Division of General Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 9, 8036 Graz, Austria.
Diagnostic and Research Center for Molecular BioMedicine, Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria.
Eur J Radiol. 2023 Aug;165:110931. doi: 10.1016/j.ejrad.2023.110931. Epub 2023 Jun 20.
To investigate whether CT texture analysis allows differentiation between adenocarcinomas, squamous cell carcinomas, carcinoids, small cell lung cancers and organizing pneumonia and between carcinomas and neuroendocrine tumors.
This retrospective study included patients 133 patients (30 patients with organizing pneumonia, 30 patients with adenocarcinoma, 30 patients with squamous cell carcinoma, 23 patients with small cell lung cancer, 20 patients with carcinoid), who underwent CT-guided biopsy of the lung and had a corresponding histopathologic diagnosis. Pulmonary lesions were segmented in consensus by two radiologists with and without a threshold of -50HU in three dimensions. Groupwise comparisons were performed to assess for differences between all five above-listed entities and between carcinomas and neuroendocrine tumors.
Pairwise comparisons of the five entities revealed 53 statistically significant texture features when using no HU-threshold and 6 statistically significant features with a threshold of -50HU. The largest AUC (0.818 [95%CI 0.706-0.930]) was found for the feature wavelet-HHH_glszm_SmallAreaEmphasis for discrimination of carcinoid from the other entities when using no HU-threshold. In differentiating neuroendocrine tumors from carcinomas, 173 parameters proved statistically significant when using no HU threshold versus 52 parameters when using a -50HU-threshold. The largest AUC (0.810 [95%CI 0.728-0,893]) was found for the parameter original_glcm_Correlation for discrimination of neuroendocrine tumors from carcinomas when using no HU-threshold.
CT texture analysis revealed features that differed significantly between malignant pulmonary lesions and organizing pneumonia and between carcinomas and neuroendocrine tumors of the lung. Applying a HU-threshold for segmentation substantially influenced the results of texture analysis.
探讨 CT 纹理分析能否区分腺癌、鳞状细胞癌、类癌、小细胞肺癌和机化性肺炎,以及区分癌和神经内分泌肿瘤。
本回顾性研究纳入了 133 名患者(30 名机化性肺炎患者、30 名腺癌患者、30 名鳞状细胞癌患者、23 名小细胞肺癌患者、20 名类癌患者),这些患者均接受了 CT 引导下的肺部活检,并获得了相应的组织病理学诊断。两位放射科医生在三维无阈值和-50HU 阈值下对肺病变进行了共识分割。进行组间比较,以评估上述所有五种实体之间以及癌和神经内分泌肿瘤之间的差异。
在不使用 HU 阈值的情况下,五种实体之间的成对比较显示出 53 个具有统计学意义的纹理特征,而使用 -50HU 阈值则显示出 6 个具有统计学意义的特征。在不使用 HU 阈值的情况下,区分类癌与其他实体的最佳 AUC(0.818 [95%CI 0.706-0.930])是小波-HHH_glszm_SmallAreaEmphasis 特征。在区分神经内分泌肿瘤和癌时,不使用 HU 阈值时,有 173 个参数具有统计学意义,而使用 -50HU 阈值时,有 52 个参数具有统计学意义。在不使用 HU 阈值的情况下,区分神经内分泌肿瘤和癌的最佳 AUC(0.810 [95%CI 0.728-0.893])是原始_glcm_Correlation 特征。
CT 纹理分析显示出恶性肺病变与机化性肺炎之间以及癌和肺神经内分泌肿瘤之间存在显著差异的特征。应用 HU 阈值进行分割会对纹理分析的结果产生重大影响。