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Cancer Cell International
Published in: Cancer Cell International 1/2022

01-12-2022 | Metastasis | Primary research

Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

Authors: Tenghui Han, Jun Zhu, Xiaoping Chen, Rujie Chen, Yu Jiang, Shuai Wang, Dong Xu, Gang Shen, Jianyong Zheng, Chunsheng Xu

Published in: Cancer Cell International | Issue 1/2022

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Abstract

Background

Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically.

Methods

We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model.

Results

A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956).

Conclusion

We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.
Appendix
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Metadata
Title
Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer
Authors
Tenghui Han
Jun Zhu
Xiaoping Chen
Rujie Chen
Yu Jiang
Shuai Wang
Dong Xu
Gang Shen
Jianyong Zheng
Chunsheng Xu
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2022
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-021-02424-7

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