He Keng, Zhang Zhao-Tao, Wang Zhen-Hua, Wang Yu, Wang Yi-Xi, Zhang Hong-Zhou, Dong Yi-Fei, Xiao Xin-Lan
Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Front Oncol. 2021 Jul 9;11:634879. doi: 10.3389/fonc.2021.634879. eCollection 2021.
To develop and validate a clinical-radiomic nomogram for the preoperative prediction of the aldosterone-producing adenoma (APA) risk in patients with unilateral adrenal adenoma.
Ninety consecutive primary aldosteronism (PA) patients with unilateral adrenal adenoma who underwent adrenal venous sampling (AVS) were randomly separated into training (n = 62) and validation cohorts (n = 28) (7:3 ratio) by a computer algorithm. Data were collected from October 2017 to June 2020. The prediction model was developed in the training cohort. Radiomic features were extracted from unenhanced computed tomography (CT) images of unilateral adrenal adenoma. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensions, select features, and establish a radiomic signature. Multivariable logistic regression analysis was used for the predictive model development, the radiomic signature and clinical risk factors integration, and the model was displayed as a clinical-radiomic nomogram. The nomogram performance was evaluated by its calibration, discrimination, and clinical practicability. Internal validation was performed.
Six potential predictors were selected from 358 texture features by using the LASSO regression model. These features were included in the Radscore. The predictors included in the individualized prediction nomogram were the Radscore, age, sex, serum potassium level, and aldosterone-to-renin ratio (ARR). The model showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.900 [95% confidence interval (CI), 0.807 to 0.993], and good calibration. The nomogram still showed good discrimination [AUC, 0.912 (95% CI, 0.761 to 1.000)] and good calibration in the validation cohort. Decision curve analysis presented that the nomogram was useful in clinical practice.
A clinical-radiomic nomogram was constructed by integrating a radiomic signature and clinical factors. The nomogram facilitated accurate prediction of the probability of APA in patients with unilateral adrenal nodules and could be helpful for clinical decision making.
开发并验证一种临床-影像组学列线图，用于术前预测单侧肾上腺腺瘤患者产生醛固酮腺瘤（APA）的风险。
90例连续接受肾上腺静脉采血（AVS）的单侧肾上腺腺瘤原发性醛固酮增多症（PA）患者，通过计算机算法随机分为训练组（n = 62）和验证组（n = 28）（比例为7:3）。数据收集时间为2017年10月至2020年6月。在训练组中开发预测模型。从单侧肾上腺腺瘤的平扫计算机断层扫描（CT）图像中提取影像组学特征。使用最小绝对收缩和选择算子（LASSO）回归模型进行数据降维、特征选择并建立影像组学特征。多变量逻辑回归分析用于预测模型开发、影像组学特征与临床危险因素整合，该模型以临床-影像组学列线图形式展示。通过校准、鉴别能力和临床实用性评估列线图性能。进行内部验证。
使用LASSO回归模型从358个纹理特征中选择了6个潜在预测因子。这些特征被纳入Radscore。个体化预测列线图中的预测因子包括Radscore、年龄、性别、血清钾水平和醛固酮/肾素比值（ARR）。该模型显示出良好的鉴别能力，受试者操作特征曲线（AUC）下面积为0.900 [95%置信区间（CI），0.807至0.993]，且校准良好。在验证组中，列线图仍显示出良好的鉴别能力[AUC，0.912（95%CI，0.761至1.000）]和良好的校准。决策曲线分析表明该列线图在临床实践中有用。
通过整合影像组学特征和临床因素构建了临床-影像组学列线图。该列线图有助于准确预测单侧肾上腺结节患者发生APA的概率，对临床决策有帮助。
