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

Ann Med. 2023 Dec;55(1):2215541. doi: 10.1080/07853890.2023.2215541.

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 score3 (trichotomy) was an independent prognostic factor, with higher TGP-I score3 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 score3 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 messagesA 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.

Keywords: Colorectal cancer; deep learning; tumor growth pattern; tumor-infiltrating lymphocytes; whole-slide images.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence*
  • Cell Proliferation
  • Clinical Decision-Making
  • Colorectal Neoplasms* / diagnosis
  • Eosine Yellowish-(YS)
  • Humans

Substances

  • Eosine Yellowish-(YS)

Grants and funding

This work was supported by Key-Area Research and Development Program of Guangdong Province (2021B0101420006), Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (U22A20345), National Science Fund for Distinguished Young Scholars (81925023), National Natural Science Foundation of China (82071892 and 82271941), the National Science Foundation for Young Scientists of China (82202267), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (2022B1212010011), High-level Hospital Construction Project (DFJHBF202105), and NSFC Incubation Project of Guangdong Provincial People’s Hospital (KY0120220037).