:80. [PMID: 33775248 DOI: 10.1186/s12916-021-01953-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Radakovich N, Nagy M, Nazha A. Machine learning in haematological malignancies. Lancet Haematol 2020;7:e541-50. [PMID: 32589980 DOI: 10.1016/S2352-3026(20)30121-6] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
26 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]
27 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]
28 [DOI: 10.1101/2020.09.07.20189977] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
29 Shim WS, Yim K, Kim TJ, Sung YE, Lee G, Hong JH, Chun SH, Kim S, An HJ, Na SJ, Kim JJ, Moon MH, Moon SW, Park S, Hong SA, Ko YH. DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks. Cancers (Basel) 2021;13:3308. [PMID: 34282757 DOI: 10.3390/cancers13133308] [Reference Citation Analysis]