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Journal Abstract Search

423 related items for 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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 Apr; 10():279. PubMed ID: 32185138
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  • 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
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