Establishment and validation of a predictive nomogram model for advanced gastric cancer with lymphovascular invasion

doi:10.11958/20230513

Abstract

Abstract:

Objective To explore the preoperative predictors of lymphovascular invasion (LVI) in patients with advanced gastric cancer, and establish the corresponding nomogram prediction model and conduct internal validation. Methods A total of 246 cases of advanced gastric cancer who underwent surgical resection in the Department of Gastrointestinal Surgery of Hengshui People's Hospital from January 2018 to December 2021 were selected. Patients were divided into the LVI positive group and the LVI negative group according to postoperative pathological diagnosis. The age, gender, tumor differentiation, tumor size, tumor site, Borrmann classification, Lauren's classification, cT stage, cN stage and systemic immune-inflammation index (SII) of patients were collected and compared between the two groups. The predictors that were statistically different between the two groups were subjected to multivariate Logistic regression and further developed into a visual prediction model. Bootstrap method was applied for internal validation of the prediction efficiency of the model. Results The differences of tumor size, Borrmann classification, tumor differentiation, Lauren classification, cT staging, cN staging and SII were statistically significant between the two groups ( P<0.05). Multivariate Logistic regression analysis showed that tumor size (OR=2.184, 95%CI:1.224-3.898), Borrmann classification (OR=2.517, 95%CI: 1.294-4.896), cT staging (OR=1.860, 95%CI: 1.045-3.308), cN staging (OR=1.816, 95%CI: 1.004-3.285) and SII (OR=1.001, 95%CI: 1.000-1.002) were independent predictors of LVI in advanced gastric cancer. A preoperative nomogram prediction model for advanced gastric cancer LVI was developed based on results of multivariate analysis. By internal validation, the area under curve (AUC) value of the subject operating characteristic (ROC) curve of the nomogram was 0.735, which was higher than that of tumor size (0.599), Borrmann staging (0.564), cT staging (0.604), cN staging (0.582) and SII (0.615), respectively. The calibration curve showed that the probability of predicted LVI by the nomogram was in a good agreement with the probability of actual LVI occurrence. The Hosmer-Lemeshow test showed good model fit (χ²=4.387, P=0.821). Conclusion The established nomogram prediction model can help to predict the probability of LVI in advanced gastric cancer preoperatively, which can provide a guideline for clinical individualized treatment.

Key words: gastric neoplasms, risk factors, nomograms, advanced, lymphovascular invasion, predictive model

CLC Number:

R735.2

Figures/Tables 6

References 20

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临床特征	LVI阳性（n=95）	LVI阴性（n=151）	χ²或Z
年龄
<60岁	42（44.2）	67（44.4）	0.001
≥60岁	53（55.8）	84（55.6）	0.001
性别
男	62（65.3）	96（63.6）	0.072
女	33（34.7）	55（36.4）	0.072
肿瘤大小
<5 cm	34（35.8）	84（55.6）	9.196^＊＊
≥5 cm	61（64.2）	67（44.4）	9.196^＊＊
肿瘤位置
上部	21（22.1）	34（22.5）	0.524
中部	31（32.6）	44（29.1）
下部	34（35.8）	60（39.7）
≥2/3	9（9.5）	13（8.6）
Borrmann分型
Ⅰ—Ⅱ	20（21.1）	51（33.8）	4.597^＊
Ⅲ—Ⅳ	75（78.9）	100（66.2）	4.597^＊
肿瘤分化
高-中分化	27（28.4）	64（42.4）	4.878^＊
低-未分化	68（71.6）	87（57.6）	4.878^＊
Lauren分型
肠型	18（18.9）	51（33.8）
弥漫型	52（54.7）	58（38.4）	8.095^＊
混合型	25（26.3）	42（27.8）
cT分期
T2—3	33（34.7）	84（55.6）	10.205^＊＊
T4	62（65.3）	67（44.4）	10.205^＊＊
cN分期
N0	31（32.6）	74（49.0）	6.391^＊
N+	64（67.4）	77（51.0）	6.391^＊
SII	878.00 （649.50，1 351.00）	682.00 （526.00，1 054.00）	3.047^＊＊

临床特征	LVI阳性（n=95）	LVI阴性（n=151）	χ²或Z
年龄
<60岁	42（44.2）	67（44.4）	0.001
≥60岁	53（55.8）	84（55.6）	0.001
性别
男	62（65.3）	96（63.6）	0.072
女	33（34.7）	55（36.4）	0.072
肿瘤大小
<5 cm	34（35.8）	84（55.6）	9.196^＊＊
≥5 cm	61（64.2）	67（44.4）	9.196^＊＊
肿瘤位置
上部	21（22.1）	34（22.5）	0.524
中部	31（32.6）	44（29.1）
下部	34（35.8）	60（39.7）
≥2/3	9（9.5）	13（8.6）
Borrmann分型
Ⅰ—Ⅱ	20（21.1）	51（33.8）	4.597^＊
Ⅲ—Ⅳ	75（78.9）	100（66.2）	4.597^＊
肿瘤分化
高-中分化	27（28.4）	64（42.4）	4.878^＊
低-未分化	68（71.6）	87（57.6）	4.878^＊
Lauren分型
肠型	18（18.9）	51（33.8）
弥漫型	52（54.7）	58（38.4）	8.095^＊
混合型	25（26.3）	42（27.8）
cT分期
T2—3	33（34.7）	84（55.6）	10.205^＊＊
T4	62（65.3）	67（44.4）	10.205^＊＊
cN分期
N0	31（32.6）	74（49.0）	6.391^＊
N+	64（67.4）	77（51.0）	6.391^＊
SII	878.00 （649.50，1 351.00）	682.00 （526.00，1 054.00）	3.047^＊＊

变量	变量类型	变量赋值
LVI	因变量	阴性=0，阳性=1
肿瘤大小	自变量	<5 cm=1，≥5 cm=2
Borrmann分型	自变量	Ⅰ—Ⅱ型=1，Ⅲ—Ⅳ型=2
肿瘤分化	自变量	高-中分化=1，低-未分化=2
Lauren分型	自变量	肠型=1，弥漫型=2，混合型=3
cT分期	自变量	cT2—3期=1，cT4期=2
cN分期	自变量	cN0期=1，cN+期=2
SII	自变量	连续变量

变量	变量类型	变量赋值
LVI	因变量	阴性=0，阳性=1
肿瘤大小	自变量	<5 cm=1，≥5 cm=2
Borrmann分型	自变量	Ⅰ—Ⅱ型=1，Ⅲ—Ⅳ型=2
肿瘤分化	自变量	高-中分化=1，低-未分化=2
Lauren分型	自变量	肠型=1，弥漫型=2，混合型=3
cT分期	自变量	cT2—3期=1，cT4期=2
cN分期	自变量	cN0期=1，cN+期=2
SII	自变量	连续变量

变量	β	SE	Waldχ²	P	OR（95%CI）
肿瘤大小	0.781	0.296	6.984	0.008	2.184（1.224~3.898）
Borrmann 分型	0.923	0.339	7.398	0.007	2.517（1.294~4.896）
Lauren 分型			5.129	0.077
弥漫型	-0.659	0.41	2.582	0.108	0.518（0.232~1.156）
混合型	0.159	0.346	0.213	0.645	1.173（0.596~2.309）
cT分期	0.620	0.294	4.456	0.035	1.860（1.045~3.308）
cN分期	0.597	0.302	3.896	0.048	1.816（1.004~3.285）
SII	0.001	<0.001	10.235	0.001	1.001（1.000~1.002）
常数项	-6.039	1.119	29.106	<0.001	0.002