Building a Risk Prediction Model for Postoperative Pulmonary Vein Obstruction via Quantitative Analysis of CTA Images
Building a Risk Prediction Model for Postoperative Pulmonary Vein Obstruction via Quantitative Analysis of CTA Images
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DOI：
10.1109/jbhi.2022.3146590
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发表时间：
2022-07-01
影响因子：
7.7
通讯作者：
Wang, Lisheng
中科院分区：
文献类型：
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作者：
Pei, Yuchen;Shi, Guocheng;Wang, Lisheng
完全性肺静脉连接异常（TAPVC）是一种罕见但致命的儿童先天性心脏病，可通过外科手术修复。然而，部分患者术后可能出现肺静脉阻塞（PVO），血供不足，需要特殊的随访策略和治疗。因此，术前对此类患者的预测是临床上重要而又具有挑战性的问题。在本文中，我们解决了这一问题，并提出了一个计算框架，用于从计算机断层血管造影（CTA）图像中确定术后PVO （PPVO）的危险因素，并建立PPVO风险预测模型。从临床经验来看，这些危险因素可能来自患者的左心房（LA）和肺静脉（PV）。因此，首先从低剂量CTA图像重建LA和PV的三维模型。然后，通过计算LA和PV的三维模型的不同形态特征，以及LA和PV的耦合空间特征，构建特征池；最后，利用机器学习技术从特征池中识别出四个风险因素，然后建立风险预测模型。因此，不仅可以有效地预测PPVO患者，而且可以量化文献中报道的定性危险因素。最后，在两家医院的两个独立临床数据集上对风险预测模型进行了评估。该模型的AUC值分别为0.88和0.87，表明了该模型在风险预测中的有效性。
Total anomalous pulmonary venous connection (TAPVC) is a rare but mortal congenital heart disease in children and can be repaired by surgical operations. However, some patients may suffer from pulmonary venous obstruction (PVO) after surgery with insufficient blood supply, necessitating special follow-up strategy and treatment. Therefore, it is a clinically important yet challenging problem to predict such patients before surgery. In this paper, we address this issue and propose a computational framework to determine the risk factors for postoperative PVO (PPVO) from computed tomography angiography (CTA) images and build the PPVO risk prediction model. From clinical experiences, such risk factors are likely from the left atrium (LA) and pulmonary vein (PV) of the patient. Thus, 3D models of LA and PV are first reconstructed from low-dose CTA images. Then, a feature pool is built by computing different morphological features from 3D models of LA and PV, and the coupling spatial features of LA and PV. Finally, four risk factors are identified from the feature pool using the machine learning techniques, followed by a risk prediction model. As a result, not only PPVO patients can be effectively predicted but also qualitative risk factors reported in the literature can now be quantified. Finally, the risk prediction model is evaluated on two independent clinical datasets from two hospitals. The model can achieve the AUC values of 0.88 and 0.87 respectively, demonstrating its effectiveness in risk prediction.