基于机器学习的肺循环血流动力学定量评估的计算机断层肺血管造影。

IF 3.2 2区医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS

International journal of cardiology Pub Date : 2025-05-30 DOI:10.1016/j.ijcard.2025.133457

Hongyan Xie , Xin Zhao , Nan Zhang , Jiayi Liu , Guang Yang , Yunshan Cao , Jialin Xu , Lei Xu , Zhonghua Sun , Zhaoying Wen , Senchun Chai , Dongting Liu

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引用次数: 0

摘要

背景：肺动脉高压（pH）是一种恶性肺循环疾病。右心导管（RHC）是定量评价肺血流动力学的金标准程序。由于目前可用的评估方法的局限性，准确和无创的肺血流动力学定量评估是具有挑战性的。方法：选取2 周内行ct肺血管造影（CTPA）和RHC检查的患者。数据集按8:2的比例随机分为训练集和测试集。建立放射学特征模型和二维特征模型，定量评价肺血流动力学。通过计算均方误差、类内相关系数（ICC）和精密度-召回率曲线下面积（AUC-PR）并进行Bland-Altman分析来确定模型的性能。结果：345例患者中，有PH患者271例（平均年龄50 ± 17 岁，男性93例），无PH患者74例（平均年龄55 ± 16 岁，男性26例）。综合5个二维特征和其他30个放射特征的放射特征模型对肺血流动力学的预测结果与RHC结果一致，优于另一个二维特征模型。放射学特征模型在预测肺血流动力学参数方面具有中等到良好的再现性（ICC达到0.87）。此外，基于分类模型可以准确识别pH （AUC-PR =0.99）。结论：本研究提供了一种利用CTPA图像全面定量评估肺血流动力学的无创方法，有可能作为RHC的替代方法，有待进一步验证。

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Machine learning–based hemodynamics quantitative assessment of pulmonary circulation using computed tomographic pulmonary angiography

Background

Pulmonary hypertension (pH) is a malignant pulmonary circulation disease. Right heart catheterization (RHC) is the gold standard procedure for quantitative evaluation of pulmonary hemodynamics. Accurate and noninvasive quantitative evaluation of pulmonary hemodynamics is challenging due to the limitations of currently available assessment methods.

Methods

Patients who underwent computed tomographic pulmonary angiography (CTPA) and RHC examinations within 2 weeks were included. The dataset was randomly divided into a training set and a test set at an 8:2 ratio. A radiomic feature model and another two-dimensional (2D) feature model aimed to quantitatively evaluate of pulmonary hemodynamics were constructed. The performance of models was determined by calculating the mean squared error, the intraclass correlation coefficient (ICC) and the area under the precision-recall curve (AUC-PR) and performing Bland–Altman analyses.

Results

345 patients: 271 patients with PH (mean age 50 ± 17 years, 93 men) and 74 without PH (mean age 55 ± 16 years, 26 men) were identified. The predictive results of pulmonary hemodynamics of radiomic feature model integrating 5 2D features and other 30 radiomic features were consistent with the results from RHC, and outperformed another 2D feature model. The radiomic feature model exhibited moderate to good reproducibility to predict pulmonary hemodynamic parameters (ICC reached 0.87). In addition, pH can be accurately identified based on a classification model (AUC-PR =0.99).

Conclusion

This study provides a noninvasive method for comprehensively and quantitatively evaluating pulmonary hemodynamics using CTPA images, which has the potential to serve as an alternative to RHC, pending further validation.

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来源期刊

International journal of cardiology 医学-心血管系统

CiteScore

6.80

自引率

5.70%

发文量

758

审稿时长

44 days

期刊介绍： The International Journal of Cardiology is devoted to cardiology in the broadest sense. Both basic research and clinical papers can be submitted. The journal serves the interest of both practicing clinicians and researchers. In addition to original papers, we are launching a range of new manuscript types, including Consensus and Position Papers, Systematic Reviews, Meta-analyses, and Short communications. Case reports are no longer acceptable. Controversial techniques, issues on health policy and social medicine are discussed and serve as useful tools for encouraging debate.