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

Nazari, M. and Shiri, I. and Hajianfar, G. and Oveisi, N. and Abdollahi, H. and Deevband, M.R. and Oveisi, M. and Zaidi, H. (2020) Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning. Radiologia Medica, 125 (8). pp. 754-762.

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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. © 2020, Italian Society of Medical Radiology.

Item Type: Article
Additional Information: cited By 1
Uncontrolled Keywords: cancer grading; computer assisted diagnosis; diagnostic imaging; female; human; kidney tumor; machine learning; male; middle aged; pathology; procedures; renal cell carcinoma; x-ray computed tomography, Carcinoma, Renal Cell; Female; Humans; Kidney Neoplasms; Machine Learning; Male; Middle Aged; Neoplasm Grading; Radiographic Image Interpretation, Computer-Assisted; Tomography, X-Ray Computed
Subjects: WJ Urogenital System
QZ Pathology
Depositing User: eprints admin
Date Deposited: 23 Sep 2020 04:52
Last Modified: 23 Sep 2020 04:52

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