Department of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, No. 7, Chung-Shan South Road, Taipei, 10002, Taiwan.
Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan.
Sci Rep. 2022 May 25;12(1):8892. doi: 10.1038/s41598-022-12835-9.
We performed the present study to investigate the role of computed tomography (CT) radiomics in differentiating nonfunctional adenoma and aldosterone-producing adenoma (APA) and outcome prediction in patients with clinically suspected primary aldosteronism (PA). This study included 60 patients diagnosed with essential hypertension (EH) with nonfunctional adenoma on CT and 91 patients with unilateral surgically proven APA. Each whole nodule on unenhanced and venous phase CT images was segmented manually and randomly split into training and test sets at a ratio of 8:2. Radiomic models for nodule discrimination and outcome prediction of APA after adrenalectomy were established separately using the training set by least absolute shrinkage and selection operator (LASSO) logistic regression, and the performance was evaluated on test sets. The model can differentiate adrenal nodules in EH and PA with a sensitivity, specificity, and accuracy of 83.3%, 78.9% and 80.6% (AUC = 0.91 [0.72, 0.97]) in unenhanced CT and 81.2%, 100% and 87.5% (AUC = 0.98 [0.77, 1.00]) in venous phase CT, respectively. In the outcome after adrenalectomy, the models showed a favorable ability to predict biochemical success (Unenhanced/venous CT: AUC = 0.67 [0.52, 0.79]/0.62 [0.46, 0.76]) and clinical success (Unenhanced/venous CT: AUC = 0.59 [0.47, 0.70]/0.64 [0.51, 0.74]). The results showed that CT-based radiomic models hold promise to discriminate APA and nonfunctional adenoma when an adrenal incidentaloma was detected on CT images of hypertensive patients in clinical practice, while the role of radiomic analysis in outcome prediction after adrenalectomy needs further investigation.
我们进行了本项研究，旨在探讨 CT 放射组学在鉴别无功能腺瘤和醛固酮分泌性腺瘤（APA）中的作用，并预测临床上疑似原发性醛固酮增多症（PA）患者的结局。本研究纳入了 60 例 CT 诊断为无功能性腺瘤的原发性高血压（EH）患者和 91 例单侧经手术证实的 APA 患者。在未增强和静脉期 CT 图像上，手动对每个完整结节进行分割，并以 8:2 的比例随机分为训练集和测试集。使用训练集，通过最小绝对值收缩和选择算子（LASSO）逻辑回归分别为结节鉴别和 APA 术后结局预测建立放射组学模型，并在测试集上进行评估。该模型可将 EH 和 PA 中的肾上腺结节区分开来，在未增强 CT 中，其敏感性、特异性和准确性分别为 83.3%、78.9%和 80.6%（AUC=0.91[0.72,0.97]），在静脉期 CT 中，分别为 81.2%、100%和 87.5%（AUC=0.98[0.77,1.00]）。在肾上腺切除术后的结局方面，该模型显示出预测生化成功（未增强/静脉 CT：AUC=0.67[0.52,0.79]/0.62[0.46,0.76]）和临床成功（未增强/静脉 CT：AUC=0.59[0.47,0.70]/0.64[0.51,0.74]）的良好能力。结果表明，当在高血压患者的 CT 图像上发现肾上腺偶发瘤时，基于 CT 的放射组学模型有望区分 APA 和无功能腺瘤，而放射组学分析在肾上腺切除术后结局预测中的作用需要进一步研究。
