Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography.

ABSTRACT: In 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumor

SUBMITTER: Uhm KH 

PROVIDER: S-EPMC8213852 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature