[1]朱凯博,邓靓娜,王海升,等.基于能谱CT参数及临床特征构建非小细胞肺癌PD-L1表达的列线图预测模型[J].中国医学物理学杂志,2025,42(4):443-449.[doi:10.3969/j.issn.1005-202X.2025.04.004]
　ZHU Kaibo,DENG Liangna,WANG Haisheng,et al.Construction of a nomogram prediction model for PD-L1 expression in non-small cell lungcancer using spectral CT parameters and clinical features[J].Chinese Journal of Medical Physics,2025,42(4):443-449.[doi:10.3969/j.issn.1005-202X.2025.04.004]
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基于能谱CT参数及临床特征构建非小细胞肺癌PD-L1表达的列线图预测模型()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第4期
页码:
443-449
栏目:
医学影像物理
出版日期:
2025-04-20

文章信息/Info

Title:
Construction of a nomogram prediction model for PD-L1 expression in non-small cell lungcancer using spectral CT parameters and clinical features
文章编号:
1005-202X（2025）04-0443-07
作者:
朱凯博; 邓靓娜; 王海升; 刘建强; 雒攀; 周俊林
兰州大学第二医院放射科 / 兰州大学第二临床医学院 / 甘肃省医学影像重点实验室 / 医学影像人工智能甘肃省国际科技合作基地，
Author(s):
ZHU Kaibo;  DENG Liangna;  WANG Haisheng;  LIU Jianqiang;  LUO Pan;  ZHOU Junlin
Department of Radiology, Lanzhou University Second Hospital/the Second Clinical Medical School, Lanzhou University/KeyLaboratory of Medical Imaging of Gansu Province/Gansu International Scientific and Technological Cooperation Base of MedicalImaging Artificial Intelligence, Lanzhou 730030, China
关键词:
非小细胞肺癌; 列线图; 能谱CT; 程序性死亡配体1
Keywords:
non-small cell lung cancer;  nomogram;  spectral CT;  programmed cell death ligand 1
分类号:
R318
DOI:
10.3969/j.issn.1005-202X.2025.04.004
文献标志码:
A
摘要:
目的：基于临床信息、常规CT征象及能谱CT参数构建列线图模型术前预测非小细胞肺癌（NSCLC）程序性死亡配体1（PD-L1）的表达水平。方法：回顾性分析经病理证实为NSCLC的52例患者，术前均行能谱CT检查。依据PD-L1表达水平分为阳性组和阴性组，收集两组患者的临床信息、常规CT征象及能谱CT定量参数。临床信息包括性别、年龄、Ki-67、肿瘤标志物。常规CT征象包括肿瘤密度、边界、钙化征、毛刺征、分叶征、胸膜凹陷征、空洞征等。测量两组病灶在动脉期和静脉期的能谱参数，包括有效原子序数（Eff-Z）、碘浓度（IC）、水浓度（WC），并计算归一化碘浓度（NIC）。比较两组间的差异，采用多因素Logistic回归来筛选独立预测因子并构建预测模型，采用受试者工作特征曲线、校准曲线和决策曲线评估列线图模型的预测效能及准确性。结果：临床信息中，两组在性别方面的差异有统计学意义（P<0.05）。在动脉期和静脉期，PD-L1阳性组的能谱定量参数IC、NIC及Eff-Z均大于PD-L1阴性组，差异有统计学意义（P<0.05）。多因素Logistic回归分析显示性别（P=0.024）、静脉期Eff-Z（P=0.002）及静脉期IC（P=0.003）为PD-L1表达的独立预测因素。基于以上独立预测因子构建的列线图预测模型曲线下面积为0.80，敏感度为88.00%，特异度为59.00%。校准曲线表明模型预测值和实际值具有较高的一致性。决策曲线显示高风险阈值界于0.10~0.83时，模型可以获得最大净收益。结论：基于能谱CT定量参数和临床信息的列线图模型在术前预测NSCLC的PD-L1表达水平中有一定的价值。
Abstract:
Objective To investigate the preoperative prediction of the expression level of programmed cell death ligand 1(PD-L1) in non-small cell lung cancer (NSCLC) by a nomogram model constructed with clinical data, conventional CTsigns and spectral CT parameters. Methods A retrospective analysis was conducted on 52 patients with pathologicallyconfirmed NSCLC and undergoing preoperative spectral CT examination. The patients were categorized into positive andnegative groups according to PD-L1 expression level, and their clinical data, conventional CT signs and spectral CTparameters were collected. Specifically, clinical data included gender, age, Ki-67 and tumor markers; conventional CT signsincluded tumor density, margins, calcification, spiculation, lobulation, pleural indentation and cavitation; and spectral CTparameters measured in the arterial and venous phases included effective atomic number (Eff-Z), iodine concentration (IC),water concentration (WC) and normalized iodine concentration (NIC). Intergroup differences were analyzed, andmultivariate Logistic regression was used to identify independent predictors and establish the prediction model which wasevaluated for prediction performance and accuracy using receiver operating characteristic (ROC) curves, calibration curveand decision curve analyses. Results For clinical data, only the difference in gender between two groups had statistical significance (P<0.05). The spectral CT parameters (IC, NIC and Eff-Z) in the arterial and venous phases of PD-L1 positivegroup were all greater than those of PD-L1 negative group, with statistically significant differences (P<0.05). MultivariateLogistic regression analysis identified gender (P=0.024), venous-phase Eff-Z (P=0.002), and venous-phase IC (P=0.003)as independent predictive factors for PD-L1 expression. The nomogram prediction model constructed with theseindependent predictors had an area under curve of 0.80, a sensitivity of 88.00%, and a specificity of 59.00%. Thecalibration curve showed that the predicted values had a high consistency with the actual values. The decision curverevealed that when the high-risk threshold was between 0.10 and 0.83, the model could achieve the maximum net benefit.Conclusion The nomogram model constructed with spectral CT parameters and clinical data has certain value in predictingthe expression level of PD-L1 in NSCLC.

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备注/Memo

备注/Memo:
【收稿日期】2024-12-13【基金项目】国家自然科学基金（82371914）【作者简介】朱凯博，硕士研究生，研究方向：胸部影像学，E-mail: 18835359804@163.com【通信作者】周俊林，博士，主任医师，教授，研究方向：神经影像，E-mail: ery_zhoujl@lzu.edu.cn
更新日期/Last Update: 2025-04-30