A deep learning quantified stroma-immune score to predict survival of patients with stage II-III colorectal cancer.

ABSTRACT:

Background

Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II-III), advocating the identification of valuable biomarkers that could improve the prognostic stratification. This study aims to develop a deep learning-based pipeline for fully automatic quantification of immune infiltration within the stroma region on immunohistochemical (IHC) whole-slide images (WSIs) and further analyze its prognostic value in CRC.

Methods

Patients from two independent cohorts were divided into three groups: the development group (N = 200), the internal (N = 134), and the external validation group (N = 90). We trained a convolutional neural network for tissue classification of CD3 and CD8 stained WSIs. A scorin

SUBMITTER: Xu Z 

PROVIDER: S-EPMC8557607 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature