CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma

  • Fan Lin
  • En-Ming Cui
  • Yi Lei
  • Liang-ping LuoEmail author
Kidneys, Ureters, Bladder, Retroperitoneum



To predict the Fuhrman grade of clear cell renal cell carcinoma (ccRCC) with a machine learning classifier based on single- or three-phase computed tomography (CT) images.

Materials and methods

Patients with pathologically proven ccRCC from February 1, 2009 to September 31, 2018 who were not treated were retrospectively collected for machine learning-based analysis. The texture features were extracted and ranked from precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP) and three-phase CT images, and open-source gradient boosting from the decision tree library of CatBoost was used to establish a machine learning classifier to differentiate low- from high-grade ccRCC. The performances of machine learning classifiers based on features from single- and three-phase CT images were compared with each other.


A total of 231 patients with 232 pathologically proven ccRCC lesions were retrospectively collected. 35, 36, 41, and 22 Features were extracted and ranked from PCP, CMP, NP, and three-phase CT images, respectively. The machine learning model based on three-phase CT images [area under the ROC curve (AUC) = 0.87] achieved the best diagnostic performance for differentiating low- from high-grade ccRCC, followed by single-phase NP (AUC = 0.84), CMP (AUC = 0.80), and PCP images (AUC = 0.82).


Machine learning classifiers can be promising noninvasive techniques to differentiate low- and high-Fuhrman nuclear grade ccRCC, and classifiers based on three-phase CT images are superior to those based on features from each single phase.


Machine learning Texture analysis Clear cell carcinoma Fuhrman nuclear grade 


Compliance with ethical standards

Conflict of interest

The authors declare there are no conflicts of interest regarding the publication of this paper.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Medical Imaging CenterThe First Affiliated Hospital of Jinan UniversityGuangzhouChina
  2. 2.Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science CenterShenzhen Second People’s HospitalShenzhenChina
  3. 3.Department of Radiology, Jiangmen Central HospitalAffiliated Jiangmen Hospital of Sun YAT-SEN UniversityJiangmenChina

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