Chronic lung allograft dysfunction phenotype and prognosis by machine learning CT analysis
Chronic lung allograft dysfunction phenotype and prognosis by machine learning CT analysis
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DOI：
10.1183/13993003.01652-2021
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发表时间：
2022-07-01
影响因子：
24.3
通讯作者：
Martinu, Tereza
中科院分区：
文献类型：
--
作者：
McInnis, Micheal C.;Ma, Jin;Martinu, Tereza
背景慢性肺移植物功能障碍（CLAD）是肺移植受者移植失败的主要原因，其预后取决于CLAD表型。我们在CLAD诊断中使用了机器学习计算机断层扫描（CT）肺纹理分析工具，以进行表型分析和与放射科医生scoring.Methods比较的统计学分析，这项回顾性研究包括所有成人首次双肺移植患者（2010年1月至2015年12月）与CLAD（2019年12月删失）和CLAD诊断附近的吸气CT。机器学习工具量化了毛玻璃样阴影、网状影、肺透明和肺血管容积（PVV）。两名放射科医生对磨玻璃样阴影、网状影、实变、胸腔积液、空气潴留和支气管扩张进行评分。使用受试者工作特征曲线分析来评估机器学习和放射科医生对CLAD表型的诊断性能。多变量考克斯比例风险回归分析的移植物存活率控制年龄，性别，原生肺疾病，巨细胞病毒血清状态和CLAD phenotype.Results 88例患者包括（57闭塞性细支气管炎综合征（BOS），20限制性移植物综合征（RAS）/混合和11未分类/未定义）与CT平均9.5天从CLAD发病。放射科医生和机器学习参数将RAS/与PVV混合的表型确定为最强指标（曲线下面积（AUC）0.85）。机器学习仅使用吸气CT的高透光肺表型BOS（AUC 0.76）。放射科医生和机器学习参数在多变量分析中预测了移植失败，其中PVV最佳（风险比1.23，95%CT 1.05-1.44; p=0.01）。放射科医生和机器学习评分均与移植失败相关，与CLAD表型无关。PVV是机器学习所独有的，在表型和统计学方面是最强的。
Background Chronic lung allograft dysfunction (CLAD) is the principal cause of graft failure in lung transplant recipients and prognosis depends on CLAD phenotype. We used a machine learning computed tomography (CT) lung texture analysis tool at CLAD diagnosis for phenotyping and prognostication compared with radiologist scoring.Methods This retrospective study included all adult first double lung transplant patients (January 2010-December 2015) with CLAD (censored December 2019) and inspiratory CT near CLAD diagnosis. The machine learning tool quantified ground-glass opacity, reticulation, hyperlucent lung and pulmonary vessel volume (PVV). Two radiologists scored for ground-glass opacity, reticulation, consolidation, pleural effusion, air trapping and bronchiectasis. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of machine learning and radiologist for CLAD phenotype. Multivariable Cox proportional hazards regression analysis for allograft survival controlled for age, sex, native lung disease, cytomegalovirus serostatus and CLAD phenotype.Results 88 patients were included (57 bronchiolitis obliterans syndrome (BOS), 20 restrictive allograft syndrome (RAS)/mixed and 11 unclassified/undefined) with CT a median 9.5 days from CLAD onset. Radiologist and machine learning parameters phenotyped RAS/mixed with PVV as the strongest indicator (area under the curve (AUC) 0.85). Machine learning hyperlucent lung phenotyped BOS using only inspiratory CT (AUC 0.76). Radiologist and machine learning parameters predicted graft failure in the multivariable analysis, best with PVV (hazard ratio 1.23, 95% CT 1.05-1.44; p=0.01).Conclusions Machine learning discriminated between CLAD phenotypes on CT. Both radiologist and machine learning scoring were associated with graft failure, independent of CLAD phenotype. PVV, unique to machine learning, was the strongest in phenotyping and prognostication.