PDF(Pubmed)
Abstract：
UNASSIGNED: To evaluate the capability of dual-layer detector spectral CT (DLCT) quantitative parameters in conjunction with clinical variables to detect malignant lesions in cytologically indeterminate thyroid nodules (TNs).
UNASSIGNED: Data from 107 patients with cytologically indeterminate TNs who underwent DLCT scans were retrospectively reviewed and randomly divided into training and validation sets (7:3 ratio). DLCT quantitative parameters (iodine concentration (IC), NICP (IC nodule/IC thyroid parenchyma), NICA (IC nodule/IC ipsilateral carotid artery), attenuation on the slope of spectral HU curve and effective atomic number), along with clinical variables, were compared between benign and malignant cohorts through univariate analysis. Multivariable logistic regression analysis was employed to identify independent predictors which were used to construct the clinical model, DLCT model, and combined model. A nomogram was formulated based on optimal performing model, and its performance was assessed using receiver operating characteristic curve, calibration curve, and decision curve analysis. The nomogram was subsequently tested in the validation set.
UNASSIGNED: Independent predictors associated with malignant TNs with indeterminate cytology included NICP in the arterial phase, Hashimoto\'s Thyroiditis (HT), and BRAF V600E (all p < 0.05). The DLCT-clinical nomogram, incorporating the aforementioned variables, exhibited superior performance than the clinical model or DLCT model in both training set (AUC: 0.875 vs 0.792 vs 0.824) and validation set (AUC: 0.874 vs 0.792 vs 0.779). The DLCT-clinical nomogram demonstrated satisfactory calibration and clinical utility in both training set and validation set.
UNASSIGNED: The DLCT-clinical nomogram emerges as an effective tool to detect malignant lesions in cytologically indeterminate TNs.
UNASSIGNED: Data from 107 patients with cytologically indeterminate TNs who underwent DLCT scans were retrospectively reviewed and randomly divided into training and validation sets (7:3 ratio). DLCT quantitative parameters (iodine concentration (IC), NICP (IC nodule/IC thyroid parenchyma), NICA (IC nodule/IC ipsilateral carotid artery), attenuation on the slope of spectral HU curve and effective atomic number), along with clinical variables, were compared between benign and malignant cohorts through univariate analysis. Multivariable logistic regression analysis was employed to identify independent predictors which were used to construct the clinical model, DLCT model, and combined model. A nomogram was formulated based on optimal performing model, and its performance was assessed using receiver operating characteristic curve, calibration curve, and decision curve analysis. The nomogram was subsequently tested in the validation set.
UNASSIGNED: Independent predictors associated with malignant TNs with indeterminate cytology included NICP in the arterial phase, Hashimoto\'s Thyroiditis (HT), and BRAF V600E (all p < 0.05). The DLCT-clinical nomogram, incorporating the aforementioned variables, exhibited superior performance than the clinical model or DLCT model in both training set (AUC: 0.875 vs 0.792 vs 0.824) and validation set (AUC: 0.874 vs 0.792 vs 0.779). The DLCT-clinical nomogram demonstrated satisfactory calibration and clinical utility in both training set and validation set.
UNASSIGNED: The DLCT-clinical nomogram emerges as an effective tool to detect malignant lesions in cytologically indeterminate TNs.
摘要：
■评估双层探测器能谱CT（DLCT）定量参数结合临床变量在细胞学不确定的甲状腺结节（TNs）中检测恶性病变的能力。
■对接受DLCT扫描的107例细胞学不确定TNs患者的数据进行回顾性分析，并随机分为训练集和验证集（7:3比例）。DLCT定量参数(碘浓度(IC)，NICP（IC结节/IC甲状腺实质），NICA（IC结节/IC同侧颈动脉），光谱HU曲线斜率和有效原子序数的衰减)，以及临床变量，通过单因素分析比较良性和恶性队列。采用多变量logistic回归分析确定用于构建临床模型的独立预测因子,DLCT模型,和组合模型。基于最佳性能模型制定了列线图，并使用接收器工作特性曲线评估其性能，校正曲线,和决策曲线分析。随后在验证集中测试列线图。
■与细胞学不确定的恶性TNs相关的独立预测因子包括动脉期的NICP，桥本甲状腺炎(HT),和BRAFV600E（所有p<0.05）。DLCT临床列线图，结合上述变量，在训练集(AUC:0.875vs0.792vs0.824)和验证集(AUC:0.874vs0.792vs0.779)中,均表现优于临床模型或DLCT模型.DLCT临床列线图在训练集和验证集中均显示出令人满意的校准和临床实用性。
■DLCT临床列线图成为检测细胞学上不确定的TNs中恶性病变的有效工具。
■对接受DLCT扫描的107例细胞学不确定TNs患者的数据进行回顾性分析，并随机分为训练集和验证集（7:3比例）。DLCT定量参数(碘浓度(IC)，NICP（IC结节/IC甲状腺实质），NICA（IC结节/IC同侧颈动脉），光谱HU曲线斜率和有效原子序数的衰减)，以及临床变量，通过单因素分析比较良性和恶性队列。采用多变量logistic回归分析确定用于构建临床模型的独立预测因子,DLCT模型,和组合模型。基于最佳性能模型制定了列线图，并使用接收器工作特性曲线评估其性能，校正曲线,和决策曲线分析。随后在验证集中测试列线图。
■与细胞学不确定的恶性TNs相关的独立预测因子包括动脉期的NICP，桥本甲状腺炎(HT),和BRAFV600E（所有p<0.05）。DLCT临床列线图，结合上述变量，在训练集(AUC:0.875vs0.792vs0.824)和验证集(AUC:0.874vs0.792vs0.779)中,均表现优于临床模型或DLCT模型.DLCT临床列线图在训练集和验证集中均显示出令人满意的校准和临床实用性。
■DLCT临床列线图成为检测细胞学上不确定的TNs中恶性病变的有效工具。
