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Chinese Journal of Colorectal Diseases(Electronic Edition) ›› 2024, Vol. 13 ›› Issue (05): 389-393. doi: 10.3877/cma.j.issn.2095-3224.2024.05.006

• Original Articles • Previous Articles     Next Articles

Preliminary study on prediction of lymph node metastasis of T1 colorectal cancer by random forest model

Weiwei Zhang1, Qi Chen1, Heyu Weng1, Liang Huang2,()   

  1. 1.Department of Colorectal Surgery, Sir Run Run Hospital, Nanjing Medical University, Nanjing 211100, China
    2.Department of Colorectal Surgery, the Sixth Affiliated Hospital of Sun Yat Sen University, Guangzhou 510655, China
  • Received:2024-07-09 Online:2024-10-25 Published:2024-11-14
  • Contact: Liang Huang

Abstract:

Objective

In order to explore the risk factors of lymph node metastasis of stage T1 colorectal cancer and establish a random forest model to predict lymph node metastasis of stage T1 colorectal cancer patients.

Methods

We collected the clinical data of 280 patients with stage T1 colorectal cancer who underwent radical surgery in the Sixth Affiliated Hospital of Sun Yat-sen University from August 2010 to June 2020,and established a random forest model to predict lymph node metastasis of stage T1 colorectal cancer patients by R software.

Results

The incidence of lymph node metastasis in T1 stage colorectal cancer was 11.07% (31/280). The importance ranking of risk factors following the random forest model for lymph node metastasis in stage T1 colorectal cancer was as follows: age,vascular or neural invasion,tumor location,differentiation degree,gender,tumor size,and Ki67 level. The area under the curve (AUC) of our random forest model for lymph node metastasis in stage T1 colorectal cancer was 0.811,with an optimal threshold of 0.072,specificity of 0.703 and sensitivity of 0.778.

Conclusion

The random forest model is helpful for clinicians to quickly analyze the risk of lymph node metastasis in stage T1 colorectal cancer and make appropriate treatment plans.

Key words: Colorectal neoplasms, Lymph node metastasis, Risk factors, Random forest model

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