Ground-glass nodule (GGN)-like adenocarcinoma is a special subtype of lung cancer. The invasiveness of the nodule correlates well with the patient’s prognosis. This study aimed to establish a radiomic model for invasiveness differentiation of malignant nodules manifesting as ground glass on high-resolution computed tomography (HRCT). Between January 2014 and July 2019, 276 pulmonary nodules manifesting as GGNs on preoperative HRCTs, whose histological results were available, were collected. The nodules were randomly classified into training (n = 221) and independent testing (n = 55) cohorts. Three logistic models using features derived from HRCT were fit in the training cohort and validated in both aforementioned cohorts for invasive adenocarcinoma and preinvasive-minimally invasive adenocarcinoma (MIA) differentiation. The model with the best performance was presented as a nomogram and was validated using a calibration curve before performing a decision curve analysis. The benefit of using the proposed model was also shown by groups of management strategies recommended by The Fleischner Society. The combined model showed the best differentiation performance (area under the curve (AUC), training set = 0.89, and testing set = 0.92). The quantitative texture model showed better performance (AUC, training set = 0.87, and testing set = 0.91) than the semantic model (AUC, training set = 0.83, and testing set = 0.79). Of the 94 type 2 nodules that were IACs, 66 were identified by this model. Models using features derived from imaging are effective for differentiating between preinvasive-MIA and IACs among lung adenocarcinomas appearing as GGNs on CT images.

中文翻译：

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使用高分辨率计算机断层扫描特征对表现为磨玻璃结节的恶性结节进行侵袭性鉴别的可行性

磨玻璃结节（GGN）样腺癌是肺癌的一种特殊亚型。结节的侵袭性与患者的预后密切相关。本研究旨在建立一个影像组学模型，用于在高分辨率计算机断层扫描 (HRCT) 上表现为毛玻璃样的恶性结节的侵袭性分化。在 2014 年 1 月至 2019 年 7 月期间，收集了 276 个在术前 HRCT 上表现为 GGN 的肺结节，其组织学结果可用。结节被随机分为训练（n  = 221）和独立测试（n = 55) 队列。三个使用 HRCT 特征的逻辑模型适合训练队列，并在上述两个队列中验证了浸润性腺癌和浸润前微创腺癌 (MIA) 分化。具有最佳性能的模型以列线图的形式呈现，并在执行决策曲线分析之前使用校准曲线进行验证。Fleischner Society 推荐的管理策略组也显示了使用建议模型的好处。组合模型显示出最佳的分化性能（曲线下面积 (AUC)，训练集 = 0.89，测试集 = 0.92）。定量纹理模型显示出比语义模型（AUC，训练集 = 0.83，测试集 = 0.79）更好的性能（AUC，训练集 = 0.87，测试集 = 0.91）。在 94 个 IAC 的 2 型结节中，66 个被该模型识别。使用来自成像的特征的模型可有效区分在 CT 图像上显示为 GGN 的肺腺癌中的侵袭前 MIA 和 IAC。