Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study.

IF 2.6 3区 医学 Q2 RESPIRATORY SYSTEM BMC Pulmonary Medicine Pub Date : 2024-10-26 DOI:10.1186/s12890-024-03360-8
Zhengming Wang, Fei Wang, Yan Yang, Weijie Fan, Li Wen, Dong Zhang
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Abstract

Purpose: To develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images.

Methods: This is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility.

Results: The T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram.

Conclusion: A nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.

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开发基于提名图的模型,纳入随访纵向肺部 CT 图像的放射学特征,以区分浸润性腺癌和良性病变:一项回顾性研究。
目的:根据随访纵向CT图像,开发并验证用于区分肺浸润性腺癌和良性病变的放射学模型:这是一项回顾性研究,包括2016年1月至2022年6月期间接受基线(T0)和随访(T1)CT扫描的336名患者(161名浸润性腺癌患者和175名良性病变患者)。患者按 7:3 的比例随机分为训练集和测试集。从T0和T1纵向CT图像上感兴趣的病灶体积中提取放射学特征。T1 和 T0 之间的放射学特征差异被定义为 delta 放射学特征。逻辑回归用于根据临床放射学(CR)、T0、T1 和 delta 放射学特征建立模型并计算特征。最后,根据 CR、T0、T1 和 delta 特征构建了提名图。对模型的校准、辨别和临床实用性进行了评估:结果:T1放射学模型优于其他独立模型。在训练集中,它的曲线下面积(AUC)为 0.858,优于 CR 模型(AUC 0.694)、T0 放射性模型(AUC 0.825)和 delta 放射性模型(AUC 0.734)。在测试集中,它的 AUC 为 0.817,再次优于 CR 模型(AUC 0.578)、T0 放射模型(AUC 0.789)和 delta 放射模型(AUC 0.647)。包含 CR、T0、T1 和 delta 特征的提名图在训练集(AUC:0.906)和测试集(AUC:0.856)中都显示出最佳的预测性能,而且与校准曲线的拟合效果极佳。决策曲线分析进一步验证了提名图的临床实用性:结论:利用从纵向 CT 图像中提取的放射学特征绘制的提名图可以提高肺浸润性腺癌和良性病变之间的鉴别能力。
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来源期刊
BMC Pulmonary Medicine
BMC Pulmonary Medicine RESPIRATORY SYSTEM-
CiteScore
4.40
自引率
3.20%
发文量
423
审稿时长
6-12 weeks
期刊介绍: BMC Pulmonary Medicine is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of pulmonary and associated disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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