139 related articles for article (PubMed ID: 37080171)
21. 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]
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. 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]
24. CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma.
Chen M; Yin F; Yu Y; Zhang H; Wen G
Cancer Imaging; 2021 Jun; 21(1):42. PubMed ID: 34162442
[TBL] [Abstract][Full Text] [Related]
25. Radiogenomics in Clear Cell Renal Cell Carcinoma: Correlations Between Advanced CT Imaging (Texture Analysis) and MicroRNAs Expression.
Marigliano C; Badia S; Bellini D; Rengo M; Caruso D; Tito C; Miglietta S; Palleschi G; Pastore AL; Carbone A; Fazi F; Petrozza V; Laghi A
Technol Cancer Res Treat; 2019 Jan; 18():1533033819878458. PubMed ID: 31564221
[TBL] [Abstract][Full Text] [Related]
26. Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT.
Han D; Yu Y; Yu N; Dang S; Wu H; Jialiang R; He T
Br J Radiol; 2020 Oct; 93(1114):20200131. PubMed ID: 32706977
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. 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]
29. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.
Kocak B; Yardimci AH; Bektas CT; Turkcanoglu MH; Erdim C; Yucetas U; Koca SB; Kilickesmez O
Eur J Radiol; 2018 Oct; 107():149-157. PubMed ID: 30292260
[TBL] [Abstract][Full Text] [Related]
30. Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective.
Li ZC; Zhai G; Zhang J; Wang Z; Liu G; Wu GY; Liang D; Zheng H
Eur Radiol; 2019 Aug; 29(8):3996-4007. PubMed ID: 30523454
[TBL] [Abstract][Full Text] [Related]
31. Texture analysis as a radiomic marker for differentiating renal tumors.
Yu H; Scalera J; Khalid M; Touret AS; Bloch N; Li B; Qureshi MM; Soto JA; Anderson SW
Abdom Radiol (NY); 2017 Oct; 42(10):2470-2478. PubMed ID: 28421244
[TBL] [Abstract][Full Text] [Related]
32. 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.
Yang R; Wu J; Sun L; Lai S; Xu Y; Liu X; Ma Y; Zhen X
Eur Radiol; 2020 Feb; 30(2):1254-1263. PubMed ID: 31468159
[TBL] [Abstract][Full Text] [Related]
33. CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma.
Wen R; Huang J; Gao RZ; Wan D; Qin H; Peng YT; Liang YQ; Li X; Wang XR; He Y; Yang H
J Comput Assist Tomogr; 2021 Sep-Oct 01; 45(5):696-703. PubMed ID: 34347707
[TBL] [Abstract][Full Text] [Related]
34. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Validation of CT radiomics for prediction of distant metastasis after surgical resection in patients with clear cell renal cell carcinoma: exploring the underlying signaling pathways.
Zhao Y; Liu G; Sun Q; Zhai G; Wu G; Li ZC
Eur Radiol; 2021 Jul; 31(7):5032-5040. PubMed ID: 33439312
[TBL] [Abstract][Full Text] [Related]
37. CT-Based Radiomics Signature for Preoperative Prediction of Coagulative Necrosis in Clear Cell Renal Cell Carcinoma.
Xu K; Liu L; Li W; Sun X; Shen T; Pan F; Jiang Y; Guo Y; Ding L; Zhang M
Korean J Radiol; 2020 Jun; 21(6):670-683. PubMed ID: 32410406
[TBL] [Abstract][Full Text] [Related]
38. Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.
Haji-Momenian S; Lin Z; Patel B; Law N; Michalak A; Nayak A; Earls J; Loew M
Abdom Radiol (NY); 2020 Mar; 45(3):789-798. PubMed ID: 31822969
[TBL] [Abstract][Full Text] [Related]
39. Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma.
Shieh A; Cen SY; Varghese BA; Hwang D; Lei X; Setayesh A; Siddiqi I; Aron M; Dsouza A; Gill IS; Wallace W; Duddalwar V
Oncology; 2024; 102(3):260-270. PubMed ID: 37699367
[TBL] [Abstract][Full Text] [Related]
40. A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma.
Lin F; Ma C; Xu J; Lei Y; Li Q; Lan Y; Sun M; Long W; Cui E
Eur J Radiol; 2020 Aug; 129():109079. PubMed ID: 32526669
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]