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

European Radiology
Ceyda Turan BektasOzgur Kilickesmez

Abstract

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). 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 cer...Continue Reading

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Citations

Oct 16, 2019·AJR. American Journal of Roentgenology·Burak KocakOzgur Kilickesmez
Jan 9, 2020·AJR. American Journal of Roentgenology·Takashi TanakaSusumu Kanazawa
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Nov 3, 2020·Abdominal Radiology·Abhishta BhandariMarita Prior
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Oct 13, 2021·Abdominal Radiology·Adam M AweMeghan G Lubner
Nov 11, 2021·European Radiology·Natalie L DemirjianVinay Duddalwar

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