Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

Mostafa Nazari*, Isaac Shiri, Ghasem Hajianfar, Niki Oveisi, Hamid Abdollahi, Mohammad Reza Deevband, Mehrdad Oveisi, Habib Zaidi

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    72 Citations (Scopus)
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    Purpose To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade. Materials and methods Seventy-one ccRCC patients (31 low grade and 40 high grade) were included in this study. Tumors were manually segmented on CT images followed by the application of three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) on delineated tumor volumes. Overall, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association between each feature and the histological condition. Multivariate analysis involved the use of machine learning (ML) algorithms and the following three feature selection algorithms: the least absolute shrinkage and selection operator, Student's t test, and minimum Redundancy Maximum Relevance. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under the receiver operating characteristic curve (AUC) metric. Results The univariate analysis demonstrated that among the different image sets, 128 bin-discretized images have statistically significant different texture parameters with a mean AUC of 0.74 +/- 3 (q value <0.05). The three ML-based classifiers showed proficient discrimination between high and low-grade ccRCC. The AUC was 0.78 for logistic regression, 0.62 for random forest, and 0.83 for the SVM model, respectively. Conclusion CT radiomic features can be considered as a useful and promising noninvasive methodology for preoperative evaluation of ccRCC Fuhrman grades.

    Original languageEnglish
    Pages (from-to)754-762
    Number of pages9
    JournalRadiologia medica
    Issue number8
    Publication statusPublished - Aug-2020


    • Computed tomography (CT)
    • Radiomics
    • Machine learning
    • Renal cell carcinoma
    • Fuhrman grading
    • MASSES
    • BIOPSY
    • SYSTEM
    • SIZE

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