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

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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).

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.

Results

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.

Conclusions

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.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

cc-RCC:

Clear cell renal cell carcinoma

CE-CT:

Contrast-enhanced computed tomography

CT :

Computed tomography

ICC:

Intra-class correlation coefficient

ML:

Machine learning

MLP:

Multilayer perceptron

RCC:

Renal cell carcinoma

ROC:

Receiver operating characteristic

ROI:

Region of interest

SVM:

Support vector machine

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Funding

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

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Authors and Affiliations

  1. Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey

    Ceyda Turan Bektas, Burak Kocak, Aytul Hande Yardimci, Cagri Erdim & Ozgur Kilickesmez

  2. Department of Radiology, Batman Women and Children’s Health Training and Research Hospital, Batman, Turkey

    Mehmet Hamza Turkcanoglu

  3. Department of Urology, Istanbul Training and Research Hospital, Istanbul, Turkey

    Ugur Yucetas

  4. Department of Pathology, Istanbul Training and Research Hospital, Istanbul, Turkey

    Sevim Baykal Koca

Authors
  1. Ceyda Turan Bektas
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Correspondence to Burak Kocak.

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

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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.

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• retrospective

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Bektas, C.T., Kocak, B., Yardimci, A.H. et al. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade. Eur Radiol 29, 1153–1163 (2019). https://doi.org/10.1007/s00330-018-5698-2

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