Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions.

ABSTRACT:

Background

The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the m

SUBMITTER: He QH 

PROVIDER: S-EPMC9834471 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature