199 related articles for article (PubMed ID: 34800150)
1. A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images.
Wu K; Wu P; Yang K; Li Z; Kong S; Yu L; Zhang E; Liu H; Guo Q; Wu S
Eur Radiol; 2022 Apr; 32(4):2255-2265. PubMed ID: 34800150
[TBL] [Abstract][Full Text] [Related]
2. An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.
Yang H; Liu H; Lin J; Xiao H; Guo Y; Mei H; Ding Q; Yuan Y; Lai X; Wu K; Wu S
Eur Radiol; 2024 Jan; 34(1):355-366. PubMed ID: 37528301
[TBL] [Abstract][Full Text] [Related]
3. Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.
Kocak B; Ates E; Durmaz ES; Ulusan MB; Kilickesmez O
Eur Radiol; 2019 Sep; 29(9):4765-4775. PubMed ID: 30747300
[TBL] [Abstract][Full Text] [Related]
4. Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status.
Kocak B; Durmaz ES; Ates E; Ulusan MB
AJR Am J Roentgenol; 2019 Mar; 212(3):W55-W63. PubMed ID: 30601030
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.
Feng Z; Rong P; Cao P; Zhou Q; Zhu W; Yan Z; Liu Q; Wang W
Eur Radiol; 2018 Apr; 28(4):1625-1633. PubMed ID: 29134348
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma.
Yang H; Wu K; Liu H; Wu P; Yuan Y; Wang L; Liu Y; Zeng H; Li J; Liu W; Wu S
Eur Radiol; 2023 Nov; 33(11):7532-7541. PubMed ID: 37289245
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma.
Ding J; Xing Z; Jiang Z; Chen J; Pan L; Qiu J; Xing W
Eur J Radiol; 2018 Jun; 103():51-56. PubMed ID: 29803385
[TBL] [Abstract][Full Text] [Related]
12. Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study.
Liu DH; Dani KA; Reddy SS; Lei X; Demirjian NL; Hwang DH; Varghese BA; Rhie SK; Yap FY; Quinn DI; Siddiqi I; Aron M; Vaishampayan U; Zahoor H; Cen SY; Gill IS; Duddalwar VA
Oncology; 2023; 101(6):375-388. PubMed ID: 37080171
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.
Demirjian NL; Varghese BA; Cen SY; Hwang DH; Aron M; Siddiqui I; Fields BKK; Lei X; Yap FY; Rivas M; Reddy SS; Zahoor H; Liu DH; Desai M; Rhie SK; Gill IS; Duddalwar V
Eur Radiol; 2022 Apr; 32(4):2552-2563. PubMed ID: 34757449
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.
Luo S; Wei R; Lu S; Lai S; Wu J; Wu Z; Pang X; Wei X; Jiang X; Zhen X; Yang R
Eur Radiol; 2022 Apr; 32(4):2340-2350. PubMed ID: 34636962
[TBL] [Abstract][Full Text] [Related]
19. Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas.
Kocak B; Durmaz ES; Kaya OK; Kilickesmez O
Acta Radiol; 2020 Jun; 61(6):856-864. PubMed ID: 31635476
[TBL] [Abstract][Full Text] [Related]
20. Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma.
Tang Z; Yu D; Ni T; Zhao T; Jin Y; Dong E
AJR Am J Roentgenol; 2020 Feb; 214(2):370-382. PubMed ID: 31799870
[No Abstract] [Full Text] [Related]
[Next] [New Search]