Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis

ConclusionShape and first-order features extracted from UP  + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear gradi ng.Key Points•Lesion delineation uncertainties are tolerated within a VOI erosion range of 2  mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading.•Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC.•Shape features and first-order statistics features showed superior discriminative capability compared to texture features.
Source: European Radiology - Category: Radiology Source Type: research