BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

242 related articles for article (PubMed ID: 31063427)

  • 1. 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]  

  • 2. 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]  

  • 3. Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors.
    Gitto S; Cuocolo R; Emili I; Tofanelli L; Chianca V; Albano D; Messina C; Imbriaco M; Sconfienza LM
    J Digit Imaging; 2021 Aug; 34(4):820-832. PubMed ID: 34405298
    [TBL] [Abstract][Full Text] [Related]  

  • 4. CT texture analysis reliability in pulmonary lesions: the influence of 3D vs. 2D lesion segmentation and volume definition by a Hounsfield-unit threshold.
    Adelsmayr G; Janisch M; Kaufmann-Bühler AK; Holter M; Talakic E; Janek E; Holzinger A; Fuchsjäger M; Schöllnast H
    Eur Radiol; 2023 May; 33(5):3064-3071. PubMed ID: 36947188
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping.
    Varghese BA; Chen F; Hwang DH; Cen SY; Desai B; Gill IS; Duddalwar VA
    AJR Am J Roentgenol; 2018 Dec; 211(6):W288-W296. PubMed ID: 30240299
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Assessment of Renal Cell Carcinoma by Texture Analysis in Clinical Practice: A Six-Site, Six-Platform Analysis of Reliability.
    Doshi AM; Tong A; Davenport MS; Khalaf AM; Mresh R; Rusinek H; Schieda N; Shinagare AB; Smith AD; Thornhill R; Vikram R; Chandarana H
    AJR Am J Roentgenol; 2021 Nov; 217(5):1132-1140. PubMed ID: 33852355
    [No Abstract]   [Full Text] [Related]  

  • 11. Comparison of CT Texture Analysis Software Platforms in Renal Cell Carcinoma: Reproducibility of Numerical Values and Association With Histologic Subtype Across Platforms.
    Dreyfuss LD; Abel EJ; Nystrom J; Stabo NJ; Pickhardt PJ; Lubner MG
    AJR Am J Roentgenol; 2021 Jun; 216(6):1549-1557. PubMed ID: 33852332
    [No Abstract]   [Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?
    Ho LM; Samei E; Mazurowski MA; Zheng Y; Allen BC; Nelson RC; Marin D
    AJR Am J Roentgenol; 2019 Mar; 212(3):554-561. PubMed ID: 30620676
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?
    Hodgdon T; McInnes MD; Schieda N; Flood TA; Lamb L; Thornhill RE
    Radiology; 2015 Sep; 276(3):787-96. PubMed ID: 25906183
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation.
    Yamashita R; Perrin T; Chakraborty J; Chou JF; Horvat N; Koszalka MA; Midya A; Gonen M; Allen P; Jarnagin WR; Simpson AL; Do RKG
    Eur Radiol; 2020 Jan; 30(1):195-205. PubMed ID: 31392481
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.