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For: 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]
Number Citing Articles
1 Lipkova J, Chen TY, Lu MY, Chen RJ, Shady M, Williams M, Wang J, Noor Z, Mitchell RN, Turan M, Coskun G, Yilmaz F, Demir D, Nart D, Basak K, Turhan N, Ozkara S, Banz Y, Odening KE, Mahmood F. Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies. Nat Med 2022;28:575-82. [PMID: 35314822 DOI: 10.1038/s41591-022-01709-2] [Reference Citation Analysis]
2 Macon WR. Computational histopathology and deep transfer learning: characterizing the molecular basis of tumor morphology. J Hematopathol 2020;13:203-4. [DOI: 10.1007/s12308-020-00425-5] [Reference Citation Analysis]
3 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 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]
5 Su A, Lee H, Tan X, Suarez CJ, Andor N, Nguyen Q, Ji HP. A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. NPJ Precis Oncol 2022;6:14. [PMID: 35236916 DOI: 10.1038/s41698-022-00252-0] [Reference Citation Analysis]
6 Kim RH, Nomikou S, Coudray N, Jour G, Dawood Z, Hong R, Esteva E, Sakellaropoulos T, Donnelly D, Moran U, Hatzimemos A, Weber JS, Razavian N, Aifantis I, Fenyo D, Snuderl M, Shapiro R, Berman RS, Osman I, Tsirigos A. Deep learning and pathomics analyses reveal cell nuclei as important features for mutation prediction of BRAF-mutated melanomas. J Invest Dermatol 2021:S0022-202X(21)02405-2. [PMID: 34757067 DOI: 10.1016/j.jid.2021.09.034] [Reference Citation Analysis]
7 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]
8 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]
9 Park J, Chung YR, Kong ST, Kim YW, Park H, Kim K, Kim DI, Jung KH. Aggregation of cohorts for histopathological diagnosis with deep morphological analysis. Sci Rep 2021;11:2876. [PMID: 33536550 DOI: 10.1038/s41598-021-82642-1] [Reference Citation Analysis]
10 Mann M, Kumar C, Zeng WF, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Syst 2021;12:759-70. [PMID: 34411543 DOI: 10.1016/j.cels.2021.06.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Pratapa A, Doron M, Caicedo JC. Image-based cell phenotyping with deep learning. Curr Opin Chem Biol 2021;65:9-17. [PMID: 34023800 DOI: 10.1016/j.cbpa.2021.04.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Tarabichi M, Demetter P, Craciun L, Maenhaut C, Detours V. Thyroid cancer under the scope of emerging technologies. Mol Cell Endocrinol 2021;541:111491. [PMID: 34740746 DOI: 10.1016/j.mce.2021.111491] [Reference Citation Analysis]
13 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]
14 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]
15 Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, Salto-Tellez M, Alwers E, Cifci D, Ghaffari Laleh N, Seibel T, Gray R, Hutchins GGA, Brenner H, van Treeck M, Yuan T, Brinker TJ, Chang-Claude J, Khader F, Schuppert A, Luedde T, Trautwein C, Muti HS, Foersch S, Hoffmeister M, Truhn D, Kather JN. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat Med 2022. [PMID: 35469069 DOI: 10.1038/s41591-022-01768-5] [Reference Citation Analysis]
16 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]
17 Hodis E, Triglia ET, Kwon JYH, Biancalani T, Zakka LR, Parkar S, Hütter JC, Buffoni L, Delorey TM, Phillips D, Dionne D, Nguyen LT, Schapiro D, Maliga Z, Jacobson CA, Hendel A, Rozenblatt-Rosen O, Mihm MC Jr, Garraway LA, Regev A. Stepwise-edited, human melanoma models reveal mutations' effect on tumor and microenvironment. Science 2022;376:eabi8175. [PMID: 35482859 DOI: 10.1126/science.abi8175] [Reference Citation Analysis]
18 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]
19 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]
20 Wang X, Xie T, Luo J, Zhou Z, Yu X, Guo X. Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment. Breast Cancer Res 2022;24. [DOI: 10.1186/s13058-022-01516-0] [Reference Citation Analysis]
21 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]
22 [DOI: 10.1101/2020.07.02.183814] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
23 Forest F, Laville D, Habougit C, Corbasson M, Bayle-Bleuez S, Tissot C, Fournel P, Tiffet O, Péoc'h M. Histopathological typing in diffuse malignant epithelioid mesothelioma: implication for prognosis and molecular basis. Pathology 2021:S0031-3025(21)00093-3. [PMID: 33965253 DOI: 10.1016/j.pathol.2021.01.010] [Reference Citation Analysis]
24 Kolmar L, Autour A, Ma X, Vergier B, Eduati F, Merten CA. Technological and computational advances driving high-throughput oncology. Trends in Cell Biology 2022. [DOI: 10.1016/j.tcb.2022.04.008] [Reference Citation Analysis]
25 Classe M, Lerousseau M, Scoazec JY, Deutsch E. Perspectives in pathomics in head and neck cancer. Curr Opin Oncol 2021;33:175-83. [PMID: 33782358 DOI: 10.1097/CCO.0000000000000731] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
26 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]
27 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]
28 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]
29 Gambardella V, Alfaro-Cervelló C, Cejalvo JM, Tapia M, Cervantes A. In the literature: August 2021. ESMO Open 2021;6:100247. [PMID: 34411970 DOI: 10.1016/j.esmoop.2021.100247] [Reference Citation Analysis]
30 McGenity C, Treanor D. Guidelines for clinical trials using artificial intelligence - SPIRIT-AI and CONSORT-AI. J Pathol 2021;253:14-6. [PMID: 33016344 DOI: 10.1002/path.5565] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
31 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]