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Cited by in F6Publishing
For: Liang JD, Ping XO, Tseng YJ, Huang GT, Lai F, Yang PM. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods. Comput Methods Programs Biomed. 2014;117:425-434. [PMID: 25278224 DOI: 10.1016/j.cmpb.2014.09.001] [Cited by in Crossref: 19] [Cited by in F6Publishing: 23] [Article Influence: 2.4] [Reference Citation Analysis]
Number Citing Articles
1 Hatzidakis A, Müller L, Krokidis M, Kloeckner R. Local and Regional Therapies for Hepatocellular Carcinoma and Future Combinations. Cancers (Basel) 2022;14:2469. [PMID: 35626073 DOI: 10.3390/cancers14102469] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
2 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Reference Citation Analysis]
3 Papaconstantinou D, Hewitt DB, Brown ZJ, Schizas D, Tsilimigras DI, Pawlik TM. Patient stratification in hepatocellular carcinoma: impact on choice of therapy. Expert Rev Anticancer Ther 2022. [PMID: 35157530 DOI: 10.1080/14737140.2022.2041415] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 Qian X, Zheng H, Xue K, Chen Z, Hu Z, Zhang L, Wan J. Recurrence Risk of Liver Cancer Post-hepatectomy Using Machine Learning and Study of Correlation With Immune Infiltration. Front Genet 2021;12:733654. [PMID: 34956309 DOI: 10.3389/fgene.2021.733654] [Reference Citation Analysis]
5 Sun LY, Ouyang Q, Cen WJ, Wang F, Tang WT, Shao JY. A Model Based on Artificial Intelligence Algorithm for Monitoring Recurrence of HCC after Hepatectomy. Am Surg 2021;:31348211063549. [PMID: 34894786 DOI: 10.1177/00031348211063549] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
6 Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27(37): 6191-6223 [PMID: 34712027 DOI: 10.3748/wjg.v27.i37.6191] [Cited by in CrossRef: 7] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
7 Li X, Yang L, Yuan Z, Lou J, Fan Y, Shi A, Huang J, Zhao M, Wu Y. Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection. J Transl Med 2021;19:281. [PMID: 34193166 DOI: 10.1186/s12967-021-02955-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
8 Lang Q, Zhong C, Liang Z, Zhang Y, Wu B, Xu F, Cong L, Wu S, Tian Y. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer. Artif Intell Rev 2021;54:5307-46. [DOI: 10.1007/s10462-021-10023-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
10 Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021;12:31. [PMID: 33675433 DOI: 10.1186/s13244-021-00977-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
11 Ivanics T, Patel MS, Erdman L, Sapisochin G. Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology). Curr Opin Organ Transplant 2020;25:426-34. [PMID: 32487887 DOI: 10.1097/MOT.0000000000000773] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
12 Lai Q, Spoletini G, Mennini G, Larghi Laureiro Z, Tsilimigras DI, Pawlik TM, Rossi M. Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review. World J Gastroenterol 2020; 26(42): 6679-6688 [PMID: 33268955 DOI: 10.3748/wjg.v26.i42.6679] [Cited by in CrossRef: 14] [Cited by in F6Publishing: 13] [Article Influence: 7.0] [Reference Citation Analysis]
13 Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26(37): 5617-5628 [PMID: 33088156 DOI: 10.3748/wjg.v26.i37.5617] [Cited by in CrossRef: 14] [Cited by in F6Publishing: 11] [Article Influence: 7.0] [Reference Citation Analysis]
14 Iwahashi S, Ghaibeh AA, Shimada M, Morine Y, Imura S, Ikemoto T, Saito Y, Hirose J. Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method. Mol Clin Oncol 2020;13:46. [PMID: 32874576 DOI: 10.3892/mco.2020.2116] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Lin CH, Ho CM, Wu CH, Liang PC, Wu YM, Hu RH, Lee PH, Ho MC. Minimally invasive surgery versus radiofrequency ablation for single subcapsular hepatocellular carcinoma ≤ 2 cm with compensated liver cirrhosis. Surg Endosc 2020;34:5566-73. [PMID: 31993821 DOI: 10.1007/s00464-019-07357-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 8] [Article Influence: 1.5] [Reference Citation Analysis]
16 Renukadevi T, Karunakaran S. Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification. Int J Imaging Syst Technol 2019;30:168-84. [DOI: 10.1002/ima.22375] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
17 Wang HY, Hung CC, Chen CH, Lee TY, Huang KY, Ning HC, Lai NC, Tsai MH, Lu LC, Tseng YJ, Lu JJ. Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach. Sci Rep 2019;9:11074. [PMID: 31423009 DOI: 10.1038/s41598-019-47361-8] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
18 Xu D, Sheng JQ, Hu PJ, Huang TS, Lee WC. Predicting hepatocellular carcinoma recurrences: A data-driven multiclass classification method incorporating latent variables. J Biomed Inform 2019;96:103237. [PMID: 31238108 DOI: 10.1016/j.jbi.2019.103237] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.3] [Reference Citation Analysis]
19 Ye X, Li H, Sakurai T, Shueng PW. Ensemble Feature Learning to Identify Risk Factors for Predicting Secondary Cancer. Int J Med Sci 2019;16:949-59. [PMID: 31341408 DOI: 10.7150/ijms.33820] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 0.7] [Reference Citation Analysis]
20 Rajathi GI, Jiji GW. Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier. Symmetry 2019;11:33. [DOI: 10.3390/sym11010033] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 2.7] [Reference Citation Analysis]
21 Richter AN, Khoshgoftaar TM. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artificial Intelligence in Medicine 2018;90:1-14. [DOI: 10.1016/j.artmed.2018.06.002] [Cited by in Crossref: 44] [Cited by in F6Publishing: 47] [Article Influence: 11.0] [Reference Citation Analysis]
22 Lv Y, Wei W, Huang Z, Chen Z, Fang Y, Pan L, Han X, Xu Z. Long non-coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection: LncRNA expression profile to predict HCC recurrence. Hepatol Res 2018;48:1140-8. [DOI: 10.1111/hepr.13220] [Cited by in Crossref: 20] [Cited by in F6Publishing: 26] [Article Influence: 5.0] [Reference Citation Analysis]
23 Wang Y, Tian Y. miRNA for diagnosis and clinical implications of human hepatocellular carcinoma. Hepatol Res 2016;46:89-99. [PMID: 26284466 DOI: 10.1111/hepr.12571] [Cited by in Crossref: 16] [Cited by in F6Publishing: 23] [Article Influence: 2.3] [Reference Citation Analysis]