Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis.

Abstract:

:Computerized image analysis for whole-slide images has been shown to improve efficiency, accuracy, and consistency in histopathology evaluations. We aimed to assess whether immunohistochemistry (IHC) image quantitative features can reflect the immune status and provide prognostic information for colorectal cancer patients. A fully automated pipeline was designed to extract histogram features from IHC digital images in a training set (N = 243). A Hist-Immune signature was generated with selected features using the LASSO Cox model. The results were validated using internal (N = 147) and external (N = 76) validation sets. The five-feature-based Hist-Immune signature was significantly associated with overall survival in training (HR 2.72, 95% CI 1.68-4.41, P < .001), internal (2.86, 1.28-6.39, 0.010), and external (2.30, 1.02-6.16, 0.044) validation sets. The full model constructed by integrating the Hist-Immune signature and clinicopathological factors had good discrimination ability (C-index 0.727, 95% CI 0.678-0.776), confirmed using internal (0.703, 0.621-0.784) and external (0.756, 0.653-0.859) validation sets. Our findings indicate that the Hist-Immune signature constructed based on the quantitative features could reflect the immune status of patients with colorectal cancer, which might advocate change in risk stratification and consequent precision medicine.

journal_name

Oncoimmunology

journal_title

Oncoimmunology

authors

Zhao K,Li Z,Li Y,Yao S,Huang Y,Wang Y,Zhang F,Wu L,Chen X,Liang C,Liu Z

doi

10.1080/2162402X.2020.1841935

subject

Has Abstract

pub_date

2020-10-30 00:00:00

pages

1841935

issue

1

eissn

2162-4011

issn

2162-402X

pii

1841935

journal_volume

9

pub_type

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