Research on establishing gastric cancer lymph node metastasis prediction model based on machine learning and routine laboratory indicators
10.3969/j.issn.1006-5725.2024.06.019
- VernacularTitle:基于机器学习利用常规检验指标建立胃癌淋巴结转移预测模型
- Author:
Jianliang YAN
1
,
2
;
Zeyu XIE
;
Rongrong JING
;
Ming CUI
Author Information
1. 南通大学附属医院检验科(江苏南通 226006)
2. 南通大学医学院 (江苏南通 226006)
- Keywords:
gastric cancer;
lymph node metastasis;
routine laboratory indicators;
machine learning
- From:
The Journal of Practical Medicine
2024;40(6):844-849
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a prediction model for lymph node metastasis(LNM)of gastric cancer based on routine laboratory indicators using machine learning algorithms.Methods This study collected data of 741 gastric cancer patients at Affiliated Hospital of Nantong University between January 2020 and January 2022 for model training and testing.Additionally,data of 102 gastric cancer patients between January 2023 and October 2023 were collected for model validation.XGBoost algorithm was used to calculate the importance of indicators and filter out a set of important indicators from 66 indicators.Five machine learning algorithms,including K-Nearest Neighbor,Support Vector Machine,Multilayer Perceptron,Random Forest and Adaboost,were constructed and trained for comparative analysis.Furthermore,the stability and accuracy of the model were further validated on the validation set.Results This study selected a set of important indicators composed of 9 routine laboratory indicators and trained the gastric cancer LNM prediction model,named V9.Additionally,through comparative experiments,it was found that the Adaboost algorithm based on the boosting strategy had the best performance,with evaluation metrics such as area under the curve,F1 score,accuracy,sensitivity,and specificity ranging from 0.833 to 0.968.The accuracy of the predictions on the validation set was 94.12%.Conclusion V9 was a gastric cancer LNM prediction model that has auxiliary clinical diagnostic value.It can be used to assess the risk of patients accurately and provide a basis for clinical decision-making.
