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Review
. 2022 Mar 7;29(3):1773-1795.
doi: 10.3390/curroncol29030146.

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer

Hang Qiu  1   2 , Shuhan Ding  3 , Jianbo Liu  4   5 , Liya Wang  1 , Xiaodong Wang  4   5
Affiliations
Review

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer

Hang Qiu et al. Curr Oncol. .

Abstract

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients' survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.

Keywords: artificial intelligence; colorectal cancer; deep learning; diagnosis; machine learning; prognosis; screening; treatment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Clinical applications of AI for CRC. The inner circle represents the main data types in CRC research, including radiological images (i.e., Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc), endoscopic images, pathological images, clinical data, and omics data; the outer circle represents the four key clinical parts of CRC, i.e., screening, diagnosis, treatment, and prognosis; for each clinical part, AI has subdivided and specific tasks, which are shown in boxes outside the circle respectively. OS, overall survival; DFS, disease-free survival; nCRT, neoadjuvant radiotherapy.
Figure 2
Figure 2
Basics concepts of AI, ML, and DL. (a) The relationships of AI, ML, and DL; (b) The workflows of ML and DL.
Figure 2
Figure 2
Basics concepts of AI, ML, and DL. (a) The relationships of AI, ML, and DL; (b) The workflows of ML and DL.
Figure 3
Figure 3
Common CRC image types. (a) Endoscopy; (b) CT; (c) MRI; (d) pathology image (the Hematoxylin & Eosin (HE)-stained slide). (Image courtesy: West China Hospital, Sichuan University).
See this image and copyright information in PMC

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