1. Moch H., Cubilla A.L., Humphrey P.A. et al. Ulbright. The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs - Part A: Renal, Penile, and Testicular Tumours // Eur. Urol. 2016. Vol. 70, No. 1. R. 93-105. https://doi.org/10.1016/J.EURURO.2016.02.029.
2. Halefoglu A.M., Ozagari A.A. Tumor grade estimation of clear cell and papillary renal cell carcinomas using contrast-enhanced MDCT and FSE T2 weighted MR imaging: radiology-pathology correlation // Radiol. Med. 2021. Vol. 126, No. 9. R. 1139-1148. https://doi.org/10.1007/S11547-021-01350-Y.
3. Miles K.A., Ganeshan B., Hayball M.P. CT texture analysis using the filtration-histogram method: what do the measurements mean? // Cancer Imaging. 2013. Vol. 13, No. 3. R. 400-406. https://doi.org/10.1102/1470-7330.2013.9045.
4. Nioche C., Orlhac F., Boughdad S. et al. Lifex: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity // Cancer Res. 2018. Vol. 78, No. 16. R. 4786-4789. https://doi.org/10.1158/0008-5472.CAN-18-0125.
5. Schieda N., Lim R.S., Krishna S. et al. Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma // AJR. Am. J. Roentgenol. 2018. Vol. 210, No. 5. R. 1079-1087. https://doi.org/10.2214/AJR.17.18874.
6. Bektas C.T., Kocak B., Yardimci A.H. et al. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade // Eur. Radiol. 2019. Vol. 29, No. 3. R. 1153-1163. https://doi.org/10.1007/S00330-018-5698-2.
7. Cornelis F., Tricaud E., Lasserre A.S. et al. Multiparametric magnetic resonance imaging for the differentiation of low- and high-grade clear cell renal carcinoma // Eur. Radiol. 2015. Vol. 25, No. 1. R. 24-31. https://doi.org/10.1007/S00330-014-3380-X.
8. Oh S., Sung D.J., Yang K.S. et al. Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma // Acta Radiologica. 2016. Vol. 58, No. 3. R. 376-384. https://doi.org/10.1177/0284185116649795.
9. Sun R., Zhao S., Jiang H. et al. Imaging Tool for Predicting Renal Clear Cell Carcinoma Fuhrman Grade: Comparing R.E.N.A.L. Nephrometry Score and CT Texture Analysis // Biomed. Res. Int. 2021. Vol. 1. R. 1-11. https://doi.org/10.1155/2021/1821876.
10. Muglia V.F., Prando A. Renal cell carcinoma: histological classification and correlation with imaging findings // Radiol. Bras. 2015. Vol. 48, No. 3. R. 166-174. https://doi.org/10.1590/0100-3984.2013.1927.
11. Zhu Y.H., Wang X., Zhang J., Chen Y.H. et al. Low enhancement on multiphase contrast-enhanced CT images: an independent predictor of the presence of high tumor grade of clear cell renal cell carcinoma // AJR. Am. J. Roentgenol. 2014. Vol. 203, No. 3. R. W295-W300. https://doi.org/10.2214/AJR.13.12297.
12. Goyal A., Sharma R., Bhalla A.S. et al. Diffusion-weighted MRI in renal cell carcinoma: A surrogate marker for predicting nuclear grade and histological subtype // Acta radiol. 2012. Vol. 53, No. 3. R. 349-358. https://doi.org/10.1258/AR.2011.110415/ASSET/IMAGES/LARGE/10.1258_AR.2011.110415-FIG2.JPEG.
13. Yi X., Xiao Q., Zeng F. et al. Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma // Front. Oncol. 2020. Vol. 10. R. 570396-570396. https://doi.org/10.3389/FONC.2020.570396.
14. Kim N.Y., Lubner M.G., Nystrom J.T. et al. Utility of CT Texture Analysis in Differentiating Low-Attenuation Renal Cell Carcinoma From Cysts: A Bi-Institutional Retrospective Study // American Journal of Roentgenology. 2019. Vol. 213, No. 6. R. 1259-1266. https://doi.org/10.2214/AJR.19.21182.
15. Yu H.S., Scalera J., Khalid M. et al. Texture analysis as a radiomic marker for differentiating renal tumors // Abdom. Radiol. 2017. Vol. 42, No. 10. R. 2470-2478. https://doi.org/10.1007/S00261-017-1144-1/TABLES/4.
16. Ding J., Xing Z., Jiang Z. et al. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma // Eur. J. Radiol. Vol. 103. R. 51-56. https://doi.org/10.1016/J.EJRAD.2018.04.013.
17. Paschall A.K., Mirmomen S.M., Symons R. et al. Differentiating papillary type I RCC from clear cell RCC and oncocytoma: application of whole-lesion volumetric ADC measurement // Abdom. Radiol. (New York). 2018. Vol. 43, No. 9. R. 2424-2430. https://doi.org/10.1007/S00261-017-1453-4.
18. Shen L., Zhou L., Liu X. et al. Comparison of biexponential and monoexponential DWI in evaluation of Fuhrman grading of clear cell renal cell carcinoma // Diagnostic Interv. Radiol. 2017. Vol. 23, No. 2, p. 100. https://doi.org/10.5152/DIR.2016.15519.
19. Villavicencio C.P., McCarthy R.J., Miller F.H. Can diffusion-weighted magnetic resonance imaging of clear cell renal carcinoma predict low from high nuclear grade tumors // Abdom. Radiol. (New York). 2017. Vol. 42, No. 4. R. 1241-1249. https://doi.org/10.1007/S00261-016-0981-7.
20. Mytsyk Y., Dutka I., Borys Y. et al. Renal cell carcinoma: applicability of the apparent coefficient of the diffusion-weighted estimated by MRI for improving their differential diagnosis, histologic subtyping, and differentiation grade // Int. Urol. Nephrol. 2017. Vol. 49, No. 2. R. 215-224. https://doi.org/10.1007/S11255-016-1460-3.
21. Adams L.C., Bressem K.K., Jurmeister P. et al. Use of quantitative T2 mapping for the assessment of renal cell carcinomas: First results // Cancer Imaging. 2019. Vol. 19, No. 1. R. 1-11. https://doi.org/10.1186/S40644-019-0222-8/FIGURES/5.
22. Zhang Y.D., Wu C.J., Wang Q. et al. Comparison of Utility of Histogram Apparent Diffusion Coefficient and R2* for Differentiation of Low-Grade From High-Grade Clear Cell Renal Cell Carcinoma // AJR. Am. J. Roentgenol. 2015. Vol. 205, No. 2. R. W193-W201. https://doi.org/10.2214/AJR.14.13802.
23. Moran K., Abreu-Gomez J., Krishn S. et al. Can MRI be used to diagnose histologic grade in T1a (<4 cm) clear cell renal cell carcinomas? // Abdom. Radiol. (New York). 2019. Vol. 44, No. 8. R. 2841-2851. https://doi.org/10.1007/S00261-019-02018-Y.
24. Kierans A.S., Rusinek H., Lee A. et al. Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma // AJR. Am. J. Roentgenol. 2014. Vol. 203, No. 6. R. W637-W644. https://doi.org/10.2214/AJR.14.12570.
25. Jiang Y., Li W., Huang C. et al. A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma // Front. Oncol. 2020. Vol. 10. R. 592. https://doi.org/10.3389/FONC.2020.00592/BIBTEX.
26. Tordjman M., Mali R., Madelin G. et al. Diagnostic test accuracy of ADC values for identification of clear cell renal cell carcinoma: systematic review and meta-analysis // Eur. Radiol. 2020. Vol. 30, No. 7. R. 4023-4038. https://doi.org/10.1007/S00330-020-06740-W.
27. Vendrami C.L., Velichko Y.S., Miller F.H. et al. Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis // AJR. Am. J. Roentgenol. 2018. Vol. 211, No. 6. R. 1234-1245. https://doi.org/10.2214/AJR.17.19213.
28. Kocak B., Ates E., Durmaz E.S. et al. 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. Vol. 29, No. 9. R. 4765-4775. https://doi.org/10.1007/S00330-019-6003-8/TABLES/5.
29. Lin F., Cui E.M., Lei Y. et al. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma // Abdom. Radiol. 2019. Vol. 44, No. 7. R. 2528-2534. https://doi.org/10.1007/S00261-019-01992-7/TABLES/2.
30. Cui E., Li Z., Ma C. et al. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics // Eur. Radiol. 2020. Vol. 30, No. 5. R. 2912-2921. https://doi.org/10.1007/S00330-019-06601-1/FIGURES/3.
31. Espinasse M., Pitre-Champagnat S., Charmettant B. et al. CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review // Diagnostics 2020. Vol. 10, No. 5. R. 258. https://doi.org/doi.org/10.3390/diagnostics10050258.
32. Buvat I., Orlhac F., Soussan M. Tumor Texture Analysis in PET: Where Do We Stand? // J. Nucl. Med. 2015. Vol. 56, No. 11. R. 1642-1644. https://doi.org/10.2967/JNUMED.115.163469.
33. Kocak B., Yardimci A.H., Bektas C.T. et al. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation // Eur. J. Radiol. 2018. Vol. 107. R. 149-157. https://doi.org/10.1016/J.EJRAD.2018.08.014.
34. Lee H. S., Hong H., Jung D.C. et al. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification // Med. Phys. 2017. Vol. 44, No. 7. R. 3604-3614, https://doi.org/10.1002/MP.12258.
35. Leng S., Takahashi N., Gomez Cardona D. et al. Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT // Abdom. Radiol. 2017. Vol. 42, No. 5. R. 1485-1492. https://doi.org/10.1007/S00261-016-1014-2/FIGURES/5.
36. Nazari M., Shiri I., Hajianfar G. et al. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning // Radiol. Medica. 2020. Vol. 125, No. 8. R. 754-762. https://doi.org/10.1007/S11547-020-01169-Z/TABLES/4.
37. Sun J., Pan L., Zha T. et al. The role of MRI texture analysis based on susceptibility-weighted imaging in predicting Fuhrman grade of clear cell renal cell carcinoma // Acta radiol. 2021. Vol. 62, No. 8. R. 1104-1111. https://doi.org/10.1177/0284185120951964/ASSET/IMAGES/LARGE/10.1177_0284185120951964-FIG2.JPEG.
38. Nguyen K., Schieda N., James N. et al. 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. Vol. 31, No. 3. R. 1676-1686. https://doi.org/10.1007/S00330-020-07233-6/TABLES/5.
39. Lubner M.G., Stabo N., Abel E.J. et al. CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes // Am. J. Roentgenol. 2016. Vol. 207, No. 1. R. 96-105. https://doi.org/10.2214/AJR.15.15451.
40. Lai S., Sun L., Wu J. et al. Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma // Cancer Manag. Res. 2021. Vol. 13. R. 999. https://doi.org/10.2147/CMAR.S290327.
41. Deng Y., Soule E., Samuel A. et al. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade // Eur. Radiol. 2019. Vol. 29, No. 12. R. 6922-6929. https://doi.org/10.1007/S00330-019-06260-2/FIGURES/4.
42. Feng Z., Shen Q., Li Y. et al. CT texture analysis: A potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma // Cancer Imaging. 2019. Vol. 19, No. 1. R. 1-7. https://doi.org/10.1186/S40644-019-0195-7/FIGURES/2.
43. Haji-Momenian S., Lin Z., Patel B. et al. Texture analysis and machine learning algorithms accurately predict histologic grade in small (<4 cm) clear cell renal cell carcinomas: a pilot study // Abdom. Radiol. 2020. Vol. 45, No. 3. R. 789-798. https://doi.org/10.1007/S00261-019-02336-1/TABLES/3.
44. Scrima A.T., Lubner M.G., Abel E.J. et al. Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers // Abdom. Radiol. 2019. Vol. 44, No. 6. R. 1999-2008. https://doi.org/10.1007/S00261-018-1649-2/FIGURES/3.
45. 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. Vol. 109. R. 8-12. https://doi.org/10.1016/J.EJRAD.2018.10.005.
46. Wu K., Wu P., Yang K. et al. A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images // Eur. Radiol. 2022. Vol. 32, No. 4. R. 2255-2265. https://doi.org/10.1007/S00330-021-08353-3/FIGURES/4.
47. Gao R., Qin H., Lin P. et al. Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma // Front. Oncol. 2021. Vol. 11. R. 2347. https://doi.org/10.3389/FONC.2021.613668/BIBTEX.
48. Demirjian N.L., Varghese B.A., Cen S.Y. et al. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma // Eur. Radiol. 2022. Vol. 32, No. 4. R. 2552-2563. https://doi.org/10.1007/S00330-021-08344-4/TABLES/3.
49. Zhang H., Yin F., Chen M. et al. Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma // Front. Oncol. 2022. Vol. 11. https://doi.org/10.3389/FONC.2021.742547.
50. Shehata M., Alksas A., Abouelkheir R.T. et al. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors // Sensors. 2021. Vol. 21, No. 14. R. 4928. https://doi.org/10.3390/S21144928.
51. Goyal A., Razik A., Kandasamy D. et al. Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study // Abdom. Radiol. 2019. Vol. 44, No. 10. R. 3336-3349. https://doi.org/10.1007/S00261-019-02122-Z/FIGURES/4.
52. Wang W., Cao K.M., Jin S.M. et al. Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis // Eur. Radiol. 2020. Vol. 30, No. 10. R. 5738-5747. https://doi.org/10.1007/S00330-020-06896-5/FIGURES/2.
53. Razik A., Goyal A., Sharma R. et al. MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma // Br. J. Radiol. 2020. Vol. 93, No. 1114. https://doi.org/10.1259/BJR.20200569.