Grubert Van Iderstine Micah, Kim Sangwook, Karur Gauri Rani, Granton John, de Perrot Marc, McIntosh Chris, McInnis Micheal
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Eur Radiol. 2025 Aug 23. doi: 10.1007/s00330-025-11972-9.
The aim of this study was to develop machine learning (ML) models to explore the relationship between chronic pulmonary embolism (PE) burden and severe pulmonary hypertension (PH) in surgical chronic thromboembolic pulmonary hypertension (CTEPH).
CTEPH patients with a preoperative CT pulmonary angiogram and pulmonary endarterectomy between 01/2017 and 06/2022 were included. A mean pulmonary artery pressure of > 50 mmHg was classified as severe. CTs were scored by a blinded radiologist who recorded chronic pulmonary embolism extent in detail, and measured the right ventricle (RV), left ventricle (LV), main pulmonary artery (PA) and ascending aorta (Ao) diameters. XGBoost models were developed to identify CTEPH feature importance and compared to a logistic regression model.
There were 184 patients included; 54.9% were female, and 21.7% had severe PH. The average age was 57 ± 15 years. PE burden alone was not helpful in identifying severe PH. The RV/LV ratio logistic regression model performed well (AUC 0.76) with a cutoff of 1.4. A baseline ML model (Model 1) including only the RV, LV, Pa and Ao measures and their ratios yielded an average AUC of 0.66 ± 0.10. The addition of demographics and statistics summarizing the CT findings raised the AUC to 0.75 ± 0.08 (F1 score 0.41).
While measures of PE burden had little bearing on PH severity independently, the RV/LV ratio, extent of disease in various segments, total webs observed, and patient demographics improved performance of machine learning models in identifying severe PH.
Question Can machine learning methods applied to CT-based cardiac measurements and detailed maps of chronic thromboembolism type and distribution predict pulmonary hypertension (PH) severity? Findings The right-to-left ventricle (RV/LV) ratio was predictive of PH severity with an optimal cutoff of 1.4, and detailed accounts of chronic thromboembolic burden improved model performance. Clinical relevance The identification of a CT-based RV/LV ratio cutoff of 1.4 gives radiologists, clinicians, and patients a point of reference for chronic thromboembolic PH severity. Detailed chronic thromboembolic burden data are useful but cannot be used alone to predict PH severity.
本研究旨在开发机器学习（ML）模型，以探索手术治疗的慢性血栓栓塞性肺动脉高压（CTEPH）中慢性肺栓塞（PE）负荷与重度肺动脉高压（PH）之间的关系。
纳入2017年1月至2022年6月期间术前行CT肺动脉造影并接受肺动脉内膜剥脱术的CTEPH患者。平均肺动脉压>50 mmHg被分类为重度。CT由一名不知情的放射科医生进行评分，该医生详细记录慢性肺栓塞范围，并测量右心室（RV）、左心室（LV）、主肺动脉（PA）和升主动脉（Ao）直径。开发XGBoost模型以确定CTEPH特征的重要性，并与逻辑回归模型进行比较。
共纳入184例患者；54.9%为女性，21.7%患有重度PH。平均年龄为57±15岁。仅PE负荷对识别重度PH并无帮助。右心室/左心室比值逻辑回归模型表现良好（AUC为0.76），截断值为1.4。仅包含RV、LV、PA和Ao测量值及其比值的基线ML模型（模型1）的平均AUC为0.66±0.10。纳入人口统计学数据和总结CT结果的统计数据后，AUC提高至0.75±0.08（F1分数为0.41）。
虽然PE负荷测量单独对PH严重程度影响不大，但右心室/左心室比值、各节段疾病范围、观察到的总条索以及患者人口统计学数据可提高机器学习模型识别重度PH的性能。
问题 应用于基于CT的心脏测量以及慢性血栓栓塞类型和分布的详细图谱的机器学习方法能否预测肺动脉高压（PH）的严重程度？ 发现 右心室与左心室（RV/LV）比值可预测PH严重程度，最佳截断值为1.4，慢性血栓栓塞负荷的详细数据可改善模型性能。 临床意义 确定基于CT的RV/LV比值截断值为1.4，为放射科医生、临床医生和患者提供了慢性血栓栓塞性PH严重程度的参考点。详细的慢性血栓栓塞负荷数据有用，但不能单独用于预测PH严重程度。
