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For: Kierans AS, Rusinek H, Lee A, Shaikh MB, Triolo M, Huang WC, Chandarana H. Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma. AJR Am J Roentgenol. 2014;203:W637-W644. [PMID: 25415729 DOI: 10.2214/ajr.14.12570] [Cited by in Crossref: 51] [Cited by in F6Publishing: 19] [Article Influence: 7.3] [Reference Citation Analysis]
Number Citing Articles
1 Nguyen K, Schieda N, James N, McInnes MDF, Wu M, Thornhill RE. Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images. Eur Radiol 2021;31:1676-86. [PMID: 32914197 DOI: 10.1007/s00330-020-07233-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
2 Galia M, Albano D, Bruno A, Agrusa A, Romano G, Di Buono G, Agnello F, Salvaggio G, La Grutta L, Midiri M, Lagalla R. Imaging features of solid renal masses. Br J Radiol. 2017;90:20170077. [PMID: 28590813 DOI: 10.1259/bjr.20170077] [Cited by in Crossref: 27] [Cited by in F6Publishing: 27] [Article Influence: 5.4] [Reference Citation Analysis]
3 Cai WL, Hong GB. Quantitative image analysis for evaluation of tumor response in clinical oncology. Chronic Dis Transl Med 2018;4:18-28. [PMID: 29756120 DOI: 10.1016/j.cdtm.2018.01.002] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
4 Kay FU, Pedrosa I. Imaging of Solid Renal Masses. Urol Clin North Am 2018;45:311-30. [PMID: 30031457 DOI: 10.1016/j.ucl.2018.03.013] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 4.8] [Reference Citation Analysis]
5 Yu W, Liang G, Zeng L, Yang Y, Wu Y. Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis. BMJ Open 2021;11:e051470. [PMID: 34937716 DOI: 10.1136/bmjopen-2021-051470] [Reference Citation Analysis]
6 Yin JD, Song LR, Lu HC, Zheng X. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps. World J Gastroenterol 2020; 26(17): 2082-2096 [PMID: 32536776 DOI: 10.3748/wjg.v26.i17.2082] [Cited by in CrossRef: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
7 Brynolfsson P, Nilsson D, Torheim T, Asklund T, Karlsson CT, Trygg J, Nyholm T, Garpebring A. Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters. Sci Rep 2017;7:4041. [PMID: 28642480 DOI: 10.1038/s41598-017-04151-4] [Cited by in Crossref: 47] [Cited by in F6Publishing: 32] [Article Influence: 9.4] [Reference Citation Analysis]
8 Bologna M, Corino VDA, Montin E, Messina A, Calareso G, Greco FG, Sdao S, Mainardi LT. Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images. J Digit Imaging 2018;31:879-94. [PMID: 29725965 DOI: 10.1007/s10278-018-0092-9] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 6.3] [Reference Citation Analysis]
9 Schob S, Meyer HJ, Dieckow J, Pervinder B, Pazaitis N, Höhn AK, Garnov N, Horvath-Rizea D, Hoffmann KT, Surov A. Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer. Int J Mol Sci 2017;18:E821. [PMID: 28417929 DOI: 10.3390/ijms18040821] [Cited by in Crossref: 45] [Cited by in F6Publishing: 44] [Article Influence: 9.0] [Reference Citation Analysis]
10 Tang X, Pang T, Yan WF, Qian WL, Gong YL, Yang ZG. The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy. Front Oncol 2021;11:591502. [PMID: 33747910 DOI: 10.3389/fonc.2021.591502] [Reference Citation Analysis]
11 He X, Zhang H, Zhang T, Han F, Song B. Predictive models composed by radiomic features extracted from multi-detector computed tomography images for predicting low- and high- grade clear cell renal cell carcinoma: A STARD-compliant article. Medicine (Baltimore) 2019;98:e13957. [PMID: 30633175 DOI: 10.1097/MD.0000000000013957] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
12 Feng Z, Zhang L, Qi Z, Shen Q, Hu Z, Chen F. Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings. Front Oncol 2020;10:279. [PMID: 32185138 DOI: 10.3389/fonc.2020.00279] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
13 Tordjman M, Mali R, Madelin G, Prabhu V, Kang SK. Diagnostic test accuracy of ADC values for identification of clear cell renal cell carcinoma: systematic review and meta-analysis. Eur Radiol 2020;30:4023-38. [PMID: 32144458 DOI: 10.1007/s00330-020-06740-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
14 Do QN, Lewis MA, Madhuranthakam AJ, Xi Y, Bailey AA, Lenkinski RE, Twickler DM. Texture analysis of magnetic resonance images of the human placenta throughout gestation: A feasibility study. PLoS One 2019;14:e0211060. [PMID: 30668581 DOI: 10.1371/journal.pone.0211060] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
15 Meng J, Zhu L, Zhu L, Xie L, Wang H, Liu S, Yan J, Liu B, Guan Y, He J, Ge Y, Zhou Z, Yang X. Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT. Oncotarget 2017;8:92442-53. [PMID: 29190929 DOI: 10.18632/oncotarget.21374] [Cited by in Crossref: 18] [Cited by in F6Publishing: 18] [Article Influence: 3.6] [Reference Citation Analysis]
16 Ford J, Dogan N, Young L, Yang F. Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain. Contrast Media Mol Imaging 2018;2018:1729071. [PMID: 30154684 DOI: 10.1155/2018/1729071] [Cited by in Crossref: 43] [Cited by in F6Publishing: 40] [Article Influence: 10.8] [Reference Citation Analysis]
17 Tsili AC, Moulopoulos LA, Varakarakis IΜ, Argyropoulou MI. Cross-sectional imaging assessment of renal masses with emphasis on MRI. Acta Radiol 2021;:2841851211052999. [PMID: 34709096 DOI: 10.1177/02841851211052999] [Reference Citation Analysis]
18 Wu Y, Kwon YS, Labib M, Foran DJ, Singer EA. Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma. Dis Markers 2015;2015:648495. [PMID: 26609190 DOI: 10.1155/2015/648495] [Cited by in Crossref: 14] [Cited by in F6Publishing: 18] [Article Influence: 2.0] [Reference Citation Analysis]
19 Stanzione A, Ricciardi C, Cuocolo R, Romeo V, Petrone J, Sarnataro M, Mainenti PP, Improta G, De Rosa F, Insabato L, Brunetti A, Maurea S. MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study. J Digit Imaging. 2020;33:879-887. [PMID: 32314070 DOI: 10.1007/s10278-020-00336-y] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 15.0] [Reference Citation Analysis]
20 Demirjian NL, Varghese BA, Cen SY, Hwang DH, Aron M, Siddiqui I, Fields BKK, Lei X, Yap FY, Rivas M, Reddy SS, Zahoor H, Liu DH, Desai M, Rhie SK, Gill IS, Duddalwar V. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma. Eur Radiol 2021. [PMID: 34757449 DOI: 10.1007/s00330-021-08344-4] [Reference Citation Analysis]
21 Bektas CT, Kocak B, Yardimci AH, Turkcanoglu MH, Yucetas U, Koca SB, Erdim C, Kilickesmez O. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade. Eur Radiol 2019;29:1153-63. [PMID: 30167812 DOI: 10.1007/s00330-018-5698-2] [Cited by in Crossref: 51] [Cited by in F6Publishing: 52] [Article Influence: 12.8] [Reference Citation Analysis]