DataSheet_2_Development and Validation of a Clinical-Image Model for Quantitatively Distinguishing Uncertain Lipid-Poor Adrenal Adenomas From Nonadenomas.pdf

BackgroundThere remains a demand for a practical method of identifying lipid-poor adrenal lesions.
PurposeTo explore the predictive value of computed tomography (CT) features combined with demographic characteristics for lipid-poor adrenal adenomas and nonadenomas.
Materials and MethodsWe retrospectively recruited patients with lipid-poor adrenal lesions between January 2015 and August 2021 from two independent institutions as follows: Institution 1 for the training set and the internal validation set and Institution 2 for the external validation set. Two radiologists reviewed CT images for the three sets. We performed a least absolute shrinkage and selection operator (LASSO) algorithm to select variables; subsequently, multivariate analysis was used to develop a generalized linear model. The probability threshold of the model was set to 0.5 in the external validation set. We calculated the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the model and radiologists. The model was validated and tested in the internal validation and external validation sets; moreover, the accuracy between the model and both radiologists were compared using the McNemar test in the external validation set.
ResultsIn total, 253 patients (median age, 55 years [interquartile range, 47–64 years]; 135 men) with 121 lipid-poor adrenal adenomas and 132 nonadenomas were included in Institution 1, whereas another 55 patients were included in Institution 2. The multivariable analysis showed that age, male, lesion size, necrosis, unenhanced attenuation, and portal venous phase attenuation were independently associated with adrenal adenomas. The clinical-image model showed AUCs of 0.96 (95% confidence interval [CI]: 0.91, 0.98), 0.93 (95% CI: 0.84, 0.97), and 0.86 (95% CI: 0.74, 0.94) in the training set, internal validation set, and external validation set, respectively. In the external validation set, the model showed a significantly and non-significantly higher accuracy than reader 1 (84% vs. 65%, P = 0.031) and reader 2 (84% vs. 69%, P = 0.057), respectively.
ConclusionsOur clinical-image model displayed good utility in differentiating lipid-poor adrenal adenomas. Further, it showed better diagnostic ability than experienced radiologists in the external validation set.

研究背景：目前仍缺乏可用于识别乏脂性肾上腺病变的实用方法。
研究目的：旨在探索联合人口统计学特征与计算机断层扫描（CT）特征的模型，对乏脂性肾上腺腺瘤与非腺瘤的预测价值。
材料与方法：本研究于2015年1月至2021年8月期间，从两家独立医疗机构回顾性纳入乏脂性肾上腺病变患者，具体分组如下：机构1作为训练集与内部验证集的来源，机构2作为外部验证集的来源。由两名放射科医师对三个数据集的CT影像进行阅片。本研究采用最小绝对收缩和选择算子（LASSO）算法进行变量筛选，随后通过多变量分析构建广义线性模型。在外部验证集中，将模型的概率阈值设置为0.5。分别计算模型与两名放射科医师的灵敏度、特异度、准确率以及受试者工作特征曲线下面积（AUC）。模型在内部验证集与外部验证集中均进行了验证与测试；此外，在外部验证集中采用McNemar检验比较模型与两名放射科医师的诊断准确率。
研究结果：机构1共纳入253例患者（中位年龄55岁，四分位间距47~64岁；男性135例），共计121例乏脂性肾上腺腺瘤与132例非腺瘤病变；机构2共纳入55例患者。多变量分析结果显示，年龄、男性性别、病变大小、坏死情况、平扫CT值及门静脉期CT值均为肾上腺腺瘤的独立影响因素。本研究构建的临床影像模型在训练集、内部验证集与外部验证集中的受试者工作特征曲线下面积分别为0.96（95%置信区间[CI]：0.91, 0.98）、0.93（95%置信区间[CI]：0.84, 0.97）以及0.86（95%置信区间[CI]：0.74, 0.94）。在外部验证集中，该模型的诊断准确率显著高于阅片者1（84% vs. 65%，P=0.031），同时高于阅片者2但差异无统计学意义（84% vs. 69%，P=0.057）。
研究结论：本研究构建的临床影像模型在区分乏脂性肾上腺腺瘤方面具有良好的应用价值；且在外部验证集中，其诊断性能优于资深放射科医师。