天津医药

天津医药 ›› 2026, Vol. 54 ›› Issue (3): 303-308.doi: 10.11958/20252816

• 应用研究 • 上一篇    下一篇

增强CT淋巴结边缘特征联合IPI对难治性弥漫大B细胞巴瘤的预测价值

刘晓华1(), 韩婷婷2, 高玉杰2,△()   

  1. 1 克什克腾旗人民医院放射科（邮编025350）
    2 赤峰市医院核医学科
  • 收稿日期:2025-09-01 修回日期:2025-11-11 出版日期:2026-03-15 发布日期:2026-03-17
  • 通讯作者: E-mail：gaoyujiegyj@163.com
  • 作者简介:刘晓华（1983），女，副主任医师，主要从事血液系统疾病的影像诊断方面研究。E-mail：372475323@qq.com
  • 基金资助:
    赤峰市自然科学基金资助项目(SZR24099)

The predictive value of refractory diffuse large B-cell lymphoma based on enhanced CT lymph node marginal features combined with IPI score

LIU Xiaohua1(), HAN Tingting2, GAO Yujie2,△()   

  1. 1 Department of Radiology, Keshiketeng County People's Hospital, Chifeng 025350, China
    2 Department of Nuclear Medicine, Chifeng Municipal Hospital
  • Received:2025-09-01 Revised:2025-11-11 Published:2026-03-15 Online:2026-03-17
  • Contact: E-mail：gaoyujiegyj@163.com

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摘要：

目的 探讨基于治疗前增强CT的淋巴结边缘特征联合国际预后指数（IPI）对预测难治性弥漫大B细胞淋巴瘤（DLBCL）的价值，并构建列线图。方法 选取98例初治DLBCL患者并收集其临床、病理及基线增强CT资料。由2名高年资放射科医师独立进行CT图像中淋巴结边缘特征评分，以评分最高者为该患者最终的淋巴结边缘特征评分。患者均接受以R-CHOP方案为基础的化疗（6~8周期）。依据Lugano 2014疗效评价标准进行疗效判定，将患者分为非难治组（69例）和难治组（29例）。采用Logistic回归分析发展为难治性DLBCL的影响因素，根据结果绘制列线图预测模型，采用受试者工作特征（ROC）曲线评估模型区分能力，Hosmer-Lemeshow拟合优度检验评估其校准度。结果 与非难治组比较，难治组患者IPI、淋巴结边缘特征评分、血清降钙素原水平均高于非难治组（P<0.05）。单因素二元Logistic回归分析结果显示，淋巴结边缘特征评分、IPI是难治性DLBCL的影响因素（P<0.01）。多因素二元Logistic回归分析结果显示，淋巴结边缘特征评分、IPI升高是难治性DLBCL的危险因素。根据Logistic回归分析结果建立发展为难治性DLBCL的列线图预测模型，ROC曲线分析结果显示，列线图模型预测难治性DLBCL的曲线下面积为0.840（95%CI：0.759~0.920），Hosmer-Lemeshow χ2=5.794，P=0.670，拟合度较好。结论 增强CT淋巴结边缘特征评分联合IPI能有效预测难治性DLBCL的患者，构建的列线图预测模型为临床早期识别高危患者、制定个体化治疗策略提供了有潜力的预测工具。

关键词: 淋巴瘤, 大B细胞, 弥漫性, 列线图, 增强CT, 淋巴结边缘特征, 国际预后指数

Abstract:

Objective To explore the value of lymph node margin features based on pre-treatment enhanced CT combined with the international prognostic index (IPI) in predicting refractory diffuse large B-cell lymphoma (DLBCL) and to construct a nomogram prediction model. Methods A total of 98 patients with newly diagnosed DLBCL were selected. Clinical data, pathological findings and baseline enhanced CT of the patients were collected. The lymph node margin feature scores in CT images were independently evaluated by two senior radiologists, and the highest score was taken as the final lymph node margin feature score of the patient. All patients received chemotherapy based on the R-CHOP regimen (6-8 cycles). Patients were classified into the non-refractory group (69 cases) and the refractory group (29 cases) according to the Lugano 2014 response evaluation criteria. Logistic regression analysis was used to identify the factors influencing the development of refractory DLBCL. A nomogram was constructed based on the results of the multivariate Logistic regression model. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination ability of the nomogram, and Hosmer-Lemeshow goodness-of-fit test was used to assess its calibration. Results Compared with the non-refractory group, patients of the refractory group had higher IPI scores, lymph node margin feature scores and serum PCT levels (P<0.05). Univariate binary Logistic regression analysis showed that lymph node margin feature scores and IPI scores were factors influencing the development of refractory DLBCL (P<0.01). Multivariate binary Logistic regression analysis showed that increased lymph node margin feature scores and IPI scores were risk factors for refractory DLBCL. A nomogram prediction model for the development of refractory DLBCL was established based on the results of Logistic regression analysis. The ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting refractory DLBCL was 0.840 (95% CI: 0.759 - 0.920), and Hosmer-Lemeshow χ2 = 5.794, P = 0.670, indicating good fit. Conclusion The enhanced CT lymph node margin feature scores combined with IPI scores can effectively predict patients with refractory DLBCL. The constructed nomogram provides a potential predictive tool for the early identification of high-risk patients and individualized treatment strategies in clinical practice.

Key words: lymphoma, large B-cell, diffuse, nomograms, contrast-enhanced CT, lymph node margin features, international prognostic index

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