[1] SIEGEL RL,MILLER KD,FUCHS HE,et al.Cancer statistics,2021[J].CA Cancer J Clin,2021,71(1):7-33.
[2] DE LEON AD,KAPUR P,PEDROSA I.Radiomics in kidney cancer:MR imaging[J].Magn Reson Imaging Clin N Am,2019,27(1):1-13.
[3] GILLIES RJ,KINAHAN PE,HRICAK H.Radiomics:Images are more than pictures,they are data[J].Radiology,2016,278(2):563-577.
[4] ERDIM C,YARDIMCI AH,BEKTAS CT,et al.Prediction of benign and malignant solid renal masses:Machine learning-based CT texture analysis[J].Acad Radiol,2020,27(10):1422-1429.
[5] 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.[J] Eur Radiol,2019,29(12):6922-6929.
[6] LI ZC,ZHAI G,ZHANG J,et al.Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT:A VHL mutation perspective[J].Eur Radiol,2019,29(8):3996-4007.
[7] ANTUNES J,VISWANATH S,RUSU M,et al.Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma:A proof-of-concept study[J].Transl Oncol,2016,9(2):155-162.
[8] KUTIKOV A,FOSSETT LK,RAMCHANDANI P,et al.Incidence of benign pathologic findings at partial nephrectomy for solitary renal mass presumed to be renal cell carcinoma on preoperative imaging[J].Urology,2006,68(4):737-740.
[9] CUI EM,LIN F,LI Q,et al.Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features[J].Acta Radiol,2019,60(11):1543-1552.
[10] 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[J].Eur Radiol,2020,30(2):1254-1263.
[11] MA Y,CAO F,XU X,et al.Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma:Compared with conventional CT analysis[J].Abdom Radiol (NY),2020,45(8):2500-2507.
[12] NIE P,YANG G,WANG Z,et al.A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma[J].Eur Radiol,2020,30(2):1274-1284.
[13] KOCAK B,YARDIMCI AH,BEKTAS CT,et al.Textural differences between renal cell carcinoma subtypes:Machine learning-based quantitative computed tomography texture analysis with independent external validation[J].Eur J Radiol,2018,107:149-157.
[14] LIU ZH,BAI X,YE HY,et al.The value of texture analysis and machine learning based on T2WI in distinguishing renal hypolipid angiomyolipoma and renal carcinoma [J].Chinese Journal Magnetic Resonance Imaging,2021,12(02):38-42.
[15] LI Y,HUANG X,XIA Y,LONG L.Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma[J].Abdom Radiol (NY),2020,45(10):3193-3201.
[16] COY H,HSIEH K,WU W,et al.Deep learning and radiomics:The utility of Google tensor flowTM inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT[J].Abdom Radiol (NY),2019,44(6):2009-2020.
[17] KIM NY,LUBNER MG,NYSTORM JT,et al.Utility of CT texture analysis in differentiating low-attenuation renal cell carcinoma from cysts:A Bi-institutional retrospective study[J].AJR Am J Roentgenol,2019,213(6):1259-1266.
[18] MISKIN N,QIN L,MATALON SA,et al.Stratification of cystic renal masses into benign and potentially malignant:Applying machine learning to the bosniak classification[J].Abdom Radiol (NY),2021,46(1):311-318.
[19] KUNAPULI G,VARGHESE BA,GANAPATHY P,et al.A decision-support tool for renal mass classification[J].J Digit Imaging,2018,31(6):929-939.
[20] 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[J].Eur Radiol,2021,31(3):1676-1686.
[21] KOCAK B,YARDIMCI AH,BEKTAS CT,et al.Textural differences between renal cell carcinoma subtypes:Machine learning-based quantitative computed tomography texture analysis with independent external validation[J].Eur J Radiol,2018,107:149-157.
[22] 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[J].Abdom Radiol (NY),2019,44(10):3336-3349.
[23] WANG W,CAO K,JIN S,et al.Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis[J].Eur Radiol,2020,30(10):5738-5747.
[24] MOCH H,CUBILA AL,HUMPHREY PA,et al.The 2016 WHO classification of tumours of the urinary system and male genital organs-part A:Renal,penile,and testicular tumours[J].Eur Urol,2016,70(1):93-105.
[25] WANG X,SONG G,JIANG H,et al.Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma[J].Abdom Radiol (NY),2021,46(9):4289-4300.
[26] SHU J,WEN D,XI Y,et al.Clear cell renal cell carcinoma:Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade[J].Eur J Radiol,2019,121:108738.
[27] MOLDOVANU CG,BOCA B,LEBOVICI A,et al.Preoperative predicting the WHO/ISUP nuclear grade of clear cell renal cell carcinoma by computed tomography-based radiomics features[J].J Pers Med,2020,11(1):8.
[28] 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[J].Eur Radiol,2020,30(5):2912-2921.
[29] GILL TS,VARGHESE BA,HWANG DH,et al.Juxtatumoral perinephric fat analysis in clear cell renal cell carcinoma[J].Abdom Radiol (NY),2019,44(4):1470-1480.
[30] YIN Q,HUNG SC,WANG L,et al.Associations between tumor vascularity,vascular endothelial growth factor expression and PET/MRI radiomic signatures in primary clear-cell-renal-cell-carcinoma:Proof-of-concept study[J].Sci Rep,2017,7:43356.
[31] COY H,YOUNG JR,PANTUCK AJ,et al.Association of tumor grade,enhancement on multiphasic CT and microvessel density in patients with clear cell renal cell carcinoma[J].Abdom Radiol (NY),2020,45(10):3184-3192.
[32] YIN Q,HUNG SC,RATHMELL WK,et al.Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma[J].Clin Radiol,2018,73(9):782-791.
[33] GHOSH P,TAMBOLI P,VIKRAM R,et al.Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features[J].J Med Imaging (Bellingham),2015,2(4):041009.
[34] BAI X,HUANG Q,ZUO P,et al.MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma[J].Eur Radiol,2021,31(2):1029-1042.
[35] BAI X,WANG HY,YE HY,et al.Texture analysis based on T_2 weighted images:Comparison of texture features of renal cancer between different metastatic sites[J].Radiology Practice,2018,33(08):794-799.
[36] EISENHAUER EA,THERASSE P,BOGAERTS J,et al.New response evaluation criteria in solid tumours:Revised RECIST guideline (version 1.1)[J].Eur J Cancer,2009,45(2):228-247.
[37] SMITH AD,ZHANG X,BYRAN J,et al.Vascular tumor burden as a new quantitative CT biomarker for predicting metastatic RCC response to antiangiogenic therapy[J].Radiology,2016,281(2):484-498.
[38] ANTUNES J,VISWANATH S,RUSU M,et al.Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma:A proof-of-concept study[J].Transl Oncol,2016,9(2):155-162.
[39] GOH V,GANESHAN B,NATHAN P,et al.Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer:CT texture as a predictive biomarker[J].Radiology,2011,261(1):165-171.
[40] HAIDER MA,VOSOUGH A,KHALVATI F,et al.CT texture analysis:A potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib[J].Cancer Imaging,2017,17(1):4.
[41] SHI Z,LIU Q.Research and challenges of imaging omics technology methods[J].Radiology Practice,2018,33(6):633-636.