Fully automated segmentation and classification of renal tumors on CT scans via machine learning.

ABSTRACT:

Background

To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.

Materials and methods

The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas. Renal tumor masks manually drawn on contrast-enhanced CT images were used to develop a 3D U-Net-based deep learning model for fully automated tumor segmentation. After segmentation, the entire classification pipeline, including fea

SUBMITTER: Han JH 

PROVIDER: S-EPMC11781067 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature