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

doi:10.3969/j.issn.1006-5725.2024.06.019

Abstract

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.

Key words: gastric cancer, lymph node metastasis, routine laboratory indicators, machine learning

CLC Number:

R319

Jianliang YAN,Zeyu XIE,Rongrong JING,Ming. CUI. Research on establishing gastric cancer lymph node metastasis prediction model based on machine learning and routine laboratory indicators[J]. The Journal of Practical Medicine, 2024, 40(6): 844-849.

Figures/Tables 6

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References 26

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临床指标	变量	训练集（n = 555）					测试集（n = 186）			χ²值	P值
临床指标	变量	non-LNM （n = 197）	LNM （n = 358）	χ²值	P值	non-LNM （n = 68）	LNM （n = 118）	χ²值	P值	χ²值	P值
性别	男	146	242	2.263	0.133	38	87	5.451	0.020	0.360	0.548
性别	女	51	116	2.263	0.133	30	31	5.451	0.020	0.360	0.548
年龄	< 均值（66）	98	159	1.247	0.264	29	60	0.857	0.355	0.078	0.779
年龄	≥ 均值（66）	99	199	1.247	0.264	39	58	0.857	0.355	0.078	0.779
分化程度	G1	19	2	44.560	< 0.001	17	1	44.437	< 0.001	22.399	< 0.001
	G2	102	214			24	49
	G3	56	132			18	66
	缺失	20	10			9	2
NLR	< 中位数（2.118）	110	167	3.933	0.047	39	52	2.539	0.111	0.022	0.882
NLR	≥ 中位数（2.118）	87	191	3.933	0.047	29	66	2.539	0.111	0.022	0.882
pTNM T阶段	T1	107	23	207.072	< 0.001	44	3	95.694	< 0.001	3.743	0.442
	T2	41	37			8	12
	T3	45	272			15	89
	T4	1	22			0	13
	缺失	3	4			1	1
pTNM N阶段	N0	197	0	555.000	< 0.001	68	0	186.000	< 0.001	0.452	0.929
	N1	0	105			0	38
	N2	0	98			0	30
	N3	0	155			0	50

	训练集（n = 555， 197 LNM， 358 non-LNM）							测试集（n = 186， 68 LNM， 118 non-LNM）
模型	AUC	F1	ACC	敏感度	特异度	PPV	NPV	AUC	F1	ACC	敏感度	特异度	PPV	NPV
AdaBoost	0.999	0.982	0.977	0.974	0.978	0.960	0.986	0.968	0.926	0.903	0.887	0.911	0.833	0.942
RF	0.990	0.950	0.935	0.918	0.944	0.899	0.955	0.960	0.896	0.866	0.815	0.893	0.803	0.900
KNN	0.879	0.844	0.800	0.720	0.845	0.724	0.843	0.813	0.823	0.763	0.690	0.797	0.606	0.850
SVM	0.763	0.767	0.659	0.550	0.684	0.276	0.874	0.750	0.771	0.661	0.548	0.684	0.258	0.883
MLP	0.749	0.778	0.659	0.586	0.668	0.171	0.933	0.742	0.800	0.683	0.818	0.674	0.136	0.983

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Research on establishing gastric cancer lymph node metastasis prediction model based on machine learning and routine laboratory indicators

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