European Radiology

, Volume 29, Issue 3, pp 1153–1163 | Cite as

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

  • Ceyda Turan Bektas
  • Burak KocakEmail author
  • Aytul Hande Yardimci
  • Mehmet Hamza Turkcanoglu
  • Ugur Yucetas
  • Sevim Baykal Koca
  • Cagri Erdim
  • Ozgur Kilickesmez



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).

Materials and methods

This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16–145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics.


Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885–0.998), three run-length matrix (ICC range, 0.889–0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941–0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively.


The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs.

Key Points

• Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy.

• Highest predictive performance was obtained with use of the SVM.

• SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.


Clear cell renal cell carcinoma Artificial intelligence Multidetector computed tomography Machine learning Fuhrman nuclear grade 



Apparent diffusion coefficient


Area under the curve


Clear cell renal cell carcinoma


Contrast-enhanced computed tomography


Computed tomography


Intra-class correlation coefficient


Machine learning


Multilayer perceptron


Renal cell carcinoma


Receiver operating characteristic


Region of interest


Support vector machine



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Burak Kocak, MD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Burak Kocak, MD, the second and corresponding author, has significant statistical expertise.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyIstanbul Training and Research HospitalIstanbulTurkey
  2. 2.Department of RadiologyBatman Women and Children’s Health Training and Research HospitalBatmanTurkey
  3. 3.Department of UrologyIstanbul Training and Research HospitalIstanbulTurkey
  4. 4.Department of PathologyIstanbul Training and Research HospitalIstanbulTurkey

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