Open AccessMultiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma
Author(s) -
ShihWei Lai,
Lei Sun,
Jialiang Wu,
Ruili Wei,
Sanzhong Luo,
Wenshuang Ding,
Xiaoyang Liu,
Ruimeng Yang,
Xin Zhen
Publication year - 2021
Publication title -
cancer management and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.024
H-Index - 40
ISSN - 1179-1322
DOI - 10.2147/cmar.s290327
Subject(s) - discriminative model , clear cell renal cell carcinoma , artificial intelligence , feature (linguistics) , pattern recognition (psychology) , computer science , renal cell carcinoma , support vector machine , receiver operating characteristic , feature extraction , medicine , radiology , nuclear medicine , machine learning , pathology , linguistics , philosophy
To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features.