Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images

Abstract

Background: To explore whether radiomics combined with computed tomography (CT) images can be used to establish a model for differentiating high grade (International Society of Urological Pathology [ISUP] grade III-IV) from low-grade (ISUP I-II) clear cell renal cell carcinoma (ccRCC).

Methods: For this retrospective study, 3-phase contrast-enhanced CT images were collected from 227 patients with pathologically confirmed ISUP-grade ccRCC (155 cases in the low-grade group and 72 cases in the high-grade group). First, we delineated the largest dimension of the tumor in the corticomedullary and nephrographic CT images to obtain the region of interest. Second, variance selection, single variable selection, and the least absolute shrinkage and selection operator were used to select features in the corticomedullary phase, nephrographic phase, and 2-phase union samples, respectively. Finally, a model was constructed using the optimal features, and the receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the predictive performance of the features in the training and validation queues. A Z test was employed to compare the differences in AUC values.

Results: The support vector machine (SVM) model constructed using the screening features for the 2-stage joint samples can effectively distinguish between high- and low-grade ccRCC, and obtained the highest prediction accuracy. Its AUC values in the training queue and the validation queue were 0.88 and 0.91, respectively. The results of the Z test showed that the differences between the 3 groups were not statistically significant.

Conclusion: The SVM model constructed by CT-based radiomic features can effectively identify the ISUP grades of ccRCC.

Figure 1

Flow chart of patient recruitment with inclusion and exclusion details.

Figure 2

Schematic diagram of feature extraction and radiomics analysis for clear cell renal cell carcinoma grading. ROC = receiver operating characteristic, ROI = region of interest.

Figure 3

LASSO algorithm for feature selection in model 3. (A) LASSO path. (B) MSE path. (C) Coefficients in LASSO model. Using LASSO model, 7 features corresponding to the optimal alpha value were selected. 1 and 2 represent the corticomedullary phase and nephrographic phase, respectively.

Figure 4

Different receiver operating characteristic (ROC) curves for support vector machine classifiers. Comparison of ROC curves between model 1, model 2, and model 3 for the prediction of ISUP grading in the (A) training and (B) testing data sets.