基于机器学习的早期胃癌淋巴结转移预测模型构建与验证

孟祥勇; 秦嘉怡; 陈文生

doi:10.16016/j.2097-0927.202403126

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基于机器学习的早期胃癌淋巴结转移预测模型构建与验证

临床医学 | 更新时间：2026-01-09

- 基于机器学习的早期胃癌淋巴结转移预测模型构建与验证
- Construction and validation of a prediction model for lymph node metastasis in early gastric cancer based on machine learning
- 陆军军医大学学报 2024年46卷第21期页码：2432-2442
- 作者机构：
  
  400038 重庆，陆军军医大学(第三军医大学)第一附属医院消化内科
- 作者简介：
  
  [陈文生，E-mail：2303141134@qq.com CHEN Wensheng, E-mail: 2303141134@qq.com
- 基金信息：
  
  利益冲突所有作者声明无利益冲突;作者贡献声明孟祥勇：数据统计分析，文章撰写；秦嘉怡：数据核对；陈文生：研究指导、论文审阅
- DOI：10.16016/j.2097-0927.202403126
  中图分类号： R319;R73-37;R735.2
- 收稿：2024-03-31，
  
  修回：2024-06-29，
  
  纸质出版：2024-11-15
- 稿件说明：
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孟祥勇, 秦嘉怡, 陈文生. 基于机器学习的早期胃癌淋巴结转移预测模型构建与验证[J]. 陆军军医大学学报, 2024,46(21):2432-2442.

Xiangyong MENG, Jiayi QIN, Wensheng CHEN. Construction and validation of a prediction model for lymph node metastasis in early gastric cancer based on machine learning[J]. Journal of Army Medical University, 2024, 46(21): 2432-2442.
孟祥勇, 秦嘉怡, 陈文生. 基于机器学习的早期胃癌淋巴结转移预测模型构建与验证[J]. 陆军军医大学学报, 2024,46(21):2432-2442. DOI： 10.16016/j.2097-0927.202403126.

Xiangyong MENG, Jiayi QIN, Wensheng CHEN. Construction and validation of a prediction model for lymph node metastasis in early gastric cancer based on machine learning[J]. Journal of Army Medical University, 2024, 46(21): 2432-2442. DOI： 10.16016/j.2097-0927.202403126.

摘要

目的

通过机器学习构建最佳的早期胃癌淋巴结转移(lymphatic metastasis，LNM)预测模型，并对其性能进行验证。

方法

收集2015年1月至2022年12月在本院行根治性手术治疗的433例早期胃癌患者的临床资料。以7 ∶3的比例划分为训练集和验证集。运用LASSO回归筛选变量，并基于多因素Logistic回归确定早期胃癌LNM的独立危险因素。应用类别型特征梯度提升机(categorical boosting

Catboost)、轻量的梯度提升机(light gradient boosting machine

LightGBM)、极端梯度提升机(eXtreme gradient boosting machine

XGboost)、随机森林(random forest

RF)、梯度提升树(gradient boosting machine

GBM)、神经网络(neural networks

NNET)、支持向量机(support vector machine

SVM)、K最临近(K-nearest

KNN)、朴素贝叶斯(naiveBayes

NB)及Logistic回归共10种ML算法构建预测模型。利用准确率、精确率、召回率、F1评分值、灵敏度、特异度、阳性预测率、阴性预测率、Kappa值、受试者工作特征曲线下面积值(AUC)、校准曲线、决策曲线以及精确率-召回率曲线评估和比较模型的预测能力。基于Shaply加性解释(SHapley additive exPlanations，SHAP)方法解释最佳模型中各变量对发生结局影响的贡献度。

结果

肿瘤浸润深度、淋巴血管浸润和吸烟史是早期胃癌LNM的独立危险因素。Catboost模型具有最佳预测性能，其在训练集中的5个性能指标均优于其他模型[AUC：0.904(95%

0.868-0.940)，F1评分：0.633，Brier评分：0.100，阴性预测率：0.975，Kappa：0.520

]

。通过计算Catboost的SHAP值发现，肿瘤浸润深度和淋巴血管浸润是预测LNM的2个关键特征变量。

结论

肿瘤浸润深度为黏膜下层、淋巴血管浸润和吸烟史是早期胃癌LNM的独立危险因素。机器学习可用于预测LNM风险，Catboost模型具有最佳预测性能，并可为临床诊断和治疗决策提供指导。

Abstract

Objective

To construct an optimal prediction model for lymph node metastasis (LNM) in early gastric cancer (EGC) using machine learning techniques and assess its predictive performance.

Methods

Clinical data of 433 EGC patients undergoing radical surgery in our hospital from January 2015 to December 2022 were collected. They were divided into a training set and a validation set in a 7 ∶3 ratio. LASSO regression was used to screen variables and multivariate logistic regression analysis was employed to identify independent risk factors for LNM in the EGC patients. Ten machine learning models were constructed using categorical boosting (Catboost)

light gradient boosting machine (LightGBM)

extreme gradient boosting machine (XGboost)

random forest (RF)

gradient boosting machine (GBM)

neural networks (NNET)

support vector machine (SVM)

K nearest (KNN)

Naive Bayes (NB) and Logistic regression. The predictive power of the above models was evaluated and compared in terms of accuracy

precision

recall

F1 score value

sensitivity

specificity

positive predictive rate

negative predictive rate

Kappa value

area value under the receiver operating characteristic curve (AUC)

calibration curve

decision curve

and precision-recall curve. Finally

SHAP (SHapley Additive exPlanations) was applied to explain the contribution of each variable in the best model for the prediction outcomes.

Results

Depth of tumor invasion

lymphovascular invasion and smoking history were independent risk factors for LNM in the EGC patients. Catboost model obtained the best predictive performance

and had 5 performance indicators outperforming the other models in the training set

that is

an AUC value of 0.904 (95%

0.868~0.940)

a F1-score of 0.633

a Brier score of 0.100

a negative predictive rate of 0.975

and a Kappa value of 0.520. Finally

calculating the SHAP values of Catboost revealed that the depth of tumor invasion and lymphovascular invasion were two key characteristic variables for predicting LNM.

Conclusion

The depth of tumor invasion of submucosal and lymphovascular invasion and smoking history are independent risk factors for LNM in early gastric cancer. ML can be used to predict LNM risk

the Catboost model has the best predictive performance and can provide guidance for clinical diagnosis and treatment decisions.

关键词

Keywords

references

郑荣寿, 陈茹, 韩冰峰, 等. 2022年中国恶性肿瘤流行情况分析[J]. 中华肿瘤杂志, 2024, 46(3): 221-231. DOI: 10.3760/cma.j.cn112152-20240119-00035.

ZHENG R S, CHEN R, HAN B F, et al . Analysis on the prevalence of malignant tumors in China in 2022[J ] . Ch in J Oncol, 2024, 46(3): 221-231. DOI: 10.3760/cma.j.cn112152-20240119-00035.

CARDOSO R, COBURN N, SEEVARATNAM R, et al . A systematic review and meta-analysis of the utility of EUS for preoperative staging for gastric cancer[J ] . Gastric Cancer, 2012, 15(Suppl 1): S19-S26. DOI: 10.1007/s10120-011-0115-4.

OLIVEIRA A L. Biotechnology, big data and artificial intelligence[J]. Biotechnol J, 2019, 14(8): e1800613. DOI: 10.1002/biot.201800613.

MIRZA B, WANG W, WANG J, et al . Machine learning and integrative analysis of biomedical big data[J ] . Genes, 2019, 10(2): 87. DOI: 10.3390/genes10020087.

WHO. International guide for monitoring alcohol consumption and related harm[M]. Geneva: WHO, 2000: 54.

Japanese Gastric Cancer Association. Japanese gastric cancer treatment guidelines 2018(5th edition)[J]. Gastric Cancer, 2021, 24(1): 1-21. DOI: 10.1007/s10120-020-01042-y.

SAITO T, KUROKAWA Y, TAKIGUCHI S, et al . Accuracy of multidetector-row CT in diagnosing lymph node metastasis in patients with gastric cancer[J ] . EurRadiol, 2015, 25(2): 368-374. DOI: 10.1007/s00330-014- 3373-9.

MOCELLIN S, PASQUALI S. Diagnostic accuracy of endoscopic ultrasonography(EUS) for the preoperative locoregional staging of primary gastric cancer[J]. Cochrane Database Syst Rev, 2015, 2015(2): CD009944. DOI: 10.1002/14651858.CD009944.pub2.

ZHANG M Q, DING C, XU L, et al . A nomogram to predict risk of lymph node metastasis in early gastric cancer[J ] . Sci Rep, 2021, 11(1): 22873. DOI: 10.1038/s41598-021-02305-z.

WEI J T, ZHANG Y N, LIU Y Q, et al . Construction and validation of a risk-scoring model that preoperatively predicts lymph node metastasis in early gastric cancer patients[J ] . Ann Surg Oncol, 2021, 28(11): 6665-6672. DOI: 10.1245/s10434-021-09867-2.

ROTA M, POSSENTI I, VALSASSINA V, et al . Dose-response association between cigarette smoking and gastric cancer risk: a systematic review and meta-analysis[J ] . Gastric Cancer, 2024, 27(2): 197-209. DOI: 10.1007/s10120-023-01459-1.

李园园, 吕民, 李苓. 早期胃癌患者淋巴结转移的影响因素及其生存情况[J]. 中国卫生工程学, 2023, 22(4): 494-496. DOI: 10.19937/j.issn.1671-4199.2023.04.019.

LI Y Y, LYU M, LI L. Influencing factors and survival status of lymph node metastasis in patients with early gastric cancer[J]. Chin J Public Health Eng, 2023, 22(4): 494-496. DOI: 10.19937/j.issn.1671-4199.2023.04.019.

刘庆伟, 李勇, 檀碧波, 等. 早期胃癌患者临床病理特征与淋巴结转移因素分析[J]. 中华普通外科杂志, 2022, 37(4): 255-259. DOI: 10.3760/cma.j.cn113855-20210831-00525.

LIU Q W, LI Y, TAN B B, et al . Clinicopathological characteristics and lymph node metastasis in patients with early gastric cancer[J ] . Chin J Gen Surg, 2022, 37(4): 255-259. DOI: 10.3760/cma.j.cn113855-20210831-00525.

ZENG Q W, LI H, ZHU Y Y, et al . Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer[J ] . Front Med, 2022, 9: 986437. DOI: 10.3389/fmed.2022.986437.

RYU E S, CHANG S J, AN J, et al . Sex-specific differences in risk factors of lymph node metastasis in patients with early gastric cancer[J ] . PLoS One, 2019, 14(10): e0224019. DOI: 10.1371/journal.pone.0224019.

MILHOMEM L M, MILHOMEM-CARDOSO D M, DA MOTA O M, et al . Risk of lymph node metastasis in early gastric cancer and indications for endoscopic resection: is it worth applying the east rules to the west?[J ] . Surg Endosc, 2021, 35(8): 4380-4388. DOI: 10.1007/s00464-020-07932-7.

YANG T, MARTINEZ-USEROS J, LIU J W, et al . A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer[J ] . Front Oncol, 2022, 12: 1023110. DOI: 10.3389/fonc.2022.1023110.

NA J E, LEE Y C, KIM T J, et al . Machine learning model to stratify the risk of lymph node metastasis for early gastric cancer: a single-center cohort study[J ] . Cancers, 2022, 14(5): 1121. DOI: 10.3390/cancers14051121.

PROKHORENKOVA L, GUSEV G, VOROBEV A, et al . CatBoost: unbiased boosting with categorical features[C ] . Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal: Curran Associates Inc., 2018: 6639-6649. DOI: 10.48550/arXiv.1706.09516.

SAFAEI N, SAFAEI B, SEYEDEKRAMI S, et al . E-CatBoost: an efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database[J ] . PLoS One, 2022, 17(5): e0262895. DOI: 10.1371/journal.pone.0262895.

POSTNIKOV E B, ESMEDLJAEVA D A, LAVROVA A I. A CatBoost machine learning for prognosis of pathogen's drug resistance in pulmonary tuberculosis[C]//2020 IEEE 2nd Global Conference on Life Sciences and Technologies(LifeTech). IEEE, 2020: 86-87. DOI: 10.1109/LifeTech48969.2020.1570619054.

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