The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy.
The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy.
复制标题
解剖形状特征在最初采用最佳药物治疗的简单 B 型主动脉夹层预后中的作用。
DOI：
10.1016/j.compbiomed.2024.108041
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发表时间：
2024
影响因子：
7.7
通讯作者：
GleasonJr,RudolphL
中科院分区：
文献类型：
--
作者：
Liu,Minliang;Dong,Hai;Mazlout,Adam;Wu,Yuxuan;Kalyanasundaram,Asanish;Oshinski,JohnN;Sun,Wei;Elefteriades,JohnA;Leshnower,BradleyG;GleasonJr,RudolphL
目的 目前，采用最佳药物治疗 (OMT) 治疗的无并发症 B 型主动脉夹层 (TBAD) 患者的长期预后仍然较差。主动脉扩张是决定患者长期生存的主要因素。本研究的目的是调查最初接受 OMT 治疗的 TBAD 患者的解剖形状特征与 (i) OMT 结果；(ii) 主动脉生长率之间的关联。方法收集46例初次接受OMT治疗的TBAD急性期和慢性期CT图像108幅。构建 TBAD 的统计形状模型 (SSM)，通过使用主成分分析 (PCA) 和偏最小二乘 (PLS) 回归从每位患者最早的初始 CT 扫描中提取形状特征。此外，根据最早的 CT 扫描对常规形状特征（例如主动脉直径）进行量化，作为比较的基线。我们确定了传统特征和 SSM 特征，这些特征对于区分 OMT“成功”和失败患者具有重要意义。此外，通过 SSM 和传统特征，使用线性和非线性回归和交叉验证来预测主动脉生长率。结果 OMT 成功组和失败组之间尺寸相关的 SSM 和常规特征（平均主动脉直径：p= 0.0484，中心线长度：p= 0.0112，PCA 评分 c 1：p= 0.0192，PLS 评分 t 1：p= 0.0004，t 2：p= 0.0274）存在显着差异，但这些特征无法预测主动脉生长率。与传统特征相比，SSM 形状特征在增长率预测方面表现出优异的结果。使用多元线性回归，在留一法交叉验证中，传统形状特征、PCA 形状特征和 PLS 形状特征的均方根误差 (RMSE) 分别为 1.23、0.85 和 0.84 毫米/年。非线性支持向量回归 (SVR) 使传统特征、PCA 特征和 PLS 特征的 RMSE 分别提高了 0.99、0.54 和 0.43 mm/年。结论 最早扫描的尺寸相关形状特征与 OMT 失败相关，但导致主动脉生长速度的预测存在较大误差。 SSM 特征与非线性回归相结合可能是预测主动脉生长率的一个有前途的途径。
Objective Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome;(ii) aortic growth rate for TBAD patients initially treated with OMT. Methods 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (eg, aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT “success” and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations. Results Size-related SSM and conventional features (mean aortic diameter: p= 0.0484, centerline length: p= 0.0112, PCA score c 1: p= 0.0192, and PLS scores t 1: p= 0.0004, t 2: p= 0.0274) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively. Conclusion Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate.