BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

394 related articles for article (PubMed ID: 31635476)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma.
    Zeng H; Chen L; Wang M; Luo Y; Huang Y; Ma X
    Aging (Albany NY); 2021 Mar; 13(7):9960-9975. PubMed ID: 33795526
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.
    Erdim C; Yardimci AH; Bektas CT; Kocak B; Koca SB; Demir H; Kilickesmez O
    Acad Radiol; 2020 Oct; 27(10):1422-1429. PubMed ID: 32014404
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations.
    Karlo CA; Di Paolo PL; Chaim J; Hakimi AA; Ostrovnaya I; Russo P; Hricak H; Motzer R; Hsieh JJ; Akin O
    Radiology; 2014 Feb; 270(2):464-71. PubMed ID: 24029645
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Development of unenhanced CT-based imaging signature for BAP1 mutation status prediction in malignant pleural mesothelioma: Consideration of 2D and 3D segmentation.
    Xie XJ; Liu SY; Chen JY; Zhao Y; Jiang J; Wu L; Zhang XW; Wu Y; Duan H; He B; Luo H; Han D
    Lung Cancer; 2021 Jul; 157():30-39. PubMed ID: 34052706
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings.
    Feng Z; Zhang L; Qi Z; Shen Q; Hu Z; Chen F
    Front Oncol; 2020; 10():279. PubMed ID: 32185138
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Reliability of Single-Slice-Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility.
    Kocak B; Durmaz ES; Kaya OK; Ates E; Kilickesmez O
    AJR Am J Roentgenol; 2019 Aug; 213(2):377-383. PubMed ID: 31063427
    [No Abstract]   [Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 20.