Abstract：
OBJECTIVE: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant.
METHODS: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model.
RESULTS: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model.
CONCLUSIONS: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery..
METHODS: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model.
RESULTS: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model.
CONCLUSIONS: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery..
摘要：
目的：在本研究中，我们基于高分辨率计算机断层扫描(HRCT)图像创建了一个影像组学模型,用于非侵入性预测亚厘米纯磨玻璃结节(pGGN)是良性还是恶性.
方法：对235例患者（251亚厘米pGGNs）进行了术前HRCT扫描并有术后病理结果的回顾性评估。结节以7:3的比例随机分配到训练组(n=175)和验证组(n=76)。在薄层肺窗中描绘了感兴趣的体积，从中提取了1316个影像组学特征。使用最小绝对收缩和选择算子(LASSO)来选择影像组学特征。使用单变量和多变量逻辑回归评估独立风险变量。通过获得临床受试者工作特征（ROC）曲线来评估性能，影像组学，和组合模型，然后决策曲线分析（DCA）评估每个模型的临床适用性。
结果：性别，volume,形状,通过单因素分析选择强度均值建立临床模型。通过LASSO回归保留了两个影像组学特征以建立影像组学模型。在训练组中，影像组学(AUC=0.844)和联合模型(AUC=0.871)的曲线下面积(AUC)高于临床模型(AUC=0.773)。在评估亚厘米pGGN是否为良性时，DCA表明,与临床模型相比,影像组学和联合模型具有更大的总体净获益.
结论：影像组学模型可用于预测手术前良性和恶性亚厘米pGGN。。
方法：对235例患者（251亚厘米pGGNs）进行了术前HRCT扫描并有术后病理结果的回顾性评估。结节以7:3的比例随机分配到训练组(n=175)和验证组(n=76)。在薄层肺窗中描绘了感兴趣的体积，从中提取了1316个影像组学特征。使用最小绝对收缩和选择算子(LASSO)来选择影像组学特征。使用单变量和多变量逻辑回归评估独立风险变量。通过获得临床受试者工作特征（ROC）曲线来评估性能，影像组学，和组合模型，然后决策曲线分析（DCA）评估每个模型的临床适用性。
结果：性别，volume,形状,通过单因素分析选择强度均值建立临床模型。通过LASSO回归保留了两个影像组学特征以建立影像组学模型。在训练组中，影像组学(AUC=0.844)和联合模型(AUC=0.871)的曲线下面积(AUC)高于临床模型(AUC=0.773)。在评估亚厘米pGGN是否为良性时，DCA表明,与临床模型相比,影像组学和联合模型具有更大的总体净获益.
结论：影像组学模型可用于预测手术前良性和恶性亚厘米pGGN。。
