Artificial Intelligence in Colorectal Cancer: From Patient Screening over Tailoring Treatment Decisions to Identification of Novel Biomarkers

Overview

Journal Digestion

Specialty Gastroenterology

Date 2024 Jun 12

PMID 38865982

Authors

Nic Gabriel Reitsam

Johanna Sophie Enke

Kien Vu Trung

Bruno Markl

Jakob Nikolas Kather

Affiliations

Soon will be listed here.

Abstract

Background: Artificial intelligence (AI) is increasingly entering and transforming not only medical research but also clinical practice. In the last 10 years, new AI methods have enabled computers to perform visual tasks, reaching high performance and thereby potentially supporting and even outperforming human experts. This is in particular relevant for colorectal cancer (CRC), which is the 3rd most common cancer type in general, as along the CRC patient journey many complex visual tasks need to be performed: from endoscopy over imaging to histopathology; the screening, diagnosis, and treatment of CRC involve visual image analysis tasks.

Summary: In all these clinical areas, AI models have shown promising results by supporting physicians, improving accuracy, and providing new biological insights and biomarkers. By predicting prognostic and predictive biomarkers from routine images/slides, AI models could lead to an improved patient stratification for precision oncology approaches in the near future. Moreover, it is conceivable that AI models, in particular together with innovative techniques such as single-cell or spatial profiling, could help identify novel clinically as well as biologically meaningful biomarkers that could pave the way to new therapeutic approaches.

Key Messages: Here, we give a comprehensive overview of AI in colorectal cancer, describing and discussing these developments as well as the next steps which need to be taken to incorporate AI methods more broadly into the clinical care of CRC.

Citing Articles

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Loper M, Makary M Tomography. 2024; 10(11):1814-1831.

PMID: 39590942 PMC: 11598375. DOI: 10.3390/tomography10110133.

Early diagnostic strategies for colorectal cancer.

Liu S, Zhang H World J Gastroenterol. 2024; 30(33):3818-3822.

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