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

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Abstract

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.

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Notes

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Acknowledgments

This work was supported by the Shahid Beheshti University of Medical Sciences under Grant Number 388 and the Swiss National Science Foundation under Grant SNRF 320030_176052.

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Authors and Affiliations

  1. Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mostafa Nazari & Mohammad Reza Deevband

  2. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland

    Isaac Shiri & Habib Zaidi

  3. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran

    Ghasem Hajianfar & Mehrdad Oveisi

  4. School of Population and Public Health, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Niki Oveisi

  5. Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University, Kerman, Iran

    Hamid Abdollahi

  6. Department of Computer Science, University of British Columbia, Vancouver, BC, Canada

    Mehrdad Oveisi

  7. Geneva University Neurocenter, Geneva University, Geneva, Switzerland

    Habib Zaidi

  8. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

    Habib Zaidi

  9. Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark

    Habib Zaidi

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  1. Mostafa Nazari
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Correspondence to Isaac Shiri or Mohammad Reza Deevband.

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All human subject studies were downloaded from The Cancer Imaging Archive (TCIA), an open-access database of medical images for cancer research. The site is funded by the National Cancer Institute’s Cancer Imaging Program, and the contract operated by the University of Arkansas for Medical Sciences.

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Nazari, M., Shiri, I., Hajianfar, G. et al. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning. Radiol med 125, 754–762 (2020). https://doi.org/10.1007/s11547-020-01169-z

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