Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer

Abstract

Background In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring system, the TGP-I score, to assess the association and interactions between tumor growth pattern (TGP) and tumor infiltrating lymphocytes at the IM and to predict its prognostic validity for CRC patient stratification. Materials and Methods The types of TGP were assessed in hematoxylin and eosin-stained whole-slide images. The CD3 + T-cells density at the IM was automatically quantified on immunohistochemical-stained slides using a deep learning method. A discovery ( N = 347) and a validation ( N = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival. Results The TGP-I score 3 (trichotomy) was an independent prognostic factor, with higher TGP-I score 3 associated with worse prognosis in the discovery (unadjusted hazard ratio [HR] for high vs. low 3.62, 95% confidence interval [CI] 2.22–5.90; p < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84–18.20; p = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score 3 had similar importance compared to tumor-node-metastasis staging (31.2% vs. 32.9%) and was stronger than other clinical parameters. Conclusions This automated workflow and the proposed TGP-I score could further provide accurate prognostic stratification and have potential value for supporting the clinical decision-making of stage I–III CRC patients. Key messages A new scoring system, the TGP-I score, was proposed to assess the association and interactions of TGP and TILs at the tumor invasive margin. TGP-I score could be an independent predictor of prognosis for CRC patients, with higher scores being associated with worse survival. TGP-I score had similar importance compared to tumor-node-metastasis staging and was stronger than other clinical parameters.

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

“…In the health care field, artificial intelligence (AI) is characterized by data management and processing, offering new possibilities to the health care paradigm [ 24 ]. Some applications of AI in the health care domain include assessing tumor interaction processes [ 25 ], serving as a tool for image-based diagnostics [ 26 , 27 ], participating in virus detection [ 28 ], and, most importantly, as a statistical and predictive method [ 29 - 32 ].…”