Clinical value of DECT quantitative parameters in predicting lymph node metastasis of gastric cancer

LAN Yanfen; LIN Yuying; MA Mingping; ZHENG Yunyan; LI Tian

doi:10.19732/j.cnki.2096-6210.2023.04.008

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Clinical value of DECT quantitative parameters in predicting lymph node metastasis of gastric cancer

更新时间：2025-12-15

- Clinical value of DECT quantitative parameters in predicting lymph node metastasis of gastric cancer
- Vol. 32, Issue 4, Pages: 360-366(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2023.04.008
  CLC：
- Published Online：13 September 2023，
  
  Published：13 September 2023
- 稿件说明：
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蓝燕芬,林宇英,马明平,等. DECT定量参数预测胃癌淋巴结转移的临床价值探析[J]. 肿瘤影像学, 2023, 32(4): 360-366 https://doi.

org/10.19732/j.cnki.2096-6210.2023.04.008
蓝燕芬,林宇英,马明平,等. DECT定量参数预测胃癌淋巴结转移的临床价值探析[J]. 肿瘤影像学, 2023, 32(4): 360-366 https://doi. DOI： 10.19732/j.cnki.2096-6210.2023.04.008.

org/10.19732/j.cnki.2096-6210.2023.04.008 DOI：

摘要

目的：

探讨胃癌原发灶双能量计算机体层成像（dual-energy computed tomography，DECT）定量参数对术前淋巴结转移（lymph node metastasis，LNM）的预测价值。

方法：

回顾并分析2020年7月—2022年1月福建医科大学省立临床医学院福建省立医院203例经术后病理学检查确诊为胃腺癌患者的临床病理及影像学资料，根据淋巴结术后病理学检查结果，将其分为淋巴结转移组［LNM（+），130例］与非转移组［LNM（-），73例］。由2名医师采用双盲方式测量并计算每例患者病灶动脉期、静脉期及延迟期标准化碘浓度（normalized iodine concentration in arterial/venous/delay phase，nICA/nICV/nICD）及相应能谱曲线（40~110 keV）斜率k值，进行单因素分析。将P＜0.05的指标纳入多因素logistic回归分析中构建预测模型。绘制受试者工作特征（receiver operating characteristic，ROC）曲线评价预测模型的诊断效能，并使用DeLong检验比较各曲线间差异。

结果：

原发灶最大径、T分期及Lauren分型在两组间差异均有统计学意义（

＜0.05）；DECT定量参数中，LNM（+）组的nIC

、nIC

、k

及k

均大于LNM（-）组（

＜0.05），而nIC

在两组间差异无统计学意义；多因素logistic回归分析示，T分期、Lauren分型、nIC

和k

是胃癌LNM的独立危险因素（

＜0.05）；纳入T分期、Lauren分型和nICV的预测模型1和纳入T分期、Lauren分型和k

的预测模型2术前诊断LNM对应的曲线下面积（area under curve，AUC）分别为0.839和0.858，两者差异无统计学意义（

＞0.05）。

结论：

联合DECT定量参数的预测模型对胃癌术前LNM状态具有较高的诊断效能，其中，静脉期DECT定量参数较其余期相更具诊断价值。

Abstract

Objective:

To investigate the predictive value of quantitative parameters of dual-energy computed tomography (DECT) scan of gastric cancer primary on preoperative lymph node metastasis (LNM).

Methods:

A retrospective analysis of the clinicopathological and imaging data of 203 patients with gastric adenocarcinoma diagnosed by postoperative pathology in Fujian Provincial Hospital

Fujian Provincial Clinical College

Fujian Medical University

from July 2020 to January 2022 was divided into LNM (+) group (130 cases) and LNM (-) group (73 cases) according to the pathological results. Two physicians measured and calculated the normalized iodine concentration in arterial

venous and delayed phase (nIC

/nIC

)

and the corresponding energy spectrum curve (40-110 keV) slope k values of each patient’s lesion in a double-blind manner for univariate analysis. The indicators of P＜0.05 were incorporated into the multivariate logistic regression analysis to build a predictive model. The receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic performance of the prediction model

and the differences between the curves were compared using DeLong’s test.

Results:

The maximum diameter of primary focus

T stage and Lauren’s classification were statistically significant between the two groups (

＜0.05); the nIC

nIC

and k

of the LNM (+) group were larger than those in the LNM (-) group (

＜0.05)

while the nIC

was not statistically significant between the two groups. Multiple logistic regression analysis showed that T stage

Lauren’s classification

nIC

and k

were independent risk factors forLNM in gastric cancer (

＜0.05). The area under curve (AUC) corresponding to the preoperative diagnosis of LNM were 0.839 and 0.858 respectively in the prediction model 1 with T stage

Lauren

’s classification and nIC

and model 2 with T stage

Lauren’s classification and k

and there was no significant difference between the two (

＞0.05).

Conclusion:

The prediction model combined with the quantitative parameters of DECT has high diagnostic power for the preoperative LNM status of gastric cancer

and the quantitative parameters of the venous phase have more diagnostic value than the residual phase.

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Related Institution

Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center

Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University

Department of Gastric and Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center

Shanxi Medical University Graduate School

Department of Imaging, Shanxi Province Cancer Hospital /Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences /Cancer Hospital Affiliated to Shanxi Medical University

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