225 related articles for article (PubMed ID: 30633175)
1. Predictive models composed by radiomic features extracted from multi-detector computed tomography images for predicting low- and high- grade clear cell renal cell carcinoma: A STARD-compliant article.
He X; Zhang H; Zhang T; Han F; Song B
Medicine (Baltimore); 2019 Jan; 98(2):e13957. PubMed ID: 30633175
[TBL] [Abstract][Full Text] [Related]
2. Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images.
He X; Wei Y; Zhang H; Zhang T; Yuan F; Huang Z; Han F; Song B
Acad Radiol; 2020 Feb; 27(2):157-168. PubMed ID: 31147235
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.
Shu J; Wen D; Xi Y; Xia Y; Cai Z; Xu W; Meng X; Liu B; Yin H
Eur J Radiol; 2019 Dec; 121():108738. PubMed ID: 31756634
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma.
Li Q; Liu YJ; Dong D; Bai X; Huang QB; Guo AT; Ye HY; Tian J; Wang HY
J Magn Reson Imaging; 2020 Nov; 52(5):1557-1566. PubMed ID: 32462799
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram.
Cheng D; Abudikeranmu Y; Tuerdi B
Curr Med Imaging; 2023; 19(9):1005-1017. PubMed ID: 36411581
[TBL] [Abstract][Full Text] [Related]
11. Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools.
Wang X; Song G; Jiang H
Cancer Imaging; 2021 Jul; 21(1):47. PubMed ID: 34225784
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma.
Lin F; Cui EM; Lei Y; Luo LP
Abdom Radiol (NY); 2019 Jul; 44(7):2528-2534. PubMed ID: 30919041
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.
Sun X; Liu L; Xu K; Li W; Huo Z; Liu H; Shen T; Pan F; Jiang Y; Zhang M
Medicine (Baltimore); 2019 Apr; 98(14):e15022. PubMed ID: 30946334
[TBL] [Abstract][Full Text] [Related]
16. Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas.
Wang P; Pei X; Yin XP; Ren JL; Wang Y; Ma LY; Du XG; Gao BL
Sci Rep; 2021 Jul; 11(1):13729. PubMed ID: 34215760
[TBL] [Abstract][Full Text] [Related]
17. 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]
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. Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging.
Sha X; Gong G; Qiu Q; Duan J; Li D; Yin Y
BMC Med Imaging; 2020 Feb; 20(1):12. PubMed ID: 32024469
[TBL] [Abstract][Full Text] [Related]
20. Use of specific contrast-enhanced CT regions of interest to differentiate renal oncocytomas from small clear cell and chromophobe renal cell carcinomas.
Qu JY; Jiang H; Wang XF; Song XH; Hao CJ
Diagn Interv Radiol; 2022 Nov; 28(6):555-562. PubMed ID: 36550755
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
