# Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

## TL;DR

This paper reviews how artificial intelligence is being used to analyze colorectal cancer pathology images, showing promising results but highlighting the need for better data.

## Contribution

The paper provides a systematic review of AI applications in CRC pathology image analysis, identifying current trends and limitations.

## Key findings

- AI models for gland segmentation showed the most progress, with Ding et al.'s model performing best.
- Most studies had limited dataset size and quality, hindering clinical application.
- Tumor classification and prognosis prediction models are still in early development stages.

## Abstract

Colorectal cancer (CRC) is one of the most common cancers requiring early pathologic diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made significant progress and shown promising results in the field of medicine despite several limitations. We performed a systematic review of AI use in CRC pathology image analysis to visualize the state-of-the-art. Studies published between January 2000 and January 2020 were searched in major online databases including MEDLINE (PubMed, Cochrane Library, and EMBASE). Query terms included “colorectal neoplasm,” “histology,” and “artificial intelligence.” Of 9000 identified studies, only 30 studies consisting of 40 models were selected for review. The algorithm features of the models were gland segmentation (n = 25, 62%), tumor classification (n = 8, 20%), tumor microenvironment characterization (n = 4, 10%), and prognosis prediction (n = 3, 8%). Only 20 gland segmentation models met the criteria for quantitative analysis, and the model proposed by Ding et al. (2019) performed the best. Studies with other features were in the elementary stage, although most showed impressive results. Overall, the state-of-the-art is promising for CRC pathological analysis. However, datasets in most studies had relatively limited scale and quality for clinical application of this technique. Future studies with larger datasets and high-quality annotations are required for routine practice-level validation.