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Cited by in F6Publishing
For: Wei L, Owen D, Rosen B, Guo X, Cuneo K, Lawrence TS, Ten Haken R, El Naqa I. A deep survival interpretable radiomics model of hepatocellular carcinoma patients. Phys Med 2021;82:295-305. [PMID: 33714190 DOI: 10.1016/j.ejmp.2021.02.013] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Bao L, Wang M, Fan Q. Hsa_circ_NOTCH3 regulates ZNF146 through sponge adsorption of miR-875-5p to promote tumorigenesis of hepatocellular carcinoma. J Gastrointest Oncol 2021;12:2388-402. [PMID: 34790400 DOI: 10.21037/jgo-21-567] [Reference Citation Analysis]
2 Peng J, Huang J, Huang G, Zhang J. Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning. Front Oncol 2021;11:730282. [PMID: 34745952 DOI: 10.3389/fonc.2021.730282] [Reference Citation Analysis]
3 Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction. JHEP Reports 2022. [DOI: 10.1016/j.jhepr.2022.100443] [Reference Citation Analysis]
4 Chamseddine I, Kim Y, De B, El Naqa I, Duda DG, Wolfgang J, Pursley J, Paganetti H, Wo J, Hong T, Koay EJ, Grassberger C. Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy. JCO Clin Cancer Inform 2022;6:e2100169. [PMID: 35192402 DOI: 10.1200/CCI.21.00169] [Reference Citation Analysis]
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