CT 应变指标可以在造血细胞移植后早期诊断闭塞性细支气管炎综合征。
CT strain metrics allow for earlier diagnosis of bronchiolitis obliterans syndrome after hematopoietic cell transplant.
发表日期：2024 Aug 20
作者：
Husham Sharifi, Christopher David Bertini, Mansour Alkhunaizi, Maria Paulina Hernandez, Zayan Musa, Carlos Borges, Ihsan Turk, Lara Bashoura, Burton F Dickey, Guang-Shing Cheng, Gregory A Yanik, Craig Galban, Haiwei Henry Guo, Myrna Godoy, Joseph Reinhardt, Eric Hoffman, Mario Castro, Gabriela Rondon, Amin Alousi, Richard E Champlin, Elizabeth J Shpall, Ying Lu, Samuel Walter Peterson, Keshav Datta, Mark Nicolls, Joe L Hsu, Ajay Sheshadri
来源：
Disease Models & Mechanisms
摘要：
造血细胞移植（HCT）后闭塞性细支气管炎综合征（BOS）与大量的发病率和死亡率相关。定量 CT (qCT) 可以帮助诊断符合美国国立卫生研究院 (NIH) 标准 (NIH-BOS) 的晚期 BOS，但尚未用于诊断早期、通常无症状的 BOS（早期 BOS），从而限制了早期干预和改善结果的潜力。使用肺功能测试 (PFT) 来定义来自两个大型癌症中心的患者的 NIH-BOS、早期 BOS 和混合 BOS（患有限制性肺病的 NIH-BOS），我们应用 qCT 来识别早期 BOS 并区分 BOS 类型。包括短暂损伤或健康肺部的患者进行比较。 PFT 在第 0、6 和 12 个月进行。使用关联统计、主成分分析、条件推理树 (CIT) 和机器学习 (ML) 分类器模型进行分析。我们的队列包括 84 名同种异体 HCT 接受者，其中 66 名 BOS（NIH 定义的、早期的或混合的）和 18 名无 BOS 的接受者。所有 qCT 指标与 1 秒用力呼气量具有中等相关性，并且每个 qCT 指标都将 BOS 与无 BOS 的指标（非 BOS）区分开来（P < 0.0001）。 CIT 区分了 94% 的 BOS 与非 BOS 参与者、85% 的早期 BOS 与非 BOS、92% 的早期 BOS 与 NIH-BOS。 ML 模型诊断 BOS 的曲线下面积 (AUC) 为 0.84（95% 置信区间 [CI] 0.74-0.94），早期 BOS 为 AUC 0.84（95% CI 0.69 - 0.97）。定量 CT 指标可以识别患有早期 BOS 的个体，为对这一弱势群体进行更密切的监测和早期治疗铺平道路。版权所有 © 2024 美国血液学会。
Bronchiolitis obliterans syndrome (BOS) after hematopoietic cell transplantation (HCT) is associated with substantial morbidity and mortality. Quantitative CT (qCT) can help diagnose advanced BOS meeting National Institutes of Health (NIH) criteria (NIH-BOS) but has not been used to diagnose early, often asymptomatic BOS (early BOS), limiting the potential for early intervention and improved outcomes. Using Pulmonary Function Tests (PFT) to define NIH-BOS, early BOS, and mixed BOS (NIH-BOS with restrictive lung disease) in patients from two large cancer centers, we applied qCT to identify early BOS and distinguish between types of BOS. Patients with transient impairment or healthy lungs were included for comparison. PFT were done at month 0, 6, and 12. Analysis was performed with association statistics, principal component analysis, conditional inference trees (CIT), and machine learning (ML) classifier models. Our cohort included 84 allogeneic HCT recipients -- 66 BOS (NIH-defined, early, or mixed) and 18 without BOS. All qCT metrics had moderate correlation with Forced Expiratory Volume in 1 second, and each qCT metric differentiated BOS from those without BOS (non-BOS) (P < 0.0001). CIT's distinguished 94% of participants with BOS versus non-BOS, 85% early BOS versus non-BOS, 92% early BOS versus NIH-BOS. ML models diagnosed BOS with area under the curve (AUC) 0.84 (95% confidence interval [CI] 0.74-0.94) and early BOS with AUC 0.84 (95% CI 0.69 - 0.97). Quantitative CT metrics can identify individuals with early BOS, paving the way for closer monitoring and earlier treatment in this vulnerable population.Copyright © 2024 American Society of Hematology.