OBJECTIVES To evaluate the diagnostic accuracy of radiomics method and frozen sections (FS) for the pathological classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT). MATERIALS AND METHODS A dataset of 831 peripheral lung adenocarcinoma manifesting as GGNs in CT were divided into two cohorts: training cohort (n = 581) and validation cohort (n = 250). Combined with clinical features, the radiomics classifier was trained and validated to distinguish the pathological classification of these nodules. FS diagnoses in the validation cohort were collected. Diagnostic performance of the FS and radiomics methods was compared in the validation cohort. The predictive factors for the misdiagnosis of FS were determined via univariate and multivariate analyses. RESULTS The accuracy of radiomics method in the training and validation cohorts was 72.5 % and 68.8 % respectively. The overall accuracy of FS in the validation cohort was 70.0 %. The concordance rate between FS and final pathology when FS had a different diagnosis from radiomics classifier was significantly lower than when FS had the same diagnosis as radiomics classifier (46 vs. 87 %, P < 0.001). Univariate and Multivariate analyses showed that different diagnosis between FS and radiomics classifier was the independent predictive factor for the misdiagnosis of FS (OR: 7.46; 95%CI: 4.00-13.91; P < 0.001). CONCLUSIONS Radiomics classifier predictions may be a reliable reference for the classification of peripheral lung adenocarcinoma manifesting as GGNs when FS cannot provide a timely diagnosis. Intraoperative diagnoses of the cases where FS had a different diagnosis from radiomics method should be considered cautiously due to the higher misdiagnosis rate.

中文翻译：

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在预测表现为毛玻璃结节的周围型肺腺癌的最终分类时，应考虑结合使用放射性组学方法和冷冻切片。

目的评估放射线学方法和冰冻切片（FS）对在计算机断层扫描（CT）中表现为毛玻璃结节（GGN）的周围型肺腺癌的病理学分类的诊断准确性。材料与方法将831例在CT中表现为GGN的周围型肺腺癌的数据集分为两个队列：训练队列（n = 581）和验证队列（n = 250）。结合临床特征，对放射学分类器进行了培训和验证，以区分这些结节的病理学分类。收集了验证队列中的FS诊断。在验证队列中比较了FS和放射组学方法的诊断性能。FS误诊的预测因素是通过单因素和多因素分析确定的。结果放射组学方法在训练和验证队列中的准确性分别为72.5％和68.8％。验证队列中FS的总体准确性为70.0％。当FS与放射线分类器不同的诊断时，FS与最终病理之间的一致性率显着低于FS与放射线分类器相同的诊断率（46比87％，P <0.001）。单因素和多因素分析表明，FS和放射性组分类器之间的不同诊断是FS误诊的独立预测因素（OR：7.46； 95％CI：4.00-13.91； P <0.001）。结论当FS无法提供及时诊断时，Radimics分类器预测可能是对表现为GGN的周围肺腺癌进行分类的可靠参考。