BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

138 related articles for article (PubMed ID: 33813624)

  • 1. Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study.
    Haji-Momenian S; Ricker R; Chen Z; Houser M; Adusumilli N; Yang M; Toubaji A; Loew M
    Abdom Radiol (NY); 2021 Sep; 46(9):4266-4277. PubMed ID: 33813624
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 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. Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images.
    Nguyen K; Schieda N; James N; McInnes MDF; Wu M; Thornhill RE
    Eur Radiol; 2021 Mar; 31(3):1676-1686. PubMed ID: 32914197
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The value of quantitative CT texture analysis in differentiation of angiomyolipoma without visible fat from clear cell renal cell carcinoma on four-phase contrast-enhanced CT images.
    You MW; Kim N; Choi HJ
    Clin Radiol; 2019 Jul; 74(7):547-554. PubMed ID: 31010583
    [TBL] [Abstract][Full Text] [Related]  

  • 7. CT texture analysis for the differentiation of papillary renal cell carcinoma subtypes.
    Duan C; Li N; Niu L; Wang G; Zhao J; Liu F; Liu X; Ren Y; Zhou X
    Abdom Radiol (NY); 2020 Nov; 45(11):3860-3868. PubMed ID: 32444891
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes.
    Lubner MG; Stabo N; Abel EJ; Del Rio AM; Pickhardt PJ
    AJR Am J Roentgenol; 2016 Jul; 207(1):96-105. PubMed ID: 27145377
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers.
    Scrima AT; Lubner MG; Abel EJ; Havighurst TC; Shapiro DD; Huang W; Pickhardt PJ
    Abdom Radiol (NY); 2019 Jun; 44(6):1999-2008. PubMed ID: 29804215
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 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. Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT.
    Schieda N; Nguyen K; Thornhill RE; McInnes MDF; Wu M; James N
    Abdom Radiol (NY); 2020 Sep; 45(9):2786-2796. PubMed ID: 32627049
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Comparison of Contrast-Enhanced Multiphase Renal Protocol CT Versus MRI for Diagnosis of Papillary Renal Cell Carcinoma.
    Dilauro M; Quon M; McInnes MD; Vakili M; Chung A; Flood TA; Schieda N
    AJR Am J Roentgenol; 2016 Feb; 206(2):319-25. PubMed ID: 26797358
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 7.