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中华消化病与影像杂志(电子版)

中华消化病与影像杂志(电子版) ›› 2024, Vol. 14 ›› Issue (02) : 107 -113. doi: 10.3877/cma.j.issn.2095-2015.2024.02.002

论著

增强CT的列线图在鉴别EB病毒相关的胃淋巴上皮瘤样癌与胃腺癌中的应用
袁梦晨1, 刘译阳1, 赵帅1, 陈林1, 高宇1, 肖晓燕1, 尤亚茹1, 梁何俊2, 高剑波1,†()   
  1. 1. 450052　郑州大学第一附属医院放射科
    2. 100053　北京，首都医科大学宣武医院消化科
  • 收稿日期:2023-08-25 出版日期:2024-04-01
  • 通信作者: 高剑波
  • 基金资助:
    国家自然科学基金(81971615)

Application of enhanced CT-based nomogram in identifying Epstein-Barr virus-associated lymphoepithelioma-like gastric carcinoma from gastric adenocarcinoma

Mengchen Yuan1, Yiyang Liu1, Shuai Zhao1, Lin Chen1, Yu Gao1, Xiaoyan Xiao1, Yaru You1, Hejun Liang2, Jianbo Gao1,†()   

  1. 1. Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
    2. Department of Gastroenterology, Xuanwu Hospital, Capital Medical Universit, Beijing100053, China
  • Received:2023-08-25 Published:2024-04-01
  • Corresponding author: Jianbo Gao
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引用本文：

袁梦晨, 刘译阳, 赵帅, 陈林, 高宇, 肖晓燕, 尤亚茹, 梁何俊, 高剑波. 增强CT的列线图在鉴别EB病毒相关的胃淋巴上皮瘤样癌与胃腺癌中的应用[J]. 中华消化病与影像杂志(电子版), 2024, 14(02): 107-113.

Mengchen Yuan, Yiyang Liu, Shuai Zhao, Lin Chen, Yu Gao, Xiaoyan Xiao, Yaru You, Hejun Liang, Jianbo Gao. Application of enhanced CT-based nomogram in identifying Epstein-Barr virus-associated lymphoepithelioma-like gastric carcinoma from gastric adenocarcinoma[J]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2024, 14(02): 107-113.

目的

开发和评估一个基于临床及增强CT的列线图，用来鉴别EB病毒（EBV）相关的胃淋巴上皮瘤样癌和胃腺癌。

方法

回顾性分析25例经病理组织学证实的EBV相关的胃淋巴上皮瘤样癌患者的临床及影像学资料，另纳入50例胃腺癌患者作为对照组。采用单因素分析和多因素Logistic回归筛选鉴别两种类型肿瘤的预测因子。构建一个个性化的列线图，并通过绘制ROC曲线、校准曲线以及决策曲线分析评价列线图的区分度、校准度及临床实用价值。采用Bootstrap法进行内部验证。

结果

肿瘤的位置（中部vs上部，OR=1.866，95% CI 0.457～7.626；下部vs上部，OR=14.454，95% CI 2.619～79.778）、CT强化峰值（OR=6.965，95% CI 1.796～27.016）及CT淋巴结状态（OR=4.242，95% CI 1.270～14.164）是淋巴上皮瘤样癌患者的独立鉴别因素。列线图的AUC值为0.815（95% CI 0.718～0.912）。列线图显示出良好的校准度和临床效益。通过200次自举重采样，进行自举验证（内部验证），最终显示该列线图具有良好的区分准确性，模型内部验证的AUC值为0.823（95% CI 0.817～0.830）。

结论

本研究建立的列线图在鉴别EBV相关的胃淋巴上皮瘤样癌与胃腺癌方面具有很大的应用潜力，可为临床的决策提供依据。

关键词: 胃腺癌, 淋巴上皮瘤样癌, 体层摄影术，X线计算机, 列线图
Objective

To develop and evaluate a clinical and enhanced CT based nomogram for the differentiation of lymphoepithelioma-like gastric carcinoma associated with Epstein-Barr virus (EBV) from gastric adenocarcinoma.

Methods

The clinical and imaging data of 25 patients with pathohistology- confirmed lymphoepithelioma-like gastric carcinoma were retrospectively analyzed, and 50 patients with gastric adenocarcinoma were included as the control group. Univariate analysis and multivariate Logistic regression were used to screen the predictors for identifying the two types of tumors. A personalized nomogram was constructed, and the distinction degree, calibration degree, and clinical utility value of the nomogram were evaluated by drawing the ROC curve, calibration curve, and decision curve analysis (DCA). Internal validation was performed using the Bootstrap method.

Results

The location of the tumor (middle vs. upper, OR=1.866, 95% CI: 0.457-7.626; lower vs. upper, OR=14.454, 95% CI: 2.619-79.778), CT enhancement peak (OR=6.965, 95% CI: 1.796-27.016) and the lymph node status defined by CT (OR=4.242, 95% CI: 1.270-14.164) were independent differential factors in patients with lymphoepithelioma- like carcinoma. The AUC value of the nomogram was 0.815 (95% CI: 0.718-0.912). The nomogram showed a good calibration and a clinical benefit. For bootstrapping validation (internal validation) with 200 bootstrap-resampling, the nomogram showed good discriminatory accuracy and the AUC value of internal model validation was 0.823 (95% CI: 0.817-0.830).

Conclusion

The nomogram established in this study has great potential in identifying EBV related lymphoepithelioma-like gastric carcinoma from gastric adenocarcinoma and warrants clinical decision making.

Key words: Gastric adenocarcinoma, Lymphoepithelioma-like carcinoma, Tomography, X-ray computed, Nomogram
图1　61岁的男性EB病毒相关的胃淋巴上皮瘤样癌患者，平扫胃体部可见大弯侧软组织肿块影（1A～1D箭头），其上可见溃疡影，增强扫描呈明显持续性强化。图2 73岁的女性胃腺癌患者，平扫可见胃窦部胃壁不均匀增厚（2A～2D箭头），增强扫描呈中度强化，胃窦腔狭窄，周围可见肿大淋巴结影（2D箭头）。
表1 2组患者临床、免疫组化、实验室、病理及CT影像学指标比较
组别 例数 性别[例(%)] 年龄(岁,±s) pT分期[例(%)] pN分期[例(%)]
男 女 ≤T3 ≥T4a N0 ≥N1
LELGC组 25 20(80) 5(20) 54.7±10.6 20(80) 5(20) 15(60) 10(40)
G-ADC组 50 40(80) 10(20) 60.2±10.8 37(74) 13(26) 13(26) 37(74)
统计量   0.000 -2.066 0.329 8.235
P值   1.000 0.042* 0.566 0.004*
组别 例数 神经侵犯[例(%)] 血管侵犯[例(%)] HER-2[例(%)]
否 是 否 是 + ++ +++ ++++
LELGC组 25 12(48) 13(52) 15(60) 10(40) 12(48) 10(40) 2(8) 1(4)
G-ADC组 50 13(26) 37(74) 12(24) 38(76) 22(44) 18(36) 6(12) 4(8)
统计量   3.630 9.375 0.780
P值   0.057* 0.002* 0.854
组别 例数 Ki-67指数[例(%)] CA125[例(%)] CA199[例(%)] CA724[例(%)]
低(<50%) 高(≥50%) 正常 升高 正常 升高 正常 升高
LELGC组 25 5(20) 20(80) 25(100) 0(0) 24(96) 1(4) 23(92) 2(8)
G-ADC组 50 10(20) 40(80) 48(96) 2(4) 41(82) 9(18) 37(74) 13(26)
统计量   0 / 1.745 3.375
P值   1 0.55 0.186 0.066*
组别 例数 CEA[例(%)] 位置[例(%)] 长径(mm,±s) 厚径(mm,±s)
正常 升高 上部 中部 下部 全胃
LELGC组 25 5(20) 20(80) 8(32) 11(44) 6(24) 0(0) 44.1±19.1 15.9±5.9
G-ADC组 50 10(20) 40(80) 24(48) 7(14) 19(38) 0(0) 53.0±14.4 17.9±5.2
统计量   0 8.230 -2.255 -1.445
P值   1 0.016* 0.027* 0.153
组别 例数 厚长比[M(Q1,Q3)] 瘤周脂肪间隙[例(%)] 溃疡[例(%)] CT平扫(HU,±s) CT动脉(HU,±s)
清晰 模糊 不存在 存在
LELGC组 25 0.36(0.31,0.45) 20(80) 5(20) 11(44) 14(56) 40.4±10.0 80.3±25.3
G-ADC组 50 0.34(0.29,0.40) 30(60) 20(40) 16(32) 34(68) 38.6±8.4 80.5±19.2
统计量   -1.158 3.000 1.042 0.815 -0.034
P值   0.247 0.083* 0.307 0.418 0.973
组别 例数 CT静脉[HU,M(Q1,Q3)] CT动脉-平扫[HU,M(Q1,Q3)] CT静脉-平扫[HU,M(Q1,Q3)] 均匀强化[例(%)] 强化程度[例(%)]
否 是 轻度 中度 重度
LELGC组 25 91.0(75.4,99.9) 36.1(23.9,54.1) 50.8(35.5,62.6) 10(40) 15(60) 1(4) 7(28) 17(68)
G-ADC组 50 80.8(75.0,92.5) 36.5(29.1,49.5) 42.4(31.1,52.9) 25(50) 25(50) 2(4) 16(32) 32(64)
统计量   1.394 -0.348 -0.955 0.670 0.128
P值   0.163 0.728 0.339 0.413 0.938
组别 例数 CT强化峰值[例(%)] CT淋巴结状态[例(%)]
动脉期 静脉期 未增大 增大
LELGC组 25 6(24) 19(76) 14(56) 11(44)
G-ADC组 50 27(54) 23(46) 15(30) 35(70)
统计量   6.088 4.751
P值   0.014* 0.029*
图3 分别为列线图模型、肿瘤位置、CT强化峰值及CT淋巴结状态的ROC曲线，其AUC值分别为0.815、0.653、0.65及0.63。
图4 基于鉴别诊断模型建立的列线图
表2 多因素Logistic回归分析EB病毒相关的胃淋巴上皮瘤样癌的独立预测因素
变量 B SE Wald χ2 P OR 95% CI
肿瘤位置     10.186 0.006      
中部vs上部 0.624 0.718 0.754 0.385 1.866 0.457 7.626
下部vs上部 2.671 0.872 9.391 0.002 14.454 2.619 79.778
CT强化峰值 1.941 0.692 7.877 0.005 6.965 1.796 27.016
CT淋巴结状态 1.445 0.896 5.516 0.019 4.242 1.270 14.164
图5 列线图模型的ROC曲线，ROC曲线下面积（AUC值）为0.815，模型的敏感度为0.660，特异度为0.840。
图6 校准（Calibration）曲线，横坐标是预测概率，纵坐标是实际概率，刻度0～1表示发生的可能性。Apparent为实际概率线，Ideal为理想线，Bias-corrected为纠正偏差线。
图7 决策曲线分析（DCA），基于连续的潜在风险阈值（X轴）和使用该模型对患者进行风险分层的净收益（Y轴）展示该模型的临床实用性。
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