Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Vital Images, Minnetonka, MN, USA.
Sci Rep. 2020 Sep 3;10(1):14585. doi: 10.1038/s41598-020-70316-3.
The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.
本研究旨在开发和测试用于评估 CT 上表现为亚实性结节的单个肺腺癌侵袭性的多类预测模型。共纳入 227 例肺腺癌：31 例非典型腺瘤性增生和原位腺癌（H1 类），64 例微浸润性腺癌（H2 类）和 132 例浸润性腺癌（H3 类）。对结节进行分割，并提取几何和 CT 衰减特征，包括功能主成分分析特征（FPC1 和 FPC2）。在特征选择步骤后，使用有序回归建立了两个预测模型：基于体积（对数）（结节体积的对数）和 FPC1 的模型 1，以及基于体积（对数）和 Q.875（CT 衰减值的 87.5%分位数）的模型 2。使用 200 次重复的蒙特卡罗交叉验证方法，这些模型提供了侵袭性的多类分类，区分能力 AUC 为 0.83 至 0.87，并预测了类概率，平均误差小于 10%。本文采用的预测建模方法详细说明了主要预测因子的价值如何有助于结节侵袭性的概率，并强调了结节 CT 衰减特征在结节侵袭性分类中的作用。
