A CT-Based Tumoral and Mini-Peritumoral Radiomics Approach: Differentiate Fat-Poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma

References

• Kim JK, Park S, Shon J, Cho K. Angiomyolipoma with minimal fat: differentiation from renal cell carcinoma at biphasic helical CT. Radiology. 2004;230:677–684. doi:10.1148/radiol.230303000314990834
   PubMed Web of Science ®Google Scholar
• Sooriakumaran P, Gibbs P, Coughlin G, et al. Angiomyolipomata: challenges, solutions, and future prospects based on over 100 cases treated. BJU Int. 2010;105:101–106. doi:10.1111/j.1464-410X.2009.08649.x19493268
   PubMed Web of Science ®Google Scholar
• Jinzaki M, Silverman SG, Akita H, Nagashima Y, Mikami S, Oya M. Renal angiomyolipoma: a radiological classification and update on recent developments in diagnosis and management. Abdom Imaging. 2014;39:588–604. doi:10.1007/s00261-014-0083-324504542
   PubMedGoogle Scholar
• Prando A, Prando DP, Prando P. Renal cell carcinoma: unusual imaging manifestations. Radiographics. 2006;26:233–244. doi:10.1148/rg.26105506016418254
   PubMed Web of Science ®Google Scholar
• Kim SH, Kim CS, Kim MJ, Cho JY, Cho SH. Differentiation of clear cell renal cell carcinoma from other subtypes and fat-poor angiomyolipoma by use of quantitative enhancement measurement during three-phase MDCT. Am J Roentgenol. 2016;2016:21–28. doi:10.2214/AJR.15.14666
   Web of Science ®Google Scholar
• Woo S, Suh CH, Cho JY, Kim SY, Kim SH. Diagnostic performance of CT for diagnosis of fat-poor angiomyolipoma in patients with renal masses: a systematic review and meta-analysis. AJR Am J Roentgenol. 2017;209:297–307. doi:10.2214/AJR.17.18184
   PubMed Web of Science ®Google Scholar
• Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–577. doi:10.1148/radiol.201515116926579733
   PubMed Web of Science ®Google Scholar
• Ma Y, Ma W, Xu X, Cao F. How does the delta-radiomics better differentiate pre-invasive GGNs from invasive GGNs? Front Oncol. 2020;10:1017. doi:10.3389/fonc.2020.0101732766129
   PubMed Web of Science ®Google Scholar
• Li H, Li A, Zhu H, et al. 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;26:632–639. doi:10.1016/j.acra.2018.06.01530087067
   PubMed Web of Science ®Google Scholar
• 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 carcinoma: compared with conventional CT analysis? Abdom Radiol (New York). 2020;45:2500–2507. doi:10.1007/s00261-020-02414-9
   PubMed Web of Science ®Google Scholar
• Hodgdon T, Mcinnes MD, Schieda N, Flood TA, Lamb L, Thornhill RE. Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology. 2015;276:787–796. doi:10.1148/radiol.201514221525906183
   PubMed Web of Science ®Google Scholar
• Shu J, Tang Y, Cui J, et al. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman Grade. Eur J Radiol. 2018;109:8–12. doi:10.1016/j.ejrad.2018.10.00530527316
   PubMed Web of Science ®Google Scholar
• Quan Y. CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging. 2019;19:11.30813956
   PubMed Web of Science ®Google Scholar
• Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P. Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: preliminary findings. Eur Radiol. 2017;27:4188–4197. doi:10.1007/s00330-016-4637-327778090
   PubMed Web of Science ®Google Scholar
• Kraemer HC. Correlation coefficients in medical research: from product moment correlation to the odds ratio. Stat Methods Med Res. 2006;15:525–545. doi:10.1177/096228020607065017260922
   PubMed Web of Science ®Google Scholar
• Sureka B, Khera PS. Radiologic classification and imaging features of renal angiomyolipomas according to the amount of fat. AJR Am J Roentgenol. 2018;210:136. doi:10.2214/AJR.17.19079
   Web of Science ®Google Scholar
• Yang R, Wu J, Sun L, et al. 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;30:1254–1263. doi:10.1007/s00330-019-06384-531468159
   PubMed Web of Science ®Google Scholar
• Lu Q, Ji C, Zhao X, et al. Histopathologic analysis of tumor bed and peritumoral pseudocapsule after in vitro tumor enucleation on radical nephrectomy specimen for clinical T1b renal cell carcinoma. Urol Oncol. 2017;35:603.e615–603.e620. doi:10.1016/j.urolonc.2017.05.017
   Web of Science ®Google Scholar
• Cocca D, Cochetti G, Zingaro MD, Lepri E, Mearini E. Evaluation of peritumoral pseudocapsule characteristics: preliminary results. Eur Urol Suppl. 2019;18:e3156. doi:10.1016/S1569-9056(19)33482-7
   Google Scholar
• Kim M, Choi WS, Jeong CW, et al. Differences in peritumoral pseudocapsule characteristics according to clinicopathological factors in clinical T1a renal tumors. Int J Clin Exp Pathol. 2015;8:11323–11331.26617856
   PubMed Web of Science ®Google Scholar
• Li QL, Cheng L, Guan H-W, Zhang Y, Wang F-P, Song X-S. Safety and efficacy of mini-margin nephron-sparing surgery for renal cell carcinoma 4-cm or less. Urology. 2008;71:924–927. doi:10.1016/j.urology.2007.11.07818267335
   PubMed Web of Science ®Google Scholar
• Li Q, Guan H, Qin J, Jiang T. Mini-Margin nephron sparing surgery for renal cell carcinoma 4 cm or less. Adv Urol. 2010;2010:145942. doi:10.1155/2010/145942
   PubMedGoogle Scholar
• Ha US, Lee KW, Jung JH, Byun SS, Hong SH. Renal capsular invasion is a prognostic biomarker in localized clear cell renal cell carcinoma. Sci Rep. 2018;8:202. doi:10.1038/s41598-017-18466-929317731
   PubMed Web of Science ®Google Scholar
• Song T, Yin Y, Liao B, Zheng S, Wei Q. Capsular invasion in renal cell carcinoma: a meta-analysis. Urol Oncol. 2013;31:1321–1326. doi:10.1016/j.urolonc.2011.12.01922285007
   PubMed Web of Science ®Google Scholar
• Feng Z, Rong P, Cao P, et al. 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. doi:10.1007/s00330-017-5118-z29134348
   PubMed Web of Science ®Google Scholar