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For: Yan L, Liu Z, Wang G, Huang Y, Liu Y, Yu Y, Liang C. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol. 2015;22:1115-1121. [PMID: 26031228 DOI: 10.1016/j.acra.2015.04.004] [Cited by in Crossref: 58] [Cited by in F6Publishing: 59] [Article Influence: 8.3] [Reference Citation Analysis]
Number Citing Articles
1 Zhang GM, Sun H, Shi B, Jin ZY, Xue HD. Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol (NY) 2017;42:561-8. [PMID: 27604896 DOI: 10.1007/s00261-016-0897-2] [Cited by in Crossref: 35] [Cited by in F6Publishing: 31] [Article Influence: 8.8] [Reference Citation Analysis]
2 Feng Z, Rong P, Cao P, Zhou Q, Zhu W, Yan Z, Liu Q, Wang W. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma. Eur Radiol. 2018;28:1625-1633. [PMID: 29134348 DOI: 10.1007/s00330-017-5118-z] [Cited by in Crossref: 92] [Cited by in F6Publishing: 84] [Article Influence: 18.4] [Reference Citation Analysis]
3 Salvador R, Sebastià M, Cárdenas G, Páez-Carpio A, Paño B, Solé M, Nicolau C. CT differentiation of fat-poor angiomyolipomas from papillary renal cell carcinomas: development of a predictive model. Abdom Radiol (NY) 2021;46:3280-7. [PMID: 33674961 DOI: 10.1007/s00261-021-02988-y] [Reference Citation Analysis]
4 Cui E, Lin F, Li Q, Li R, Chen X, Liu Z, Long W. Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features. Acta Radiol 2019;60:1543-52. [DOI: 10.1177/0284185119830282] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
5 E L, Lu L, Li L, Yang H, Schwartz LH, Zhao B. Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. Academic Radiology 2019;26:1245-52. [DOI: 10.1016/j.acra.2018.10.013] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 5.3] [Reference Citation Analysis]
6 Lee HS, Hong H, Jung DC, Park S, Kim J. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification. Med Phys 2017;44:3604-14. [DOI: 10.1002/mp.12258] [Cited by in Crossref: 33] [Cited by in F6Publishing: 28] [Article Influence: 6.6] [Reference Citation Analysis]
7 Qi X, Li Q, Che X, Wang Q, Wu G. The Uniqueness of Clear Cell Renal Cell Carcinoma: Summary of the Process and Abnormality of Glucose Metabolism and Lipid Metabolism in ccRCC. Front Oncol 2021;11:727778. [PMID: 34604067 DOI: 10.3389/fonc.2021.727778] [Reference Citation Analysis]
8 Davenport MS, Chandarana H, Curci NE, Doshi A, Kaffenberger SD, Pedrosa I, Remer EM, Schieda N, Shinagare AB, Smith AD, Wang ZJ, Wells SA, Silverman SG. Society of Abdominal Radiology disease-focused panel on renal cell carcinoma: update on past, current, and future goals. Abdom Radiol (NY) 2018;43:2213-20. [PMID: 29948056 DOI: 10.1007/s00261-018-1663-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Wang XJ, Qu BQ, Zhou JP, Zhou QM, Lu YF, Pan Y, Xu JX, Miu YY, Wang HQ, Yu RS. A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features. Front Oncol 2021;11:633034. [PMID: 33968732 DOI: 10.3389/fonc.2021.633034] [Reference Citation Analysis]
10 Ekert K, Hinterleitner C, Horger M. Prognosis assessment in metastatic gastrointestinal stromal tumors treated with tyrosine kinase inhibitors based on CT-texture analysis. Eur J Radiol. 2019;116:98-105. [PMID: 31153581 DOI: 10.1016/j.ejrad.2019.04.018] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
11 Pei X, Wang P, Ren JL, Yin XP, Ma LY, Wang Y, Ma X, Gao BL. Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas. Front Oncol 2021;11:659969. [PMID: 34123817 DOI: 10.3389/fonc.2021.659969] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Low G, Huang G, Fu W, Moloo Z, Girgis S. Review of renal cell carcinoma and its common subtypes in radiology. World J Radiol 2016; 8(5): 484-500 [PMID: 27247714 DOI: 10.4329/wjr.v8.i5.484] [Cited by in CrossRef: 63] [Cited by in F6Publishing: 61] [Article Influence: 10.5] [Reference Citation Analysis]
13 Xu Y, Xu Q, Ma Y, Duan J, Zhang H, Liu T, Li L, Sun H, Shi K, Xie S, Wang W. Characterizing MRI features of rectal cancers with different KRAS status.BMC Cancer. 2019;19:1111. [PMID: 31727020 DOI: 10.1186/s12885-019-6341-6] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 3.3] [Reference Citation Analysis]
14 Zhou L, Zhang Z, Chen YC, Zhao ZY, Yin XD, Jiang HB. A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors. Transl Oncol 2019;12:292-300. [PMID: 30448734 DOI: 10.1016/j.tranon.2018.10.012] [Cited by in Crossref: 36] [Cited by in F6Publishing: 27] [Article Influence: 9.0] [Reference Citation Analysis]
15 Yang R, Wu J, Sun L, Lai S, Xu Y, Liu X, Ma Y, Zhen X. Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat. Eur Radiol 2020;30:1254-63. [PMID: 31468159 DOI: 10.1007/s00330-019-06384-5] [Cited by in Crossref: 25] [Cited by in F6Publishing: 22] [Article Influence: 8.3] [Reference Citation Analysis]
16 Sun J, Zhang XY, Li XT, Li YL, Wang ZL. Use of Iodine Concentration in the Lipid-Poor Portion of the Renal Mass for Differentiation of Angiomyolipoma from Renal Cell Carcinoma. Cancer Biother Radiopharm 2019;34:224-30. [PMID: 31070481 DOI: 10.1089/cbr.2018.2696] [Reference Citation Analysis]
17 Lin F, Cui EM, Lei Y, Luo LP. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol (NY) 2019;44:2528-34. [PMID: 30919041 DOI: 10.1007/s00261-019-01992-7] [Cited by in Crossref: 23] [Cited by in F6Publishing: 17] [Article Influence: 11.5] [Reference Citation Analysis]
18 Vaidya T, Agrawal A, Mahajan S, Thakur MH, Mahajan A. The Continuing Evolution of Molecular Functional Imaging in Clinical Oncology: The Road to Precision Medicine and Radiogenomics (Part II). Mol Diagn Ther 2019;23:27-51. [PMID: 30387041 DOI: 10.1007/s40291-018-0367-3] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
19 Wang X, Song G, Sun J, Shao G. Differential diagnosis of hypervascular ultra-small renal cell carcinoma and renal angiomyolipoma with minimal fat in early stage by using thin-section multidetector computed tomography. Abdom Radiol (NY) 2020;45:3849-59. [PMID: 32415344 DOI: 10.1007/s00261-020-02542-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
20 Schieda N, Lim RS, Krishna S, McInnes MDF, Flood TA, Thornhill RE. Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma. AJR Am J Roentgenol 2018;210:1079-87. [PMID: 29547054 DOI: 10.2214/AJR.17.18874] [Cited by in Crossref: 28] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
21 Zhang P, Min X, Feng Z, Kang Z, Li B, Cai W, Fan C, Yin X, Xie J, Lv W, Wang L. Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions. Cancer Manag Res 2021;13:839-47. [PMID: 33536790 DOI: 10.2147/CMAR.S288378] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Kocak B, Durmaz ES, Erdim C, Ates E, Kaya OK, Kilickesmez O. Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies. AJR Am J Roentgenol. 2020;214:129-136. [PMID: 31613661 DOI: 10.2214/ajr.19.21709] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 4.7] [Reference Citation Analysis]
23 Yang G, Gong A, Nie P, Yan L, Miao W, Zhao Y, Wu J, Cui J, Jia Y, Wang Z. Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma. Mol Imaging 2019;18:1536012119883161. [PMID: 31625454 DOI: 10.1177/1536012119883161] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
24 Li Y, Xu X, Weng S, Yan C, Chen J, Ye R. CT Image-Based Texture Analysis to Predict Microvascular Invasion in Primary Hepatocellular Carcinoma. J Digit Imaging 2020;33:1365-75. [PMID: 32968880 DOI: 10.1007/s10278-020-00386-2] [Reference Citation Analysis]
25 Zhang GM, Shi B, Xue HD, Ganeshan B, Sun H, Jin ZY. Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma? Clin Radiol 2019;74:287-94. [PMID: 30554807 DOI: 10.1016/j.crad.2018.11.009] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 5.0] [Reference Citation Analysis]
26 Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol 2020;38:2329-47. [PMID: 31691082 DOI: 10.1007/s00345-019-03000-5] [Cited by in Crossref: 21] [Cited by in F6Publishing: 20] [Article Influence: 7.0] [Reference Citation Analysis]
27 Erkoc M, Bozkurt M, Besiroglu H, Canat L, Atalay HA. Success of extracorporeal shock wave lithotripsy based on CT texture analysis. Int J Clin Pract 2021;75:e14823. [PMID: 34491588 DOI: 10.1111/ijcp.14823] [Reference Citation Analysis]
28 Tang Z, Yu D, Ni T, Zhao T, Jin Y, Dong E. Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma. AJR Am J Roentgenol 2020;214:370-82. [PMID: 31799870 DOI: 10.2214/AJR.19.21625] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
29 Yi X, Guan X, Chen C, Zhang Y, Zhang Z, Li M, Liu P, Yu A, Long X, Liu L, Chen BT, Zee C. Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma. J Cancer 2018;9:3577-82. [PMID: 30310515 DOI: 10.7150/jca.26356] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]
30 Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics. 2017;37:1483-1503. [PMID: 28898189 DOI: 10.1148/rg.2017170056] [Cited by in Crossref: 300] [Cited by in F6Publishing: 278] [Article Influence: 60.0] [Reference Citation Analysis]
31 Zhang Y, Zhao Y, Lv Y, Gu X. Value of Quantitative CTTA in Differentiating Malignant From Benign Bosniak III Renal Lesions on CT Images. J Comput Assist Tomogr 2021. [PMID: 34176873 DOI: 10.1097/RCT.0000000000001181] [Reference Citation Analysis]
32 Li Y, Yan C, Weng S, Shi Z, Sun H, Chen J, Xu X, Ye R, Hong J. Texture analysis of multi-phase MRI images to detect expression of Ki67 in hepatocellular carcinoma. Clin Radiol 2019;74:813.e19-27. [PMID: 31362887 DOI: 10.1016/j.crad.2019.06.024] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 3.3] [Reference Citation Analysis]
33 Schieda N, Lim RS, McInnes MDF, Thomassin I, Renard-Penna R, Tavolaro S, Cornelis FH. Characterization of small (<4cm) solid renal masses by computed tomography and magnetic resonance imaging: Current evidence and further development. Diagn Interv Imaging 2018;99:443-55. [PMID: 29606371 DOI: 10.1016/j.diii.2018.03.004] [Cited by in Crossref: 24] [Cited by in F6Publishing: 21] [Article Influence: 6.0] [Reference Citation Analysis]
34 Deng Y, Soule E, Cui E, Samuel A, Shah S, Lall C, Sundaram C, Sandrasegaran K. Usefulness of CT texture analysis in differentiating benign and malignant renal tumours. Clin Radiol 2020;75:108-15. [PMID: 31668402 DOI: 10.1016/j.crad.2019.09.131] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
35 Sun XY, Feng QX, Xu X, Zhang J, Zhu FP, Yang YH, Zhang YD. Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists. AJR Am J Roentgenol 2020;214:W44-54. [PMID: 31553660 DOI: 10.2214/AJR.19.21617] [Cited by in Crossref: 17] [Cited by in F6Publishing: 9] [Article Influence: 5.7] [Reference Citation Analysis]
36 Lee H, Hong H, Kim J, Jung DC. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation. Med Phys 2018;45:1550-61. [DOI: 10.1002/mp.12828] [Cited by in Crossref: 33] [Cited by in F6Publishing: 25] [Article Influence: 8.3] [Reference Citation Analysis]
37 Xu Q, Zhu Q, Liu H, Chang L, Duan S, Dou W, Li S, Ye J. Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists. J Magn Reson Imaging 2021. [PMID: 34462986 DOI: 10.1002/jmri.27900] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Zhang Y, Li X, Lv Y, Gu X. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma. Tomography 2020;6:325-32. [PMID: 33364422 DOI: 10.18383/j.tom.2020.00039] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Krajewski KM, Shinagare AB. Novel imaging in renal cell carcinoma. Curr Opin Urol 2016;26:388-95. [PMID: 27262139 DOI: 10.1097/MOU.0000000000000314] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
40 Lakhman Y, Veeraraghavan H, Chaim J, Feier D, Goldman DA, Moskowitz CS, Nougaret S, Sosa RE, Vargas HA, Soslow RA, Abu-Rustum NR, Hricak H, Sala E. Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis. Eur Radiol. 2017;27:2903-2915. [PMID: 27921159 DOI: 10.1007/s00330-016-4623-9] [Cited by in Crossref: 71] [Cited by in F6Publishing: 62] [Article Influence: 11.8] [Reference Citation Analysis]
41 Liu J, Xue K, Li S, Zhang Y, Cheng J. Combined Diagnosis of Whole-Lesion Histogram Analysis of T1- and T2-Weighted Imaging for Differentiating Adrenal Adenoma and Pheochromocytoma: A Support Vector Machine-Based Study. Can Assoc Radiol J 2021;72:452-9. [PMID: 32208861 DOI: 10.1177/0846537120911736] [Reference Citation Analysis]
42 You MW, Kim N, Choi HJ. The value of quantitative CT texture analysis in differentiation of angiomyolipoma without visible fat from clear cell renal cell carcinoma on four-phase contrast-enhanced CT images. Clin Radiol 2019;74:547-54. [PMID: 31010583 DOI: 10.1016/j.crad.2019.02.018] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]
43 Wang X, Song G, Jiang H. Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools. Cancer Imaging 2021;21:47. [PMID: 34225784 DOI: 10.1186/s40644-021-00417-3] [Reference Citation Analysis]
44 Choi TW, Kim JH, Yu MH, Park SJ, Han JK. Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol 2018;59:383-92. [DOI: 10.1177/0284185117725367] [Cited by in Crossref: 53] [Cited by in F6Publishing: 53] [Article Influence: 10.6] [Reference Citation Analysis]
45 Liu H, Jing B, Han W, Long Z, Mo X, Li H. A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma. J Med Syst 2019;43:59. [PMID: 30707369 DOI: 10.1007/s10916-019-1175-y] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
46 Wang Z, He Y, Wang N, Zhang T, Wu H, Jiang X, Mo L. Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images. Medicine (Baltimore) 2020;99:e20093. [PMID: 32358396 DOI: 10.1097/MD.0000000000020093] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
47 Li XL, Shi LX, Du QC, Wang W, Shao LW, Wang YW. Magnetic resonance imaging features of minimal-fat angiomyolipoma and causes of preoperative misdiagnosis. World J Clin Cases 2020; 8(12): 2502-2509 [PMID: 32607327 DOI: 10.12998/wjcc.v8.i12.2502] [Reference Citation Analysis]
48 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. Academic Radiology 2020;27:157-68. [DOI: 10.1016/j.acra.2019.05.004] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
49 Huang Z, Li M, He D, Wei Y, Yu H, Wang Y, Yuan F, Song B. Two-dimensional Texture Analysis Based on CT Images to Differentiate Pancreatic Lymphoma and Pancreatic Adenocarcinoma: A Preliminary Study. Acad Radiol 2019;26:e189-95. [PMID: 30193819 DOI: 10.1016/j.acra.2018.07.021] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 3.8] [Reference Citation Analysis]
50 Miller FH, Minocha J, Parthasarathy S, Adam SZ, Parada C, Yaghmai V. Loss of intratumoral macroscopic fat in renal angiomyolipoma following chemoradiation therapy for pancreatic cancer. BJR Case Rep 2017;3:20150439. [PMID: 30363307 DOI: 10.1259/bjrcr.20150439] [Reference Citation Analysis]
51 Chen C, Kang Q, Xu B, Shi Z, Guo H, Wei Q, Lu Y, Wu X. Fat poor angiomyolipoma differentiation from renal cell carcinoma at 320-slice dynamic volume CT perfusion. Abdom Radiol (NY) 2018;43:1223-30. [PMID: 28828638 DOI: 10.1007/s00261-017-1286-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
52 Lim RS, Flood TA, McInnes MDF, Lavallee LT, Schieda N. Renal angiomyolipoma without visible fat: Can we make the diagnosis using CT and MRI? Eur Radiol 2018;28:542-53. [PMID: 28779401 DOI: 10.1007/s00330-017-4988-4] [Cited by in Crossref: 31] [Cited by in F6Publishing: 30] [Article Influence: 6.2] [Reference Citation Analysis]
53 Lubner MG. Radiomics and Artificial Intelligence for Renal Mass Characterization. Radiol Clin North Am 2020;58:995-1008. [PMID: 32792129 DOI: 10.1016/j.rcl.2020.06.001] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
54 Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging "what a clinical radiologist needs to know". Eur J Radiol 2022;146:110055. [PMID: 34902669 DOI: 10.1016/j.ejrad.2021.110055] [Reference Citation Analysis]
55 Nie P, Yang G, Wang Z, Yan L, Miao W, Hao D, Wu J, Zhao Y, Gong A, Cui J, Jia Y, Niu H. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma. Eur Radiol 2020;30:1274-84. [PMID: 31506816 DOI: 10.1007/s00330-019-06427-x] [Cited by in Crossref: 29] [Cited by in F6Publishing: 25] [Article Influence: 9.7] [Reference Citation Analysis]
56 Feng M, Zhang M, Liu Y, Jiang N, Meng Q, Wang J, Yao Z, Gan W, Dai H. Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study. BMC Cancer 2020;20:611. [PMID: 32605628 DOI: 10.1186/s12885-020-07094-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
57 Razik A, Goyal A, Sharma R, Kandasamy D, Seth A, Das P, Ganeshan B. MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma. Br J Radiol 2020;93:20200569. [PMID: 32667833 DOI: 10.1259/bjr.20200569] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
58 Suarez-Ibarrola R, Basulto-Martinez M, Heinze A, Gratzke C, Miernik A. Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature. Cancers (Basel) 2020;12:E1387. [PMID: 32481542 DOI: 10.3390/cancers12061387] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
59 Brodie A, Dai N, Teoh JY, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021;39:379-99. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Reference Citation Analysis]
60 Kocak B, Ates E, Durmaz ES, Ulusan MB, Kilickesmez O. Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas. Eur Radiol 2019;29:4765-75. [DOI: 10.1007/s00330-019-6003-8] [Cited by in Crossref: 28] [Cited by in F6Publishing: 25] [Article Influence: 9.3] [Reference Citation Analysis]
61 Ma Y, Ma W, Xu X, Guan Z, Pang P. A convention-radiomics CT nomogram for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma. Sci Rep 2021;11:4644. [PMID: 33633296 DOI: 10.1038/s41598-021-84244-3] [Reference Citation Analysis]
62 Park BK. Renal Angiomyolipoma: Radiologic Classification and Imaging Features According to the Amount of Fat. AJR Am J Roentgenol. 2017;209:826-835. [PMID: 28726505 DOI: 10.2214/ajr.17.17973] [Cited by in Crossref: 40] [Cited by in F6Publishing: 10] [Article Influence: 8.0] [Reference Citation Analysis]
63 Ma Y, Cao F, Xu X, Ma W. Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis? Abdom Radiol (NY) 2020;45:2500-7. [PMID: 31980867 DOI: 10.1007/s00261-020-02414-9] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 9.0] [Reference Citation Analysis]