Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT is Associated with FEV1 Decline in COPD

Muhammad Faizyab Ali Chaudhary, Hira Anees Awan, Sarah E Gerard, Sandeep Bodduluri, Alejandro P Comellas, Igor Z Barjaktarevic, R Graham Barr, Christopher B Cooper, Craig J Galban, MeiLan K Han, Jeffrey L Curtis, Nadia N Hansel, Jerry A Krishnan, Martha G Menchaca, Fernando J Martinez, Jill Ohar, Luis G. Vargas Buonfiglio, Robert Paine, Surya P Bhatt, Eric A Hoffman, Joseph M Reinhardt
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Abstract

Rationale: Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSADTLC). Objectives: To evaluate an AI model for estimating fSADTLC and study its clinical associations in chronic obstructive pulmonary disease (COPD). Methods: We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a subset (n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema. Measurements and Main Results: Inspiratory fSADTLC was highly correlated with fSADPRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSADTLC was associated with FEV1 (L) (adj.β = -0.034, P < 0.001), FEV1/FVC (adj.β = -0.008, P < 0.001), SGRQ (adj.β = 0.243, P < 0.001), and FEV1 decline (mL / year) (adj.β = -1.156, P < 0.001). fSADTLC was also associated with FEV1 (L) (adj.β = -0.032, P < 0.001), FEV1/FVC (adj.β = -0.007, P < 0.001), SGRQ (adj.β = 0.190, P = 0.02), and FEV1 decline (mL / year) (adj.β = -0.866, P = 0.001) in COPDGene. We found fSADTLC to be more repeatable than fSADPRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88). Conclusions: Inspiratory fSADTLC captures small airways disease as reliably as fSADPRM and is associated with FEV1 decline.
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通过深度学习估计吸气胸部 CT 中的小气道疾病与慢性阻塞性肺疾病患者的 FEV1 下降有关
理论依据:量化功能性小气道疾病(fSAD)需要额外的呼气计算机断层扫描(CT),这限制了临床应用。人工智能(AI)可仅通过胸部 CT 扫描的总肺活量(TLC)(fSADTLC)来量化功能性小气道疾病:评估估算 fSADTLC 的人工智能模型,并研究其与慢性阻塞性肺病(COPD)的临床关联:我们分析了 2513 名慢性阻塞性肺病亚人群和中期结果测量研究(SPIROMICS)的参与者。利用一个子集(n = 1055),我们开发了一个生成模型来生成虚拟呼气 CT,用于估算其余 1458 名 SPIROMICS 参与者的 fSADTLC。我们将 fSADTLC 与双容积参数反应映射 fSADPRM 进行了比较。我们研究了 fSADTLC 与 FEV1、FEV1/FVC、六分钟步行距离 (6MWD)、圣乔治呼吸问卷 (SGRQ) 和 FEV1 下降之间的单变量和多变量关系。这些结果在 COPDGene 研究的一个子集(n = 458)中得到了验证。多变量模型对年龄、种族、性别、体重指数、基线 FEV1、吸烟包年、吸烟状态和肺气肿百分比进行了调整。测量和主要结果:在 SPIROMICS(Pearson's R = 0.895)和 COPDGene(R = 0.897)队列中,吸气 fSADTLC 与 fSADPRM 高度相关。在 SPIROMICS 中,fSADTLC 与 FEV1 (L)(adj.β = -0.034,P < 0.001)、FEV1/FVC(adj.β = -0.008,P < 0.001)、SGRQ(adj.β = 0.243,P < 0.001)和 FEV1 下降(毫升/年)(adj.β = -1.156 ,P < 0.001)相关。在 COPDGene 中,fSADTLC 也与 FEV1 (L) (adj.β = -0.032,P < 0.001)、FEV1/FVC(adj.β = -0.007,P < 0.001)、SGRQ(adj.β = 0.190,P = 0.02)和 FEV1 下降(毫升/年)(adj.β = -0.866,P = 0.001)相关。我们发现 fSADTLC 比 fSADPRM 具有更高的重复性,类内相关性为 0.99(95% CI:0.98,0.99)vs 0.83(95% CI:0.76,0.88):吸气 fSADTLC 与 fSADPRM 一样可靠地捕捉小气道疾病,并且与 FEV1 下降相关。
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