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For: Jiang Y, You K, Qiu X, Bi Z, Mo H, Li L, Liu Y. Tumor volume predicts local recurrence in early rectal cancer treated with radical resection: A retrospective observational study of 270 patients. International Journal of Surgery 2018;49:68-73. [DOI: 10.1016/j.ijsu.2017.11.052] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang X, Zheng Z, Chen M, Lin J, Lu X, Huang Y, Huang S, Chi P. Morphology of the anterior mesorectum: a new predictor for local recurrence in patients with rectal cancer. Chin Med J (Engl) 2022. [PMID: 35861423 DOI: 10.1097/CM9.0000000000002024] [Reference Citation Analysis]
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3 Cai S, Zhang H, Chen X, Wang T, Lu J, Liu X, Zhang G. MR volumetry in predicting the aggressiveness of endometrioid adenocarcinoma: correlation with final pathological results. Acta Radiol 2020;61:705-13. [PMID: 31564116 DOI: 10.1177/0284185119877331] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
4 Lutsyk M, Awawda M, Gourevich K, Ben Yosef R. Tumor Volume as Predictor of Pathologic Complete Response Following Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer. Am J Clin Oncol 2021;44:482-6. [PMID: 34269693 DOI: 10.1097/COC.0000000000000846] [Reference Citation Analysis]
5 Haggstrom L, Kang S, Winn R, Putnis S, Nasser E, Chen J, Aghmesheh M, Brungs D. Factors influencing recurrence of stage I-III rectal cancer in regional Australia. ANZ J Surg 2020;90:2490-5. [PMID: 32729654 DOI: 10.1111/ans.16187] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
6 Ouchi A, Kinoshita T, Nakanishi H, Komori K, Oshiro T, Yoshimura M, Fujita N, Hosoda W, Shimizu Y. PCR-based quantitative detection of intraperitoneal free cancer cells for predicting locoregional recurrence after rectal cancer resection. ANZ J Surg 2022. [PMID: 35018696 DOI: 10.1111/ans.17436] [Reference Citation Analysis]
7 Shao H, Ma X, Gao Y, Wang J, Wu J, Wang B, Li J, Tian J. Comparison of the diagnostic efficiency for local recurrence of rectal cancer using CT, MRI, PET and PET-CT: A systematic review protocol. Medicine (Baltimore). 2018;97:e12900. [PMID: 30508883 DOI: 10.1097/md.0000000000012900] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Karahan Şen NP, Aksu A, Kaya GÇ. Value of volumetric and textural analysis in predicting the treatment response in patients with locally advanced rectal cancer. Ann Nucl Med 2020;34:960-7. [PMID: 32951129 DOI: 10.1007/s12149-020-01527-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML). Diagnostics (Basel) 2021;11:742. [PMID: 33919342 DOI: 10.3390/diagnostics11050742] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Rengo M, Landolfi F, Picchia S, Bellini D, Losquadro C, Badia S, Caruso D, Iannicelli E, Osti MF, Tombolini V, Carbone I, Giunta G, Laghi A. Rectal cancer response to neoadjuvant chemoradiotherapy evaluated with MRI: Development and validation of a classification algorithm. Eur J Radiol 2022;147:110146. [PMID: 34998098 DOI: 10.1016/j.ejrad.2021.110146] [Reference Citation Analysis]