Robust whole-tumour 3D volumetric CT-based radiomics approach for predicting the WHO/ISUP grade of a ccRCC tumour

Clear Cell Renal Cell Carcinoma (ccRCC) is a common and major type of cancer. It is clinically vital to know the nuclear grade of a ccRCC tumour pre-surgically and non-invasively as this may impact therapeutic actions. We hypothesise that using three-dimensional (3D) radiomic features and ensemble learning models can increase the discrimination power between grades. The hybrid feature selection method was used to reduce the number of features. Besides the actual tumour volume, five additional volumes of interests (VOls) were created to consider peritumour regions and test the robustness of the model against variations in segmentation. The best result was acquired when Synthetic Minority Oversampling Technique (SMOTE) was used in combination with Light Gradient Boosting Machine (LightGBM). The area under the curve (AUC) for this model was 0.89 -+/- 0.02. The results were 0.86 and 0.83 when the contour was made smaller and larger by 2 mm. We conclude that a ccRCC tumour grade can be predicted from 3D CT images with a high reliability, despite the inadequacy of a dataset. The algorithm is moderately robust against deviations in segmentation by observers. The features from the peritumour area did not result in any significant improvement.

Automated summary

This study aimed to improve the discrimination power of clearing cell renal cell carcinoma (ccRCC) tumor grades using three-dimensional radiomic features and ensemble learning models. The results showed that the proposed model achieved high reliability in predicting the tumor grade from 3D CT images, despite the limited dataset. The algorithm demonstrated moderate robustness against segmentation deviations by observers, while features from the peritumour area did not significantly improve the results.