Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, Shandong, China.
United Imaging Intelligence (Beijing) Co., Ltd., Beijing, 100094, China.
Eur Radiol. 2023 Mar;33(3):1873-1883. doi: 10.1007/s00330-022-09182-8. Epub 2022 Oct 20.
To investigate the effectiveness of CT-based radiomics nomograms in differentiating adrenal lipid-poor benign lesions and metastases in a cancer population.
This retrospective study enrolled 178 patients with cancer history from three medical centres categorised as those with adrenal lipid-poor benign lesions or metastases. Patients were divided into training, validation, and external testing cohorts. Radiomics features were extracted from triphasic CT images (unenhanced, arterial, and venous) to establish three single-phase models and one triphasic radiomics model using logistic regression. Unenhanced and triphasic nomograms were established by incorporating significant clinico-radiological factors and radscores. The models were evaluated by the receiver operating characteristic curve, Delong's test, calibration curve, and decision curve.
Lesion side, diameter, and enhancement ratio resulted as independent factors and were selected into nomograms. The areas under the curves (AUCs) of unenhanced and triphasic radiomics models in validation (0.878, 0.914, p = 0.381) and external testing cohorts (0.900, 0.893, p = 0.882) were similar and higher than arterial and venous models (validation: 0.842, 0.765; testing: 0.814, 0.806). Unenhanced and triphasic nomograms yielded similar AUCs in validation (0.903, 0.906, p = 0.955) and testing cohorts (0.928, 0.946, p = 0.528). The calibration curves showed good agreement and decision curves indicated satisfactory clinical benefits.
Unenhanced and triphasic CT-based radiomics nomograms resulted as a useful tool to differentiate adrenal lipid-poor benign lesions from metastases in a cancer population. They exhibited similar predictive efficacies, indicating that enhanced examinations could be avoided in special populations.
• All four radiomics models and two nomograms using triphasic CT images exhibited favourable performances in three cohorts to characterise the cancer population's adrenal benign lesions and metastases. • Unenhanced and triphasic radiomics models had similar predictive performances, outperforming arterial and venous models. • Unenhanced and triphasic nomograms also exhibited similar efficacies and good clinical benefits, indicating that contrast-enhanced examinations could be avoided when identifying adrenal benign lesions and metastases.
探究 CT 影像组学列线图在癌症患者人群中鉴别肾上腺乏脂性良性病变与转移瘤的有效性。
本回顾性研究纳入了来自三家医疗中心的 178 例有癌症病史的患者,分为肾上腺乏脂性良性病变组和转移瘤组。将患者分为训练集、验证集和外部测试集。使用逻辑回归从三期 CT 图像(平扫、动脉期和静脉期)中提取影像组学特征,建立了三个单期模型和一个三期联合模型。通过纳入有意义的临床-影像学因素和 radscore 建立了平扫和三期列线图。通过受试者工作特征曲线、DeLong 检验、校准曲线和决策曲线评估模型。
病变侧别、直径和强化比值是独立因素,被选入列线图。验证集和外部测试集的平扫和三期影像组学模型的曲线下面积(AUC)相似,且高于动脉期和静脉期模型(验证集:0.878、0.914,p=0.381;外部测试集:0.900、0.893,p=0.882)。平扫和三期列线图在验证集(0.903、0.906,p=0.955)和外部测试集(0.928、0.946,p=0.528)的 AUC 也相似。校准曲线显示出良好的一致性,决策曲线表明具有满意的临床获益。
基于 CT 的平扫和三期影像组学列线图是一种有用的工具,可用于鉴别癌症患者人群中的肾上腺乏脂性良性病变与转移瘤。它们具有相似的预测效能,表明在特殊人群中可以避免增强检查。
四组影像组学模型和两组基于三期 CT 图像的列线图在三组研究中表现出良好的性能,可用于区分癌症患者人群中的肾上腺良性病变和转移瘤。
平扫和三期影像组学模型具有相似的预测效能,优于动脉期和静脉期模型。
平扫和三期列线图也具有相似的效能和良好的临床获益,表明在鉴别肾上腺良性病变和转移瘤时可以避免增强检查。