Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

Journal: European radiology
Published Date: Aug 30, 2018

Abstract

OBJECTIVE: To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).

Authors

  • Ceyda Turan Bektas
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey.
  • Burak Kocak
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey. drburakkocak@gmail.com.
  • Aytul Hande Yardimci
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey.
  • Mehmet Hamza Turkcanoglu
    Department of Radiology, Batman Women and Children's Health Training and Research Hospital, Batman, Turkey.
  • Ugur Yucetas
    Department of Urology, Istanbul Training and Research Hospital, Istanbul, Turkey.
  • Sevim Baykal Koca
    Department of Pathology, Istanbul Training and Research Hospital, Istanbul, Turkey.
  • Cagri Erdim
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey.
  • Ozgur Kilickesmez
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey.

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