Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study.

ABSTRACT:

Background

Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally.

Methods

532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. Th

SUBMITTER: Ji MY 

PROVIDER: S-EPMC7077008 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature