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Cited by in F6Publishing
For: Wang J, Chen J, Zhou R, Gao Y, Li J. Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients. BMC Cancer 2022;22:420. [PMID: 35439946 DOI: 10.1186/s12885-022-09518-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Yardimci AH, Kocak B, Sel I, Bulut H, Bektas CT, Cin M, Dursun N, Bektas H, Mermut O, Yardimci VH, Kilickesmez O. Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI. Jpn J Radiol 2023;41:71-82. [PMID: 35962933 DOI: 10.1007/s11604-022-01325-7] [Reference Citation Analysis]
2 Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28(42): 6002-6016 [DOI: 10.3748/wjg.v28.i42.6002] [Reference Citation Analysis]