■慢性阻塞性肺疾病(COPD)构成了巨大的全球健康负担,要求先进的诊断工具进行早期检测和准确的表型。在这行,这项研究旨在通过比较传统的参数反应映射(PRM)和一种新颖的自我监督异常检测方法之间的空间和定量关系,来增强胸部计算机断层扫描(CT)的COPD特征。并揭示COPD动态过渡阶段的其他潜在见解。
■回顾性评估了COPDGene数据集中的1,310名从不吸烟者和GOLD0人以及COPD患者(GOLD1-4)的非对比吸气和呼气CT。一种新的自我监督异常检测方法被应用于量化与COPD相关的肺部异常,作为区域偏差。对这些区域异常分数进行了定性和定量比较,每个黄金类,至PRM卷(肺气肿:PRMEmph,功能性小气道疾病:PRMfSAD)和主成分分析(PCA)和聚类,应用于自监督潜在空间。还评估了其与肺功能测试(PFTs)的关系。
■自监督潜在空间的初始t分布随机邻居嵌入(t-SNE)可视化突出了不同的空间模式,揭示有和没有肺气肿和空气滞留的区域之间的明显分离。通过PCA和聚类分析在该潜在空间中确定了四个稳定簇。随着黄金阶段的增加,PRMEmph,PRMfSAD,异常评分,集群3的卷显示出不断升级的趋势,与集群2的下降形成鲜明对比。不同阶段的患者异常评分显着不同(p<0.01),除了从不吸烟者和GOLD0患者。相比之下,PRMEmph,PRMfSAD,和聚类类别显示较少的显著差异。皮尔逊相关系数揭示了与PFTs的中等异常评分相关性(0.41-0.68),除了功能剩余容量和吸烟持续时间。异常评分与PRMEmph(r=0.66,p<0.01)和PRMfSAD(r=0.61,p<0.01)相关。异常评分显着改善了PRM调整的多变量模型对预测临床参数的拟合(p<0.001)。Bland-Altman图显示,在整个测量范围内,PRM衍生的体积与簇之间的体积一致性并不恒定。
■我们的研究强调了异常检测方法和传统PRM在捕获COPD细微差别异质性方面的协同效用。观察到的空间格局差异,集群动力学,与PFT的相关性强调了这些方法的独特但互补的优势。整合异常检测和PRM为了解COPD病理生理学提供了有希望的途径。可能提供更有针对性的诊断和干预方法,以改善患者的预后。
UNASSIGNED: Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD.
UNASSIGNED: Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated.
UNASSIGNED: Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements.
UNASSIGNED: Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.