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Cited by in F6Publishing
For: 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]
Number Citing Articles
1 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]
2 Hu LF, Lan HR, Huang D, Li XM, Jin KT. Personalized Immunotherapy in Colorectal Cancers: Where Do We Stand? Front Oncol 2021;11:769305. [PMID: 34888246 DOI: 10.3389/fonc.2021.769305] [Reference Citation Analysis]
3 Kumari S, Advani D, Sharma S, Ambasta RK, Kumar P. Combinatorial therapy in tumor microenvironment: Where do we stand? Biochim Biophys Acta Rev Cancer 2021;1876:188585. [PMID: 34224836 DOI: 10.1016/j.bbcan.2021.188585] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
4 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]
5 Gilson P, Merlin JL, Harlé A. Detection of Microsatellite Instability: State of the Art and Future Applications in Circulating Tumour DNA (ctDNA). Cancers (Basel) 2021;13:1491. [PMID: 33804907 DOI: 10.3390/cancers13071491] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27(21): 2681-2709 [PMID: 34135549 DOI: 10.3748/wjg.v27.i21.2681] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Kim BW, Choi MC, Kim MK, Lee JW, Kim MT, Noh JJ, Park H, Jung SG, Joo WD, Song SH, Lee C. Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers. Cancers (Basel) 2021;13:5670. [PMID: 34830824 DOI: 10.3390/cancers13225670] [Reference Citation Analysis]
8 Liu Q, Ding H. Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence. Front Neurorobot 2022;16:820028. [DOI: 10.3389/fnbot.2022.820028] [Reference Citation Analysis]
9 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]
10 Zeng H, Chen L, Zhang M, Luo Y, Ma X. Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Gynecol Oncol 2021:S0090-8258(21)00577-1. [PMID: 34275655 DOI: 10.1016/j.ygyno.2021.07.015] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 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]