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Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.

Shiwei LuoRuili WeiSonglin LuShengsheng LaiJialiang WuZhe WuXinrui PangXinhua WeiXinqing JiangXin ZhenRui-Meng Yang
Published in: European radiology (2021)
• Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.
Keyphrases
  • contrast enhanced
  • machine learning
  • lymph node metastasis
  • computed tomography
  • magnetic resonance
  • squamous cell carcinoma
  • dual energy
  • deep learning
  • big data