Department of Radiology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People's Republic of China.
School of Medicine, Xiamen University, Xiamen, Fujian Province, China.
BMC Med Imaging. 2022 Jul 27;22(1):133. doi: 10.1186/s12880-022-00862-x.
To construct a noninvasive radiomics model for evaluating the pathological degree and an individualized treatment strategy for patients with the manifestation of ground glass nodules (GGNs) on CT images.
The retrospective primary cohort investigation included patients with GGNs on CT images who underwent resection between June 2015 and June 2020. The intratumoral regions of interest were segmented semiautomatically, and radiomics features were extracted from the intratumoral and peritumoral regions. After feature selection by ANOVA, Max-Relevance and Min-Redundancy (mRMR) and Least Absolute Shrinkage and Selection Operator (Lasso) regression, a random forest (RF) model was generated. Receiver operating characteristic (ROC) analysis was calculated to evaluate each classification. Shapley additive explanations (SHAP) was applied to interpret the radiomics features.
In this study, 241 patients including atypical adenomatous hyperplasia (AAH) or adenocarcinoma in situ (AIS) (n = 72), minimally invasive adenocarcinoma (MIA) (n = 83) and invasive adenocarcinoma (IAC) (n = 86) were selected for radiomics analysis. Three intratumoral radiomics features and one peritumoral feature were finally identified by the triple RF classifier with an average area under the curve (AUC) of 0.960 (0.963 for AAH/AIS, 0.940 for MIA, 0.978 for IAC) in the training set and 0.944 (0.955 for AAH/AIS, 0.952 for MIA, 0.926 for IAC) in the testing set for evaluation of the GGNs.
The triple classification based on intra- and peritumoral radiomics features derived from the noncontrast CT images had satisfactory performance and may be used as a noninvasive tool for preoperative evaluation of the pure ground-glass nodules and developing of individualized treatment strategies.
构建一种非侵入性的放射组学模型，用于评估 CT 图像上表现为磨玻璃结节（GGN）的患者的病理程度和个体化治疗策略。
本回顾性主要队列研究纳入了 2015 年 6 月至 2020 年 6 月期间在 CT 图像上接受 GGN 切除术的患者。采用半自动方法对肿瘤内感兴趣区域进行分割，并从肿瘤内和肿瘤旁区域提取放射组学特征。通过方差分析（ANOVA）、最大相关性和最小冗余度（mRMR）和最小绝对收缩和选择算子（Lasso）回归进行特征选择后，生成随机森林（RF）模型。计算接收者操作特征（ROC）分析来评估每个分类。应用 Shapley 加法解释（SHAP）解释放射组学特征。
本研究中，对 241 例患者（不典型腺瘤样增生（AAH）或原位腺癌（AIS）n=72 例，微浸润性腺癌（MIA）n=83 例和浸润性腺癌（IAC）n=86 例）进行了放射组学分析。通过三重 RF 分类器最终确定了三个肿瘤内放射组学特征和一个肿瘤旁特征，其在训练集中的平均曲线下面积（AUC）为 0.960（AAH/AIS 为 0.963，MIA 为 0.940，IAC 为 0.978），在测试集中为 0.944（AAH/AIS 为 0.955，MIA 为 0.952，IAC 为 0.926），用于评估 GGN。
基于非对比 CT 图像的肿瘤内和肿瘤旁放射组学特征的三重分类具有较好的性能，可作为术前评估纯磨玻璃结节和制定个体化治疗策略的非侵入性工具。
