Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study.
Aged
Colonic Neoplasms
/ diagnostic imaging
Colonic Polyps
/ diagnostic imaging
Colonography, Computed Tomographic
Contrast Media
Diagnosis, Differential
Female
Humans
Image Interpretation, Computer-Assisted
Machine Learning
Male
Mass Screening
Middle Aged
Precancerous Conditions
/ diagnostic imaging
Proof of Concept Study
Prospective Studies
Journal
Radiology
ISSN: 1527-1315
Titre abrégé: Radiology
Pays: United States
ID NLM: 0401260
Informations de publication
Date de publication:
05 2021
05 2021
Historique:
pubmed:
24
2
2021
medline:
7
8
2021
entrez:
23
2
2021
Statut:
ppublish
Résumé
Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonography in an average-risk asymptomatic colorectal cancer screening sample with external validation using radiomics. Materials and Methods In this secondary analysis of a prospective trial, colorectal polyps of all size categories and morphologies were manually segmented on CT colonographic images and were classified as benign (hyperplastic polyp or regular mucosa) or premalignant (adenoma) according to the histopathologic reference standard. Quantitative image features characterizing shape (
Identifiants
pubmed: 33620287
doi: 10.1148/radiol.2021202363
doi:
Substances chimiques
Contrast Media
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM