These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

511 related articles for article (PubMed ID: 30601030)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 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. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model.
    Chen X; Zhou Z; Hannan R; Thomas K; Pedrosa I; Kapur P; Brugarolas J; Mou X; Wang J
    Phys Med Biol; 2018 Oct; 63(21):215008. PubMed ID: 30277889
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis.
    Schieda N; Thornhill RE; Al-Subhi M; McInnes MD; Shabana WM; van der Pol CB; Flood TA
    AJR Am J Roentgenol; 2015 May; 204(5):1013-23. PubMed ID: 25905936
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.
    Ren J; Yuan Y; Qi M; Tao X
    Eur Radiol; 2020 Dec; 30(12):6858-6866. PubMed ID: 32591885
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 26.