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.
Keywords: Whole-slide image; colorectal cancer; immunohistochemistry; overall survival; quantitation.
© 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.