To identify optimal classification methods for computed tomography (CT) radiomicsbased preoperative prediction of clear cells renal cell carcinoma (ccRCC) grade.Methods and material: Seventy-one ccRCC patients (31 low-grade and 40 high-grade) were included in the study. All tumors were segmented manually on CT images, and three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) were applied on tumor volumes. In total, 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 of each feature with the histological condition. In the case of multivariate analysis, the following was implemented: three feature selection including the least absolute shrinkage and selection operator (LASSO), student's t-test and minimum Redundancy Maximum Relevance (mRMR) algorithms. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate the high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under receiver operating characteristic curve (AUC).Results: Univariate analysis demonstrated that among different image sets, 128 bin discretized images have statistically significant different (q-value < 0.05) texture parameters with a mean of AUC 0.74±3 (q-value < 0.05). The three ML-based classifier shows proficient discrimination of the high from low-grade ccRCC. The AUC was 0.78 in logistic regression, 0.62 in random forest, and 0.83 in SVM model, respectively. Conclusion:Radiomics features can be a useful and promising non-invasive method for preoperative evaluation of ccRCC Fuhrman grades.

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In order to establish diagnostic reference levels (DRLs) for multi-detector computed tomography (MDCT), four routine CT examinations were identified and a computer program was developed to collect data from 19 MDCT scanners in Iran. Mean values of Volume computed tomography dose index (CTDIvol) and dose-length product (DLP) in each site were calculated and the DRLs were defined as the 75th percentile of the distribution of the CTDIvol/DLP values for each examination. In terms of DLP, the DRLs of adult age group are 700, 290, 330, and 550 mGy cm for the Brain, Sinus, Chest, and Abdomen and Pelvis protocols, respectively. Although DRLs of this study are comparable to other international DRLs and in most cases are less than the international reference values, the great extent of dose distributions indicates that the CT imaging procedures in Iran should be optimized by applying diagnostic reference levels in order to decrease the radiation dose to patient undergoing CT examination.

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