Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps.

ABSTRACT:

Objectives

To investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning.

Methods

In this retrospective analysis of an average risk colorectal cancer screening sample, polyps of all size categories and morphologies were manually segmented on supine and prone CT colonography images and classified as premalignant (adenoma) or benign (hyperplastic polyp or regular mucosa) according to histopathology. Two deep learning models SEG and noSEG were trained on 3D CT colonography image subvolumes to predict polyp class, and model SEG was additionally trained with polyp segmentation masks. Diagnostic performance was validated in an independent external multicentre test sample. Predictions were analysed with the visualis

SUBMITTER: Wesp P 

PROVIDER: S-EPMC9213389 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature