Endocrinology, Department of Clinical and Molecular Medicine, Sant'Andrea University Hospital, Sapienza University of Rome, Rome, Italy.
Department of Surgical and Medical Sciences and Translational Medicine, Sant'Andrea University Hospital, Sapienza University of Rome, Rome, Italy.
Front Endocrinol (Lausanne). 2022 Jun 17;13:873189. doi: 10.3389/fendo.2022.873189. eCollection 2022.
New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >-275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs.
新的放射影像学技术利用影像学的定量变量，能够识别出假设的病理组织。我们在本中心对一系列 72 例偶然发现的肾上腺瘤（AIs）进行了研究，这些病例根据实验室结果分为功能性和非功能性。使用特定的软件（Mazda），对每个 AI 在初步的非对比阶段进行研究，在每个病变内的感兴趣区域周围提取 314 个特征。得出特征的平均值和标准差，并对两组之间的平均值差异进行统计学分析。接收者操作特征（ROC）曲线用于确定每个变量的最佳截止值，并使用向后和逐步选择的多变量逻辑回归构建预测模型。构建了一个 11 变量的预测模型，并使用 ROC 曲线来区分功能性 AI 可能性高的患者。使用阈值>-275.147，我们在诊断功能性 AI 时获得了 93.75%的敏感性和 100%的特异性。基于这些结果，计算机断层扫描（CT）纹理分析似乎是诊断 AIs 的有前途的工具。
