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For: Lee SH, Song IH, Jang HJ. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 2021;149:728-40. [PMID: 33851412 DOI: 10.1002/ijc.33599] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Saeed OAM, Mann SA, Luchini C, Huang K, Zhang S, Sen JD, Piredda ML, Wang M, Baldrige LA, Sperling RM, Curless KL, Cheng L. Evaluating mismatch repair deficiency for solid tumor immunotherapy eligibility: immunohistochemistry versus microsatellite molecular testing. Hum Pathol 2021;115:10-8. [PMID: 34052294 DOI: 10.1016/j.humpath.2021.05.009] [Reference Citation Analysis]
3 Echle A, Laleh NG, Schrammen PL, West NP, Trautwein C, Brinker TJ, Gruber SB, Buelow RD, Boor P, Grabsch HI, Quirke P, Kather JN. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review. ImmunoInformatics 2021;3-4:100008. [DOI: 10.1016/j.immuno.2021.100008] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
4 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]
5 Alam MR, Abdul-Ghafar J, Yim K, Thakur N, Lee SH, Jang HJ, Jung CK, Chong Y. Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review. Cancers (Basel) 2022;14:2590. [PMID: 35681570 DOI: 10.3390/cancers14112590] [Reference Citation Analysis]
6 Park JH, Kim EY, Luchini C, Eccher A, Tizaoui K, Shin JI, Lim BJ. Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review. Int J Mol Sci 2022;23:2462. [PMID: 35269607 DOI: 10.3390/ijms23052462] [Reference Citation Analysis]
7 Jang HJ, Song IH, Lee SH. Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images. Cancers (Basel) 2021;13:3811. [PMID: 34359712 DOI: 10.3390/cancers13153811] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 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]
9 Ranasinghe R, Mathai M, Zulli A. A synopsis of modern - day colorectal cancer: Where we stand. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 2022. [DOI: 10.1016/j.bbcan.2022.188699] [Reference Citation Analysis]