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For: Naik N, Madani A, Esteva A, Keskar NS, Press MF, Ruderman D, Agus DB, Socher R. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains. Nat Commun 2020;11:5727. [PMID: 33199723 DOI: 10.1038/s41467-020-19334-3] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Xue C, Chu Q, Zheng Q, Jiang S, Bao Z, Su Y, Lu J, Li L. Role of main RNA modifications in cancer: N6-methyladenosine, 5-methylcytosine, and pseudouridine. Signal Transduct Target Ther 2022;7:142. [PMID: 35484099 DOI: 10.1038/s41392-022-01003-0] [Reference Citation Analysis]
3 Tufail AB, Ma YK, Kaabar MKA, Martínez F, Junejo AR, Ullah I, Khan R. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Comput Math Methods Med 2021;2021:9025470. [PMID: 34754327 DOI: 10.1155/2021/9025470] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Xu Y, Cui H, Dong T, Zou B, Fan B, Li W, Wang S, Sun X, Yu J, Wang L. Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer. Front Oncol 2021;11:688706. [PMID: 34888228 DOI: 10.3389/fonc.2021.688706] [Reference Citation Analysis]
5 Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L. Artificial intelligence applied to breast pathology. Virchows Arch 2021. [PMID: 34791536 DOI: 10.1007/s00428-021-03213-3] [Reference Citation Analysis]
6 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]
7 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]
8 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]
9 Jahangir S, Loya A, Mushtaq S, Akhter N, Hashmi AA. CD117/c-KIT Expression in Phyllodes Tumor of the Breast and Its Correlation With Morphology and Clinical Outcome. Cureus 2021;13:e14914. [PMID: 34123614 DOI: 10.7759/cureus.14914] [Reference Citation Analysis]
10 Zheng K, Gu Q, Zhou D, Zhou M, Zhang L. Recent progress in surgical adhesives for biomedical applications. Smart Materials in Medicine 2022;3:41-65. [DOI: 10.1016/j.smaim.2021.11.004] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Cuypers E, Claes BSR, Biemans R, Lieuwes NG, Glunde K, Dubois L, Heeren RMA. 'On the Spot' Digital Pathology of Breast Cancer Based on Single-Cell Mass Spectrometry Imaging. Anal Chem 2022. [PMID: 35413180 DOI: 10.1021/acs.analchem.1c05238] [Reference Citation Analysis]
12 Gamble P, Jaroensri R, Wang H, Tan F, Moran M, Brown T, Flament-auvigne I, Rakha EA, Toss M, Dabbs DJ, Regitnig P, Olson N, Wren JH, Robinson C, Corrado GS, Peng LH, Liu Y, Mermel CH, Steiner DF, Chen PC. Determining breast cancer biomarker status and associated morphological features using deep learning. Commun Med 2021;1. [DOI: 10.1038/s43856-021-00013-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
13 Noguchi T, Ando T, Emoto S, Nozawa H, Kawai K, Sasaki K, Murono K, Kishikawa J, Ishi H, Yokoyama Y, Abe S, Nagai Y, Anzai H, Sonoda H, Hata K, Sasaki T, Ishihara S. Artificial Intelligence Program to Predict p53 Mutations in Ulcerative Colitis–Associated Cancer or Dysplasia. Inflammatory Bowel Diseases 2022. [DOI: 10.1093/ibd/izab350] [Reference Citation Analysis]