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Abstract：
UNASSIGNED: Ground-glass nodule (GGN) is the most common manifestation of lung adenocarcinoma on computed tomography (CT). Clinically, the success rate of preoperative diagnosis of GGN by puncture biopsy and other means is still low. The aim of this study is to investigate the clinical and radiomics characteristics of lung adenocarcinoma presenting as GGN on CT images using radiomics analysis methods, establish a radiomics model, and predict the classification of pathological tissue and instability of GGN type lung adenocarcinoma.
UNASSIGNED: This study retrospectively collected 249 patients with 298 GGN lesions who were pathologically confirmed of having lung adenocarcinoma. The images were imported into the Siemens scientific research prototype software to outline the region of interest and extract the radiomics features. Logistic model A (a radiomics model to identify the infiltration of lung adenocarcinoma manifesting as GGNs) was established using features after the dimensionality reduction process. The receiver operating characteristic (ROC) curve of the model on training set and the verification set was drawn, and the area under the curve (AUC) was calculated. Second, a total of 112 lesions were selected from 298 lesions originating from CT images of at least two occasions, and the time between the first CT and the preoperative CT was defined as not less than 90 days. The mass doubling time (MDT) of all lesions was calculated. According to the different MDT diagnostic thresholds instability was predicted. Finally, their AUCs were calculated and compared.
UNASSIGNED: There were statistically significant differences in age and lesion location distribution between the \"noninvasive\" lesion group and the invasive lesion group (P<0.05), but there were no statistically significant differences in sex (P>0.05). Model A had an AUC of 0.89, sensitivity of 0.75, and specificity of 0.86 in the training set and an AUC of 0.87, sensitivity of 0.63, and specificity of 0.90 in the validation set. There was no significant difference statistically in MDT between \"noninvasive\" lesions and invasive lesions (P>0.05). The AUCs of radiomics models B1, B2 and B3 were 0.89, 0.80, and 0.81, respectively; the sensitivities were 0.71, 0.54, and 0.76, respectively; the specificities were 0.83, 0.77, and 0.60, respectively; and the accuracies were 0.78, 0.65, and 0.69, respectively.
UNASSIGNED: There were statistically significant differences in age and location of lesions between the \"noninvasive\" lesion group and the invasive lesion group. The radiomics model can predict the invasiveness of lung adenocarcinoma manifesting as GGNs. There was no significant difference in MDT between \"noninvasive\" lesions and invasive lesions. The radiomics model can predict the instability of lung adenocarcinoma manifesting as GGN. When the threshold of MDT was set at 813 days, the model had higher specificity, accuracy, and diagnostic efficiency.
UNASSIGNED: This study retrospectively collected 249 patients with 298 GGN lesions who were pathologically confirmed of having lung adenocarcinoma. The images were imported into the Siemens scientific research prototype software to outline the region of interest and extract the radiomics features. Logistic model A (a radiomics model to identify the infiltration of lung adenocarcinoma manifesting as GGNs) was established using features after the dimensionality reduction process. The receiver operating characteristic (ROC) curve of the model on training set and the verification set was drawn, and the area under the curve (AUC) was calculated. Second, a total of 112 lesions were selected from 298 lesions originating from CT images of at least two occasions, and the time between the first CT and the preoperative CT was defined as not less than 90 days. The mass doubling time (MDT) of all lesions was calculated. According to the different MDT diagnostic thresholds instability was predicted. Finally, their AUCs were calculated and compared.
UNASSIGNED: There were statistically significant differences in age and lesion location distribution between the \"noninvasive\" lesion group and the invasive lesion group (P<0.05), but there were no statistically significant differences in sex (P>0.05). Model A had an AUC of 0.89, sensitivity of 0.75, and specificity of 0.86 in the training set and an AUC of 0.87, sensitivity of 0.63, and specificity of 0.90 in the validation set. There was no significant difference statistically in MDT between \"noninvasive\" lesions and invasive lesions (P>0.05). The AUCs of radiomics models B1, B2 and B3 were 0.89, 0.80, and 0.81, respectively; the sensitivities were 0.71, 0.54, and 0.76, respectively; the specificities were 0.83, 0.77, and 0.60, respectively; and the accuracies were 0.78, 0.65, and 0.69, respectively.
UNASSIGNED: There were statistically significant differences in age and location of lesions between the \"noninvasive\" lesion group and the invasive lesion group. The radiomics model can predict the invasiveness of lung adenocarcinoma manifesting as GGNs. There was no significant difference in MDT between \"noninvasive\" lesions and invasive lesions. The radiomics model can predict the instability of lung adenocarcinoma manifesting as GGN. When the threshold of MDT was set at 813 days, the model had higher specificity, accuracy, and diagnostic efficiency.
摘要：
■磨玻璃结节（GGN）是肺腺癌在计算机断层扫描（CT）上最常见的表现。临床上,经穿刺活检等手段术前诊断GGN的成功率仍然较低。本研究的目的是利用影像组学分析方法探讨在CT图像上表现为GGN的肺腺癌的临床和影像组学特征。建立一个影像组学模型，并预测GGN型肺腺癌的病理组织分类和不稳定性。
■本研究回顾性收集了249例经病理证实为肺腺癌的298例GGN病变患者。将图像导入到西门子科学研究原型软件中，以勾勒出感兴趣的区域并提取影像组学特征。使用降维过程后的特征建立Logistic模型A（用于识别表现为GNs的肺腺癌浸润的放射组学模型）。绘制了模型在训练集和验证集上的受试者工作特性(ROC)曲线,并计算曲线下面积(AUC)。第二,从至少两次CT图像来源的298个病灶中，共选择112个病灶，首次CT与术前CT之间的时间定义为不少于90天。计算所有病灶的质量倍增时间(MDT)。根据不同的MDT诊断阈值预测不稳定性。最后,计算并比较了它们的AUC.
■无创性病变组与有创性病变组的年龄和病变部位分布差异有统计学意义(P<0.05),但性别差异无统计学意义(P>0.05)。模型A在训练集中具有0.89的AUC、0.75的灵敏度和0.86的特异性,并且在验证集中具有0.87的AUC、0.63的灵敏度和0.90的特异性。“非侵袭性”病变与侵袭性病变之间的MDT差异无统计学意义（P>0.05）。影像组学模型B1、B2和B3的AUC分别为0.89、0.80和0.81；敏感性分别为0.71、0.54和0.76；特异性分别为0.83、0.77和0.60；准确性分别为0.78、0.65和0.69。
■“非侵入性”病变组与侵入性病变组之间的年龄和病变位置有统计学上的显着差异。影像组学模型可以预测表现为GGN的肺腺癌的侵袭性。“非侵入性”病变和侵入性病变之间的MDT没有显着差异。影像组学模型可以预测表现为GGN的肺腺癌的不稳定性。当MDT的阈值设置为813天时，该模型具有较高的特异性，准确度,诊断效率。
■本研究回顾性收集了249例经病理证实为肺腺癌的298例GGN病变患者。将图像导入到西门子科学研究原型软件中，以勾勒出感兴趣的区域并提取影像组学特征。使用降维过程后的特征建立Logistic模型A（用于识别表现为GNs的肺腺癌浸润的放射组学模型）。绘制了模型在训练集和验证集上的受试者工作特性(ROC)曲线,并计算曲线下面积(AUC)。第二,从至少两次CT图像来源的298个病灶中，共选择112个病灶，首次CT与术前CT之间的时间定义为不少于90天。计算所有病灶的质量倍增时间(MDT)。根据不同的MDT诊断阈值预测不稳定性。最后,计算并比较了它们的AUC.
■无创性病变组与有创性病变组的年龄和病变部位分布差异有统计学意义(P<0.05),但性别差异无统计学意义(P>0.05)。模型A在训练集中具有0.89的AUC、0.75的灵敏度和0.86的特异性,并且在验证集中具有0.87的AUC、0.63的灵敏度和0.90的特异性。“非侵袭性”病变与侵袭性病变之间的MDT差异无统计学意义（P>0.05）。影像组学模型B1、B2和B3的AUC分别为0.89、0.80和0.81；敏感性分别为0.71、0.54和0.76；特异性分别为0.83、0.77和0.60；准确性分别为0.78、0.65和0.69。
■“非侵入性”病变组与侵入性病变组之间的年龄和病变位置有统计学上的显着差异。影像组学模型可以预测表现为GGN的肺腺癌的侵袭性。“非侵入性”病变和侵入性病变之间的MDT没有显着差异。影像组学模型可以预测表现为GGN的肺腺癌的不稳定性。当MDT的阈值设置为813天时，该模型具有较高的特异性，准确度,诊断效率。
