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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: 58] [Cited by in F6Publishing: 59] [Article Influence: 7.3] [Reference Citation Analysis]
Number Citing Articles
1 Wang K, Yu J, Li Y, Xu Q. Histogram Analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer.. [DOI: 10.21203/rs.3.rs-2316691/v1] [Reference Citation Analysis]
2 Lin N, Yu S, Xia Z, Wang Y, Chen W, Sha Y. Apparent Diffusion Coefficient-Based Radiomic Nomogram in Sinonasal Squamous Cell Carcinoma: A Preliminary Study on Histological Grade Evaluation. J Comput Assist Tomogr 2022. [PMID: 35675693 DOI: 10.1097/RCT.0000000000001329] [Reference Citation Analysis]
3 Lin N, Yu S, Lin M, Shi Y, Chen W, Xia Z, Cheng Y, Sha Y. A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study. Front Oncol 2022;12:870935. [PMID: 35651794 DOI: 10.3389/fonc.2022.870935] [Reference Citation Analysis]
4 Ma Y, Guan Z, Liang H, Cao H. Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Through CT-Based Tumoral and Peritumoral Radiomics. Front Oncol 2022;12:831112. [PMID: 35237524 DOI: 10.3389/fonc.2022.831112] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [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]
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7 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]
8 Erbay G, Goren MR, Karadeli E, Pekoz B, Koc Z, Arica S. Use of Histogram Analysis in Diffusion-Weighted Magnetic Resonance Imaging for Differentiation of Renal Tumor Subgroups. Iran J Radiol 2021;18. [DOI: 10.5812/iranjradiol.110963] [Reference Citation Analysis]
9 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: 31] [Cited by in F6Publishing: 24] [Article Influence: 15.5] [Reference Citation Analysis]
10 Sun J, Pan L, Zha T, Xing W, Chen J, Duan S. The role of MRI texture analysis based on susceptibility-weighted imaging in predicting Fuhrman grade of clear cell renal cell carcinoma. Acta Radiol 2021;62:1104-11. [PMID: 32867506 DOI: 10.1177/0284185120951964] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
11 Ren J, Qi M, Yuan Y, Tao X. Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the oral tongue and floor of mouth: a preliminary study. Acta Radiol 2021;62:453-61. [PMID: 32536260 DOI: 10.1177/0284185120931683] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
12 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]
13 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: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
14 Schieda N, Nguyen K, Thornhill RE, McInnes MDF, Wu M, James N. Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT. Abdom Radiol (NY) 2020;45:2786-96. [PMID: 32627049 DOI: 10.1007/s00261-020-02632-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
15 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: 16] [Cited by in F6Publishing: 15] [Article Influence: 5.3] [Reference Citation Analysis]
16 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: 14] [Cited by in F6Publishing: 12] [Article Influence: 4.7] [Reference Citation Analysis]
17 Haji-Momenian S, Lin Z, Patel B, Law N, Michalak A, Nayak A, Earls J, Loew M. Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study. Abdom Radiol (NY) 2020;45:789-98. [PMID: 31822969 DOI: 10.1007/s00261-019-02336-1] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 4.3] [Reference Citation Analysis]
18 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: 11] [Cited by in F6Publishing: 12] [Article Influence: 3.7] [Reference Citation Analysis]
19 He X, Wei Y, Zhang H, Zhang T, Yuan F, Huang Z, Han F, Song B. Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images. Acad Radiol 2020;27:157-68. [PMID: 31147235 DOI: 10.1016/j.acra.2019.05.004] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]
20 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: 25] [Cited by in F6Publishing: 29] [Article Influence: 6.3] [Reference Citation Analysis]
21 Goyal A, Razik A, Kandasamy D, Seth A, Das P, Ganeshan B, Sharma R. Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study. Abdom Radiol (NY) 2019;44:3336-49. [PMID: 31300850 DOI: 10.1007/s00261-019-02122-z] [Cited by in Crossref: 30] [Cited by in F6Publishing: 28] [Article Influence: 7.5] [Reference Citation Analysis]
22 Thomas R, Qin L, Alessandrino F, Sahu SP, Guerra PJ, Krajewski KM, Shinagare A. A review of the principles of texture analysis and its role in imaging of genitourinary neoplasms. Abdom Radiol (NY) 2019;44:2501-10. [PMID: 30448920 DOI: 10.1007/s00261-018-1832-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 1.5] [Reference Citation Analysis]
23 Li H, Li A, Zhu H, Hu Y, Li J, Xia L, Hu D, Kamel IR, Li Z. Whole-Tumor Quantitative Apparent Diffusion Coefficient Histogram and Texture Analysis to Differentiation of Minimal Fat Angiomyolipoma from Clear Cell Renal Cell Carcinoma. Acad Radiol 2019;26:632-9. [PMID: 30087067 DOI: 10.1016/j.acra.2018.06.015] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 3.5] [Reference Citation Analysis]
24 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: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]
25 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: 19] [Cited by in F6Publishing: 20] [Article Influence: 4.8] [Reference Citation Analysis]
26 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: 74] [Cited by in F6Publishing: 67] [Article Influence: 14.8] [Reference Citation Analysis]
27 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: 57] [Cited by in F6Publishing: 64] [Article Influence: 11.4] [Reference Citation Analysis]
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32 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: 20] [Cited by in F6Publishing: 22] [Article Influence: 3.3] [Reference Citation Analysis]
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