基于临床特征和放射学特征的Nomogram用于临床分期IA非小细胞肺癌癌症患者术前空气间隙扩散预测:一项多中心研究。

IF 1.4 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Diagnostic and interventional radiology Pub Date : 2023-11-07 Epub Date: 2023-09-19 DOI:10.4274/dir.2023.232404
Yun Wang, Deng Lyu, Di Zhang, Lei Hu, Junhong Wu, Wenting Tu, Yi Xiao, Li Fan, Shiyuan Liu
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引用次数: 0

摘要

目的:探讨临床分期IA型癌症(NSCLC)患者的临床特征和放射学特征对预测其气隙扩散(STAS)的价值。,训练队列(n=236)和内部验证队列(n=100)(比例为7:3)。此外,还收集了来自另外两家医院的69名患者作为外部验证队列。记录了8个临床患者特征,并对20个肿瘤放射学特征进行了定量测量和定性分析。在训练队列中,使用单变量和多变量分析比较临床特征和放射学特征的差异。创建了列线图,并在验证队列中评估了模型的预测功效。受试者工作特征曲线和曲线下面积(AUC)值用于评估模型的判别能力。此外,使用Hosmer-Lemeshow检验和校准曲线来评估模型的拟合优度,并使用决策曲线来分析模型的临床应用价值。结果:最佳预测因素包括性别、癌胚抗原(CEA)、合并肿瘤比(CTR)、密度类型和远端带状征。其中,肿瘤密度类型[比值比(OR):6.738]和远端带状体征(OR:5.141)是预测STAS状态的独立危险因素。此外,构建了三种不同的STAS预测模型,即临床模型、放射学模型和组合模型。临床模型包括性别和CEA,放射学模型包括CTR、密度型和远端带状征,联合模型包括上述两个模型。DeLong测试结果显示,在所有三个队列中,联合模型均优于临床模型,在外部验证队列中优于放射学模型;队列AUC值分别为0.874、0.822和0.810。结果还表明,联合模型在模型中具有最高的诊断功效。Hosmer-Lemeshow检验表明,组合模型在所有三个队列中都显示出良好的拟合性,校准曲线表明,组合模式的预测概率值与实际STAS状态非常一致。最后,决策曲线表明,联合模型比临床和放射学模型具有更好的临床应用价值。结论:本研究基于临床特征和放射学特征创建的列线图对临床分期IA NSCLC患者的STAS状态具有较高的诊断效率,并可能支持在手术前制定个性化的治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nomogram based on clinical characteristics and radiological features for the preoperative prediction of spread through air spaces in patients with clinical stage IA non-small cell lung cancer: a multicenter study.

Purpose: To investigate the value of clinical characteristics and radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC).

Methods: A total of 336 patients with NSCLC from our hospital were randomly divided into two groups, i.e., the training cohort (n = 236) and the internal validation cohort (n = 100) (7:3 ratio). Furthermore, 69 patients from two other hospitals were collected as the external validation cohort. Eight clinical patient characteristics were recorded, and 20 tumor radiological features were quantitatively measured and qualitatively analyzed. In the training cohort, the differences in clinical characteristics and radiological features were compared using univariate and multivariate analysis. A nomogram was created, and the predictive efficacy of the model was evaluated in the validation cohorts. The receiver operating characteristic curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. In addition, the Hosmer-Lemeshow test and calibration curve were used to evaluate the goodness-of-fit of the model, and the decision curve was used to analyze the model's clinical application value.

Results: The best predictors included gender, the carcinoembryonic antigen (CEA), consolidation-to-tumor ratio (CTR), density type, and distal ribbon sign. Among these, the tumor density type [odds ratio (OR): 6.738] and distal ribbon sign (OR: 5.141) were independent risk factors for predicting the STAS status. Moreover, three different STAS prediction models were constructed, i.e., a clinical, radiological, and combined model. The clinical model comprised gender and the CEA, the radiological model included the CTR, density type, and distal ribbon sign, and the combined model comprised the above two models. A DeLong test results revealed that the combined model was superior to the clinical model in all three cohorts and superior to the radiological model in the external validation cohort; the cohort AUC values were 0.874, 0.822, and 0.810, respectively. The results also showed that the combined model had the highest diagnostic efficacy among the models. The Hosmer-Lemeshow test showed that the combined model showed a good fit in all three cohorts, and the calibration curve showed that the predicted probability value of the combined model was in good agreement with the actual STAS status. Finally, the decision curve showed that the combined model had a better clinical application value than the clinical and radiological models.

Conclusion: The nomogram created in this study, based on clinical characteristics and radiological features, has a high diagnostic efficiency for predicting the STAS status in patients with clinical stage IA NSCLC and may support the creation of personalized treatment strategies before surgery.

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来源期刊
Diagnostic and interventional radiology
Diagnostic and interventional radiology Medicine-Radiology, Nuclear Medicine and Imaging
自引率
4.80%
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期刊介绍: Diagnostic and Interventional Radiology (Diagn Interv Radiol) is the open access, online-only official publication of Turkish Society of Radiology. It is published bimonthly and the journal’s publication language is English. The journal is a medium for original articles, reviews, pictorial essays, technical notes related to all fields of diagnostic and interventional radiology.
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