Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study.
Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study.
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DOI：
10.1148/radiol.2016161315
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发表时间：
2017-05
期刊：
影响因子：
19.7
通讯作者：
O'Regan DP
中科院分区：
文献类型：
--
作者：
Dawes TJW;de Marvao A;Shi W;Fletcher T;Watson GMJ;Wharton J;Rhodes CJ;Howard LSGE;Gibbs JSR;Rueckert D;Cook SA;Wilkins MR;O'Regan DP
应用机器学习从心脏MR图像中获得的复杂运动表型，可以更准确地预测肺动脉高压患者的预后。目的：通过心脏收缩运动三维模式的监督式机器学习，确定肺动脉高压患者的生存和右心室衰竭的机制是否可以预测。该研究得到了研究伦理委员会的批准，参与者给予书面知情同意。256例新诊断为肺动脉高压的患者（143例女性，平均年龄±标准差，63岁±17岁）接受了心脏磁共振（MR）成像、右侧心导管插入和6分钟步行测试，中位随访时间为4.0年。采用半自动化分割短轴电影图像，建立了右心室运动的三维模型。监督主成分分析用于确定最能预测生存的收缩运动模式。通过中位生存时间和曲线下面积的差异以及1年生存时间相关的受试者工作特征分析来评估生存预测。随访结束时，36%的患者（256例中的93例）死亡，1例进行了肺移植。较差的结果是室间隔和游离壁的有效收缩丧失，加上基底纵向运动减少。当与常规影像学、血流动力学、功能和临床指标相结合时，三维心脏运动改善了生存预测（受试者工作特征曲线下面积，分别为0.73 vs 0.60, P < 0.001），并根据高危组和低危组的中位生存时间差异提供了更大的区分（分别为13.8 vs 10.7年，P < 0.001）。一种机器学习生存模型，利用三维心脏运动预测新诊断肺动脉高压患者的独立于传统危险因素的结果。本文的在线补充材料是可用的。
Applying machine learning of complex motion phenotypes obtained from cardiac MR images allows more accurate prediction of patient outcomes in pulmonary hypertension. To determine if patient survival and mechanisms of right ventricular failure in pulmonary hypertension could be predicted by using supervised machine learning of three-dimensional patterns of systolic cardiac motion. The study was approved by a research ethics committee, and participants gave written informed consent. Two hundred fifty-six patients (143 women; mean age ± standard deviation, 63 years ± 17) with newly diagnosed pulmonary hypertension underwent cardiac magnetic resonance (MR) imaging, right-sided heart catheterization, and 6-minute walk testing with a median follow-up of 4.0 years. Semiautomated segmentation of short-axis cine images was used to create a three-dimensional model of right ventricular motion. Supervised principal components analysis was used to identify patterns of systolic motion that were most strongly predictive of survival. Survival prediction was assessed by using difference in median survival time and area under the curve with time-dependent receiver operating characteristic analysis for 1-year survival. At the end of follow-up, 36% of patients (93 of 256) died, and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and free wall, coupled with reduced basal longitudinal motion. When added to conventional imaging and hemodynamic, functional, and clinical markers, three-dimensional cardiac motion improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, respectively; P < .001) and provided greater differentiation according to difference in median survival time between high- and low-risk groups (13.8 vs 10.7 years, respectively; P < .001). A machine-learning survival model that uses three-dimensional cardiac motion predicts outcome independent of conventional risk factors in patients with newly diagnosed pulmonary hypertension. Online supplemental material is available for this article.