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For: Schmauch B, Romagnoni A, Pronier E, Saillard C, Maillé P, Calderaro J, Kamoun A, Sefta M, Toldo S, Zaslavskiy M, Clozel T, Moarii M, Courtiol P, Wainrib G. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat Commun 2020;11:3877. [PMID: 32747659 DOI: 10.1038/s41467-020-17678-4] [Cited by in Crossref: 40] [Cited by in F6Publishing: 39] [Article Influence: 20.0] [Reference Citation Analysis]
Number Citing Articles
1 Alix-Panabieres C, Magliocco A, Cortes-Hernandez LE, Eslami-S Z, Franklin D, Messina JL. Detection of cancer metastasis: past, present and future. Clin Exp Metastasis 2021. [PMID: 33961169 DOI: 10.1007/s10585-021-10088-w] [Reference Citation Analysis]
2 Lee K, Lockhart JH, Xie M, Chaudhary R, Slebos RJC, Flores ER, Chung CH, Tan AC. Deep Learning of Histopathology Images at the Single Cell Level. Front Artif Intell 2021;4:754641. [PMID: 34568816 DOI: 10.3389/frai.2021.754641] [Reference Citation Analysis]
3 Uthamacumaran A. A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks. Patterns (N Y) 2021;2:100226. [PMID: 33982021 DOI: 10.1016/j.patter.2021.100226] [Reference Citation Analysis]
4 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]
5 Jackson BR, Ye Y, Crawford JM, Becich MJ, Roy S, Botkin JR, de Baca ME, Pantanowitz L. The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice. Acad Pathol 2021;8:2374289521990784. [PMID: 33644301 DOI: 10.1177/2374289521990784] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Khan AN, Ihalage AA, Ma Y, Liu B, Liu Y, Hao Y. Deep learning framework for subject-independent emotion detection using wireless signals. PLoS One 2021;16:e0242946. [PMID: 33534826 DOI: 10.1371/journal.pone.0242946] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]
7 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]
8 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]
9 Schneider L, Laiouar-Pedari S, Kuntz S, Krieghoff-Henning E, Hekler A, Kather JN, Gaiser T, Fröhling S, Brinker TJ. Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. Eur J Cancer 2022;160:80-91. [PMID: 34810047 DOI: 10.1016/j.ejca.2021.10.007] [Reference Citation Analysis]
10 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]
11 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]
12 Tavolara TE, Niazi MKK, Gower AC, Ginese M, Beamer G, Gurcan MN. Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice. EBioMedicine 2021;67:103388. [PMID: 34000621 DOI: 10.1016/j.ebiom.2021.103388] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 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]
14 Koteluk O, Wartecki A, Mazurek S, Kołodziejczak I, Mackiewicz A. How Do Machines Learn? Artificial Intelligence as a New Era in Medicine. J Pers Med 2021;11:32. [PMID: 33430240 DOI: 10.3390/jpm11010032] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
15 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]
16 Carrillo-perez F, Morales JC, Castillo-secilla D, Gevaert O, Rojas I, Herrera LJ. Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis. JPM 2022;12:601. [DOI: 10.3390/jpm12040601] [Reference Citation Analysis]
17 Tan MS, Cheah PL, Chin AV, Looi LM, Chang SW. A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach. Comput Biol Med 2021;139:104947. [PMID: 34678481 DOI: 10.1016/j.compbiomed.2021.104947] [Reference Citation Analysis]
18 Echle A, Ghaffari Laleh N, Quirke P, Grabsch HI, Muti HS, Saldanha OL, Brockmoeller SF, van den Brandt PA, Hutchins GGA, Richman SD, Horisberger K, Galata C, Ebert MP, Eckardt M, Boutros M, Horst D, Reissfelder C, Alwers E, Brinker TJ, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Chang-Claude J, Brenner H, Trautwein C, Boor P, Jaeger D, Gaisa NT, Hoffmeister M, West NP, Kather JN. Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application. ESMO Open 2022;7:100400. [PMID: 35247870 DOI: 10.1016/j.esmoop.2022.100400] [Reference Citation Analysis]
19 Huang W, Randhawa R, Jain P, Hubbard S, Eickhoff J, Kummar S, Wilding G, Basu H, Roy R. A Novel Artificial Intelligence-Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers. JCO Clin Cancer Inform 2022;6:e2100131. [PMID: 35192404 DOI: 10.1200/CCI.21.00131] [Reference Citation Analysis]
20 Kleppe A. Area under the curve may hide poor generalisation to external datasets. ESMO Open 2022;7:100429. [PMID: 35397433 DOI: 10.1016/j.esmoop.2022.100429] [Reference Citation Analysis]
21 Bera K, Katz I, Madabhushi A. Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO Clin Cancer Inform 2020;4:1039-50. [PMID: 33166198 DOI: 10.1200/CCI.20.00110] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Jin W, Luo Q. When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy evaluation. Computers in Biology and Medicine 2022;145:105499. [DOI: 10.1016/j.compbiomed.2022.105499] [Reference Citation Analysis]
23 Werner J, Kronberg RM, Stachura P, Ostermann PN, Müller L, Schaal H, Bhatia S, Kather JN, Borkhardt A, Pandyra AA, Lang KS, Lang PA. Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2. Viruses 2021;13:610. [PMID: 33918368 DOI: 10.3390/v13040610] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Wang X, Wang H, Liu D, Wang N, He D, Wu Z, Zhu X, Wen X, Li X, Li J, Wang Z. Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment. OncoImmunology 2022;11:2043662. [DOI: 10.1080/2162402x.2022.2043662] [Reference Citation Analysis]
25 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]
26 Sántha P, Lenggenhager D, Finstadsveen A, Dorg L, Tøndel K, Amrutkar M, Gladhaug IP, Verbeke C. Morphological Heterogeneity in Pancreatic Cancer Reflects Structural and Functional Divergence. Cancers (Basel) 2021;13:895. [PMID: 33672734 DOI: 10.3390/cancers13040895] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
27 Zhu Y, Brettin T, Xia F, Partin A, Shukla M, Yoo H, Evrard YA, Doroshow JH, Stevens RL. Converting tabular data into images for deep learning with convolutional neural networks. Sci Rep 2021;11:11325. [PMID: 34059739 DOI: 10.1038/s41598-021-90923-y] [Reference Citation Analysis]
28 Rinoldi C, Zargarian SS, Nakielski P, Li X, Liguori A, Petronella F, Presutti D, Wang Q, Costantini M, De Sio L, Gualandi C, Ding B, Pierini F. Nanotechnology-Assisted RNA Delivery: From Nucleic Acid Therapeutics to COVID-19 Vaccines. Small Methods 2021;:2100402. [PMID: 34514087 DOI: 10.1002/smtd.202100402] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
29 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]
30 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]
31 Kang M, Ko E, Mersha TB. A roadmap for multi-omics data integration using deep learning. Brief Bioinform 2021:bbab454. [PMID: 34791014 DOI: 10.1093/bib/bbab454] [Reference Citation Analysis]
32 Eslami M, Espah-Borujeni A, Eramian H, Weston M, Zheng G, Urrutia J, Corbet C, Becker D, Maschhoff P, Clowers K, Cristafaro A, Hosseini HD, Gordon DB, Dorfan Y, Singer J, Vaugh M, Gaffney N, Fonner J, Stubbs J, Voigt CA, Yeung E. Prediction of Whole-Cell Transcriptional Response with Machine Learning. Bioinformatics 2021:btab676. [PMID: 34570169 DOI: 10.1093/bioinformatics/btab676] [Reference Citation Analysis]
33 Schumaker G, Becker A, An G, Badylak S, Johnson S, Jiang P, Vodovotz Y, Cockrell RC. Optical Biopsy Using a Neural Network to Predict Gene Expression From Photos of Wounds. J Surg Res 2022;270:547-54. [PMID: 34826690 DOI: 10.1016/j.jss.2021.10.017] [Reference Citation Analysis]
34 Del Giudice M, Peirone S, Perrone S, Priante F, Varese F, Tirtei E, Fagioli F, Cereda M. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology. Int J Mol Sci 2021;22:4563. [PMID: 33925407 DOI: 10.3390/ijms22094563] [Reference Citation Analysis]
35 Giglia G, Gambino G, Sardo P. Through Predictive Personalized Medicine. Brain Sci 2020;10:E594. [PMID: 32872094 DOI: 10.3390/brainsci10090594] [Reference Citation Analysis]
36 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]
37 Beaufrère A, Calderaro J, Paradis V. Combined hepatocellular-cholangiocarcinoma: An update. J Hepatol 2021;74:1212-24. [PMID: 33545267 DOI: 10.1016/j.jhep.2021.01.035] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
38 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]
39 Li F, Dong S, Leier A, Han M, Guo X, Xu J, Wang X, Pan S, Jia C, Zhang Y, Webb GI, Coin LJM, Li C, Song J. Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Brief Bioinform 2021:bbab461. [PMID: 34729589 DOI: 10.1093/bib/bbab461] [Reference Citation Analysis]
40 Javaid A, Shahab O, Adorno W, Fernandes P, May E, Syed S. Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases. Inflamm Bowel Dis 2021:izab187. [PMID: 34417815 DOI: 10.1093/ibd/izab187] [Reference Citation Analysis]
41 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]
42 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]
43 Wang X, Zou C, Zhang Y, Li X, Wang C, Ke F, Chen J, Wang W, Wang D, Xu X, Xie L, Zhang Y. Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images. Front Genet 2021;12:661109. [PMID: 34354733 DOI: 10.3389/fgene.2021.661109] [Reference Citation Analysis]
44 Morilla I. Repairing the human with artificial intelligence in oncology. Artif Intell Cancer 2021; 2(5): 60-68 [DOI: 10.35713/aic.v2.i5.60] [Reference Citation Analysis]
45 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]
46 Lewis SM, Asselin-Labat ML, Nguyen Q, Berthelet J, Tan X, Wimmer VC, Merino D, Rogers KL, Naik SH. Spatial omics and multiplexed imaging to explore cancer biology. Nat Methods 2021. [PMID: 34341583 DOI: 10.1038/s41592-021-01203-6] [Reference Citation Analysis]
47 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]
48 Cancian P, Cortese N, Donadon M, Di Maio M, Soldani C, Marchesi F, Savevski V, Santambrogio MD, Cerina L, Laino ME, Torzilli G, Mantovani A, Terracciano L, Roncalli M, Di Tommaso L. Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis. Cancers (Basel) 2021;13:3313. [PMID: 34282750 DOI: 10.3390/cancers13133313] [Reference Citation Analysis]
49 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]
50 Levy-Jurgenson A, Tekpli X, Kristensen VN, Yakhini Z. Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci Rep 2020;10:18802. [PMID: 33139755 DOI: 10.1038/s41598-020-75708-z] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
51 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]
52 Freyre CAC, Spiegel S, Gubser Keller C, Vandemeulebroecke M, Hoefling H, Dubost V, Cörek E, Moulin P, Hossain I. Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology-A Machine Learning Approach to Histopathology. Toxicol Pathol 2021;49:798-814. [PMID: 33625320 DOI: 10.1177/0192623320987202] [Reference Citation Analysis]
53 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]
54 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]
55 Sabbih GO, Danquah MK. Neuroblastoma GD2 Expression and Computational Analysis of Aptamer-Based Bioaffinity Targeting. Int J Mol Sci 2021;22:9101. [PMID: 34445807 DOI: 10.3390/ijms22169101] [Reference Citation Analysis]
56 Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 2021. [PMID: 34518686 DOI: 10.1038/s41580-021-00407-0] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]