242 related articles for article (PubMed ID: 34215760)
21. Ratio of maximum to minimum tumor diameter can predict the pathology type of renal cell carcinoma before surgery.
Jiang W; Wang D; Shi H; Shang B; Wen L; Zhang L; Zhang J; Zhang H; Zheng S; Shou J
Tumori; 2021 Feb; 107(1):64-70. PubMed ID: 32597325
[TBL] [Abstract][Full Text] [Related]
22. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.
Nie P; Yang G; Wang Z; Yan L; Miao W; Hao D; Wu J; Zhao Y; Gong A; Cui J; Jia Y; Niu H
Eur Radiol; 2020 Feb; 30(2):1274-1284. PubMed ID: 31506816
[TBL] [Abstract][Full Text] [Related]
23. A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma.
Yang L; Gao L; Arefan D; Tan Y; Dan H; Zhang J
BMC Med Imaging; 2022 Jan; 22(1):15. PubMed ID: 35094674
[TBL] [Abstract][Full Text] [Related]
24. Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.
Yin RH; Yang YC; Tang XQ; Shi HF; Duan SF; Pan CJ
J Xray Sci Technol; 2021; 29(6):1149-1160. PubMed ID: 34657848
[TBL] [Abstract][Full Text] [Related]
25. Development and validation of a CT-based nomogram for preoperative prediction of clear cell renal cell carcinoma grades.
Zheng Z; Chen Z; Xie Y; Zhong Q; Xie W
Eur Radiol; 2021 Aug; 31(8):6078-6086. PubMed ID: 33515086
[TBL] [Abstract][Full Text] [Related]
26. The value of a dual-energy CT Iodine map radiomics model for the prediction of collagen fiber content in the ccRCC tumor microenvironment.
Li Z; Wang N; Bing X; Li Y; Yao J; Li R; Ouyang A
BMC Med Imaging; 2023 Nov; 23(1):186. PubMed ID: 37968599
[TBL] [Abstract][Full Text] [Related]
27. 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.
Lee H; Hong H; Kim J; Jung DC
Med Phys; 2018 Apr; 45(4):1550-1561. PubMed ID: 29474742
[TBL] [Abstract][Full Text] [Related]
28. Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma?
Zhang GM; Shi B; Xue HD; Ganeshan B; Sun H; Jin ZY
Clin Radiol; 2019 Apr; 74(4):287-294. PubMed ID: 30554807
[TBL] [Abstract][Full Text] [Related]
29. Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis.
Wang W; Cao K; Jin S; Zhu X; Ding J; Peng W
Eur Radiol; 2020 Oct; 30(10):5738-5747. PubMed ID: 32367419
[TBL] [Abstract][Full Text] [Related]
30. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients.
Nazari M; Shiri I; Zaidi H
Comput Biol Med; 2021 Feb; 129():104135. PubMed ID: 33254045
[TBL] [Abstract][Full Text] [Related]
31. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors.
Zheng Y; Zhou D; Liu H; Wen M
Eur Radiol; 2022 Oct; 32(10):6953-6964. PubMed ID: 35484339
[TBL] [Abstract][Full Text] [Related]
32. Ultrasound contrast-enhanced radiomics model for preoperative prediction of the tumor grade of clear cell renal cell carcinoma: an exploratory study.
Luo Y; Liu X; Jia Y; Zhao Q
BMC Med Imaging; 2024 Jun; 24(1):135. PubMed ID: 38844837
[TBL] [Abstract][Full Text] [Related]
33. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.
Bektas CT; Kocak B; Yardimci AH; Turkcanoglu MH; Yucetas U; Koca SB; Erdim C; Kilickesmez O
Eur Radiol; 2019 Mar; 29(3):1153-1163. PubMed ID: 30167812
[TBL] [Abstract][Full Text] [Related]
34. Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas.
Pei X; Wang P; Ren JL; Yin XP; Ma LY; Wang Y; Ma X; Gao BL
Front Oncol; 2021; 11():659969. PubMed ID: 34123817
[TBL] [Abstract][Full Text] [Related]
35. The Application of CT Radiomics in the Diagnosis of Vein Wall Invasion in Patients With Renal Cell Carcinoma Combined With Tumor Thrombus.
Zhao X; Yan Y; Xie W; Zhao L; Zhang S; Liu J; Liu C; Ma L
Oncologist; 2024 Feb; 29(2):151-158. PubMed ID: 37672362
[TBL] [Abstract][Full Text] [Related]
36. Characterization of clear cell renal cell carcinoma and other renal tumors: evaluation of dual-energy CT using material-specific iodine and fat imaging.
Udare A; Walker D; Krishna S; Chatelain R; McInnes MD; Flood TA; Schieda N
Eur Radiol; 2020 Apr; 30(4):2091-2102. PubMed ID: 31858204
[TBL] [Abstract][Full Text] [Related]
37. A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma.
Meng X; Shu J; Xia Y; Yang R
Biomed Res Int; 2020; 2020():7103647. PubMed ID: 32775436
[TBL] [Abstract][Full Text] [Related]
38. CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.
Zhang H; Yin F; Chen M; Qi A; Yang L; Wen G
Br J Radiol; 2024 May; 97(1158):1169-1179. PubMed ID: 38688660
[TBL] [Abstract][Full Text] [Related]
39. The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma.
Bing X; Wang N; Li Y; Sun H; Yao J; Li R; Li Z; Ouyang A
Technol Cancer Res Treat; 2024; 23():15330338241235554. PubMed ID: 38404055
[TBL] [Abstract][Full Text] [Related]
40. Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?
Ma Y; Cao F; Xu X; Ma W
Abdom Radiol (NY); 2020 Aug; 45(8):2500-2507. PubMed ID: 31980867
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
