154 related articles for article (PubMed ID: 33895621)
1. Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging.
Hussain MA; Hamarneh G; Garbi R
Comput Med Imaging Graph; 2021 Jun; 90():101924. PubMed ID: 33895621
[TBL] [Abstract][Full Text] [Related]
2. Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study.
Goyal A; Razik A; Kandasamy D; Seth A; Das P; Ganeshan B; Sharma R
Abdom Radiol (NY); 2019 Oct; 44(10):3336-3349. PubMed ID: 31300850
[TBL] [Abstract][Full Text] [Related]
3. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.
Nazari M; Shiri I; Hajianfar G; Oveisi N; Abdollahi H; Deevband MR; Oveisi M; Zaidi H
Radiol Med; 2020 Aug; 125(8):754-762. PubMed ID: 32193870
[TBL] [Abstract][Full Text] [Related]
4. Clear Cell Papillary Renal Cell Carcinoma: A Recent Entity With Distinct Imaging Patterns.
Tordjman M; Dbjay J; Chamouni A; Morini A; Timsit MO; Mejean A; Vasiliu V; Eiss D; Correas JM; Verkarre V; Helenon O
AJR Am J Roentgenol; 2020 Mar; 214(3):579-587. PubMed ID: 31770020
[No Abstract] [Full Text] [Related]
5. CT-based radiomics for differentiating renal tumours: a systematic review.
Bhandari A; Ibrahim M; Sharma C; Liong R; Gustafson S; Prior M
Abdom Radiol (NY); 2021 May; 46(5):2052-2063. PubMed ID: 33136182
[TBL] [Abstract][Full Text] [Related]
6. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade.
Deng Y; Soule E; Samuel A; Shah S; Cui E; Asare-Sawiri M; Sundaram C; Lall C; Sandrasegaran K
Eur Radiol; 2019 Dec; 29(12):6922-6929. PubMed ID: 31127316
[TBL] [Abstract][Full Text] [Related]
7. CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma.
Feng Z; Shen Q; Li Y; Hu Z
Cancer Imaging; 2019 Feb; 19(1):6. PubMed ID: 30728073
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
Zabihollahy F; Schieda N; Krishna S; Ukwatta E
Eur Radiol; 2020 Sep; 30(9):5183-5190. PubMed ID: 32350661
[TBL] [Abstract][Full Text] [Related]
11. Identification and validation of novel prognostic markers in Renal Cell Carcinoma.
Rabjerg M
Dan Med J; 2017 Oct; 64(10):. PubMed ID: 28975890
[TBL] [Abstract][Full Text] [Related]
12. Correlation between CT perfusion parameters and Fuhrman grade in pTlb renal cell carcinoma.
Chen C; Kang Q; Wei Q; Xu B; Ye H; Wang T; Lu Y; Lu J
Abdom Radiol (NY); 2017 May; 42(5):1464-1471. PubMed ID: 27999886
[TBL] [Abstract][Full Text] [Related]
13. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.
Cui E; Li Z; Ma C; Li Q; Lei Y; Lan Y; Yu J; Zhou Z; Li R; Long W; Lin F
Eur Radiol; 2020 May; 30(5):2912-2921. PubMed ID: 32002635
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Update on the Role of Imaging in Clinical Staging and Restaging of Renal Cell Carcinoma Based on the AJCC 8th Edition, From the
Elkassem AA; Allen BC; Sharbidre KG; Rais-Bahrami S; Smith AD
AJR Am J Roentgenol; 2021 Sep; 217(3):541-555. PubMed ID: 33759558
[TBL] [Abstract][Full Text] [Related]
16. Computed tomography perfusion imaging of renal cell carcinoma: systematic comparison with histopathological angiogenic and prognostic markers.
Reiner CS; Roessle M; Thiesler T; Eberli D; Klotz E; Frauenfelder T; Sulser T; Moch H; Alkadhi H
Invest Radiol; 2013 Apr; 48(4):183-91. PubMed ID: 23328912
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
[TBL] [Abstract][Full Text] [Related]
19. Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation.
Fenstermaker M; Tomlins SA; Singh K; Wiens J; Morgan TM
Urology; 2020 Oct; 144():152-157. PubMed ID: 32711010
[TBL] [Abstract][Full Text] [Related]
20. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade.
Shu J; Tang Y; Cui J; Yang R; Meng X; Cai Z; Zhang J; Xu W; Wen D; Yin H
Eur J Radiol; 2018 Dec; 109():8-12. PubMed ID: 30527316
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]