The Crohn’s-like lymphoid reaction density: a new artificial intelligence quantified prognostic immune index in colon cancer

Abstract

The Crohn’s-like lymphoid reaction (CLR) is manifested as peritumoral lymphocytes aggregation in colon cancer, which is a major component of the host immune response to cancer. However, the lack of a unified and objective CLR evaluation standard limits its clinical application. We, therefore, developed a deep learning model for the fully automated CLR density quantification on routine hematoxylin and eosin (HE)-stained whole-slide images (WSIs) and further investigated its prognostic validity for patient stratification. The CLR density was calculated by using a deep learning method on HE-stained WSIs. A training (N\u2009=\u2009279) and a validation (N\u2009=\u2009194) cohorts were used to evaluate the prognostic value of CLR density for overall survival (OS). The fully automated quantified CLR density was an independent prognostic factor, with high CLR density associated with increased OS in the discovery (HR 0.58, 95% CI 0.38–0.89, P\u2009=\u20090.012) and validation cohort (0.45, 0.23–0.88, 0.020). Integrating CLR density into a Cox model with other risk factors showed improved prognostic capability. We developed a new immune indicator (CLR density) quantified by a deep learning method to evaluate the lymphocytes aggregation in colon cancer. The CLR density was demonstrated its predictive value for OS in two independent cohorts. This approach allows for the objective and standardized quantification while reducing pathologists’ workload. Therefore, this fully automated standardized method of CLR evaluation had potential clinical value.