使用计算机辅助结节评估和风险收益（CANARY）对肺腺癌谱系肺结节的组织病理学特征进行无创特征描述——一项初步研究。
Noninvasive characterization of the histopathologic features of pulmonary nodules of the lung adenocarcinoma spectrum using computer-aided nodule assessment and risk yield (CANARY)--a pilot study.
机构信息
Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA.
出版信息
J Thorac Oncol. 2013 Apr;8(4):452-60. doi: 10.1097/JTO.0b013e3182843721.
INTRODUCTION
Pulmonary nodules of the adenocarcinoma spectrum are characterized by distinctive morphological and radiologic features and variable prognosis. Noninvasive high-resolution computed tomography-based risk stratification tools are needed to individualize their management.
METHODS
Radiologic measurements of histopathologic tissue invasion were developed in a training set of 54 pulmonary nodules of the adenocarcinoma spectrum and validated in 86 consecutively resected nodules. Nodules were isolated and characterized by computer-aided analysis, and data were analyzed by Spearman correlation, sensitivity, and specificity and the positive and negative predictive values.
RESULTS
Computer-aided nodule assessment and risk yield (CANARY) can noninvasively characterize pulmonary nodules of the adenocarcinoma spectrum. Unsupervised clustering analysis of high-resolution computed tomography data identified nine unique exemplars representing the basic radiologic building blocks of these lesions. The exemplar distribution within each nodule correlated well with the proportion of histologic tissue invasion, Spearman R = 0.87, p < 0.0001 and 0.89 and p < 0.0001 for the training and the validation set, respectively. Clustering of the exemplars in three-dimensional space corresponding to tissue invasion and lepidic growth was used to develop a CANARY decision algorithm that successfully categorized these pulmonary nodules as "aggressive" (invasive adenocarcinoma) or "indolent" (adenocarcinoma in situ and minimally invasive adenocarcinoma). Sensitivity, specificity, positive predictive value, and negative predictive value of this approach for the detection of aggressive lesions were 95.4, 96.8, 95.4, and 96.8%, respectively, in the training set and 98.7, 63.6, 94.9, and 87.5%, respectively, in the validation set.
CONCLUSION
CANARY represents a promising tool to noninvasively risk stratify pulmonary nodules of the adenocarcinoma spectrum.
简介
肺腺癌谱系中的肺结节具有独特的形态学和影像学特征，以及不同的预后。需要非侵入性的高分辨率计算机断层扫描风险分层工具来个体化管理这些结节。
方法
在一组 54 个肺腺癌谱系肺结节的训练集中开发了组织侵袭的放射学测量，并在 86 个连续切除的结节中进行了验证。通过计算机辅助分析对结节进行分离和特征描述，并通过 Spearman 相关系数、敏感性、特异性以及阳性和阴性预测值进行数据分析。
结果
计算机辅助结节评估和风险预测（CANARY）可以对肺腺癌谱系中的肺结节进行非侵入性特征描述。高分辨率计算机断层扫描数据的无监督聚类分析确定了 9 个独特的示例，代表了这些病变的基本影像学构建块。每个结节中示例的分布与组织侵袭的比例密切相关，Spearman R 值分别为 0.87（p<0.0001）和 0.89（p<0.0001），用于训练集和验证集。根据组织侵袭和贴壁生长的三维空间对示例进行聚类，开发了 CANARY 决策算法，该算法成功地将这些肺结节分类为“侵袭性”（浸润性腺癌）或“惰性”（原位腺癌和微浸润性腺癌）。在训练集中，该方法检测侵袭性病变的敏感性、特异性、阳性预测值和阴性预测值分别为 95.4%、96.8%、95.4%和 96.8%，在验证集中分别为 98.7%、63.6%、94.9%和 87.5%。
结论
CANARY 是一种有前途的非侵入性肺腺癌谱系肺结节风险分层工具。
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