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分子分类器作为高分辨率计算机断层扫描的补充,用于识别普通间质性肺炎的效用。

Utility of a Molecular Classifier as a Complement to High-Resolution Computed Tomography to Identify Usual Interstitial Pneumonia.

机构信息

Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.

Department of Internal Medicine, University of Utah, Salt Lake City, Utah.

出版信息

Am J Respir Crit Care Med. 2021 Jan 15;203(2):211-220. doi: 10.1164/rccm.202003-0877OC.

DOI:10.1164/rccm.202003-0877OC
PMID:32721166
Abstract

Usual interstitial pneumonia (UIP) is the defining morphology of idiopathic pulmonary fibrosis (IPF). Guidelines for IPF diagnosis conditionally recommend surgical lung biopsy for histopathology diagnosis of UIP when radiology and clinical context are not definitive. A "molecular diagnosis of UIP" in transbronchial lung biopsy, the Envisia Genomic Classifier, accurately predicted histopathologic UIP. We evaluated the combined accuracy of the Envisia Genomic Classifier and local radiology in the detection of UIP pattern. Ninety-six patients who had diagnostic lung pathology as well as a transbronchial lung biopsy for molecular testing with Envisia Genomic Classifier were included in this analysis. The classifier results were scored against reference pathology. UIP identified on high-resolution computed tomography (HRCT) as documented by features in local radiologists' reports was compared with histopathology. In 96 patients, the Envisia Classifier achieved a specificity of 92.1% (confidence interval [CI],78.6-98.3%) and a sensitivity of 60.3% (CI, 46.6-73.0%) for histology-proven UIP pattern. Local radiologists identified UIP in 18 of 53 patients with UIP histopathology, with a sensitivity of 34.0% (CI, 21.5-48.3%) and a specificity of 96.9% (CI, 83.8-100%). In conjunction with HRCT patterns of UIP, the Envisia Classifier results identified 24 additional patients with UIP (sensitivity 79.2%; specificity 90.6%). In 96 patients with suspected interstitial lung disease, the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by the Envisia Genomic Classifier combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy.

摘要

普通型间质性肺炎(UIP)是特发性肺纤维化(IPF)的典型形态学特征。对于 IPF 的诊断,指南建议在影像学和临床情况不明确的情况下,对 UIP 进行有条件的外科肺活检以进行组织病理学诊断。在经支气管肺活检中,一种名为 Envisia 基因组分类器的“UIP 的分子诊断”,可准确预测组织病理学 UIP。我们评估了 Envisia 基因组分类器和局部影像学在检测 UIP 模式方面的综合准确性。这项分析共纳入了 96 名患者,这些患者均进行了诊断性肺部病理学检查,并且进行了经支气管肺活检,以进行 Envisia 基因组分类器的分子检测。将分类器的结果与参考病理学进行评分。将高分辨率计算机断层扫描(HRCT)上确定的 UIP(通过当地放射科医生报告中的特征确定)与组织病理学进行比较。在 96 例患者中,Envisia 分类器在组织学上证实 UIP 模式的特异性为 92.1%(置信区间 [CI],78.6%-98.3%),敏感性为 60.3%(CI,46.6%-73.0%)。在 53 例组织学上证实为 UIP 的患者中,18 例当地放射科医生诊断为 UIP,敏感性为 34.0%(CI,21.5%-48.3%),特异性为 96.9%(CI,83.8%-100%)。结合 UIP 的 HRCT 模式,Envisia 分类器结果还确定了 24 例 UIP 患者(敏感性为 79.2%;特异性为 90.6%)。在 96 例疑似间质性肺病患者中,无论 HRCT 模式如何,Envisia 基因组分类器都能识别 UIP。这些结果表明,Envisia 基因组分类器联合 HRCT 以及多学科讨论中的临床因素,对 UIP 模式的识别,可能有助于临床医生在无需进行外科肺活检的情况下,对间质性肺病(尤其是 IPF)做出诊断。

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