Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui, 323000, China.
Department of Radiology, Lishui People's Hospital, Lishui, 323000, China.
Clin Radiol. 2019 Dec;74(12):933-943. doi: 10.1016/j.crad.2019.07.026. Epub 2019 Sep 11.
A nomogram model was developed to predict the histological subtypes of lung invasive adenocarcinomas (IAs) and minimally invasive adenocarcinomas (MIAs) that manifest as part-solid ground-glass nodules (GGNs).
This retrospective study enrolled 119 patients with histopathologically confirmed part-solid GGNs assigned to the training (n=83) or testing cohorts (n=36). Radiomic features were extracted based on the unenhanced computed tomography (CT) images. R software was applied to process the qualitative and quantitative data. The CT features model, radiomic signature model, and combined prediction model were constructed and compared.
A total of 396 radiomic features were extracted from the preoperative CT images, four features including MaxIntensity, RMS, ZonePercentage, and LongRunEmphasis_angle0_offset7 were indicated to be the best discriminators to establish the radiomic signature model. The performance of the model was satisfactory in both the training and testing set with areas under the curve (AUCs) of 0.854 (95% confidence interval [CI]: 0.774 to 0.934) and 0.813 (95% CI: 0.670 to 0.955), respectively. The CT morphology of the lesion shape and diameter of the solid component were confirmed to be a significant feature for building the CT features model, which had an AUC of 0.755 (95% CI: 0.648 to 0.843). A nomogram that integrated lesion shape and radiomic signature was constructed, which contributed an AUC of 0.888 (95% CI: 0.82 to 0.955).
The radiomic signature could provide an important reference for differentiating IAs from MIAs, and could be significantly enhanced by the addition of CT morphology. The nomogram may be highly informative for making clinical decisions.
建立一个列线图模型，以预测表现为部分实性磨玻璃结节（GGN）的肺浸润性腺癌（IA）和微浸润性腺癌（MIA）的组织学亚型。
本回顾性研究纳入了 119 名经组织病理学证实的部分实性 GGN 患者，将其分为训练队列（n=83）和测试队列（n=36）。基于平扫 CT 图像提取放射组学特征。R 软件用于处理定性和定量数据。构建并比较了 CT 特征模型、放射组学特征模型和联合预测模型。
从术前 CT 图像中提取了 396 个放射组学特征，其中包括 MaxIntensity、RMS、ZonePercentage 和 LongRunEmphasis_angle0_offset7 在内的 4 个特征被确定为建立放射组学特征模型的最佳鉴别特征。该模型在训练集和测试集中的表现均令人满意，曲线下面积（AUC）分别为 0.854（95%置信区间 [CI]：0.774 至 0.934）和 0.813（95%CI：0.670 至 0.955）。病变形状和实性成分直径的 CT 形态学被证实是构建 CT 特征模型的重要特征，该模型的 AUC 为 0.755（95%CI：0.648 至 0.843）。构建了一个整合病变形状和放射组学特征的列线图，其 AUC 为 0.888（95%CI：0.82 至 0.955）。
放射组学特征可为鉴别 IA 和 MIA 提供重要参考，且通过添加 CT 形态学特征可显著增强其效能。该列线图可能为临床决策提供重要信息。
