查看更多>>摘要：目的 探讨双层探测器光谱CT多参数分析对甲状腺乳头状癌（PTC）初始复发风险的预测价值。 方法 该研究为横断面研究。回顾性收集2021年11月至2022年10月于南京医科大学第一附属医院经病理证实的102例PTC患者，男25例、女77例，年龄（42±13）岁。对PTC患者进行初始复发风险评估，分为低危组（75例）和中高危组（27例）。纳入分析的临床特征包括年龄、性别、体重指数、是否合并结节性甲状腺肿、桥本氏甲状腺炎及术前甲状腺功能。形态学特征包括病灶大小、位置、形状、纵横比、与包膜接触程度、钙化和囊变。测量光谱CT动静脉期病灶定量参数，包括碘浓度（IC）、标准化碘浓度（NIC）、有效原子序数（Zeff）、标准化有效原子序数（NZeff）、电子云密度（ED）、40~200 keV（间隔30 keV）单能图像CT值和能谱曲线斜率（λHU）。分别采用独立样本t检验、Mann-Whitney U检验或χ2检验比较2组间各临床、形态学特征及光谱CT定量参数的差异。利用二元logistic回归分别构建基于临床和形态学特征的临床模型、光谱CT定量参数模型及两者联合模型，并采用受试者操作特征曲线评估模型预测PTC患者初始复发风险效能，曲线下面积（AUC）的比较采用DeLong检验。 结果 低危组与中高危组患者间性别、病灶长径、病灶短径及钙化差异有统计学意义（P<0.05）。中高危组病灶的动脉期IC、动脉期Zeff、动脉期λHU、动脉期CT值40 keV、静脉期NIC、静脉期NZeff均低于低危组，差异有统计学意义（P<0.05）。经logistic回归分析，临床模型纳入了性别（OR为2.895，95%CI 1.047~8.002，P=0.040）和病灶长径（OR为1.142，95%CI 1.042~1.251，P=0.004），预测PTC患者初始复发风险的AUC为0.720，灵敏度为63.0%，特异度为78.7%。光谱CT定量参数模型中纳入了动脉期IC（OR为0.580，95%CI 0.370~0.908，P=0.017）、静脉期NIC（OR为0.077，95%CI 0.011~0.536，P=0.010）、静脉期NZeff（OR为0.002，95%CI 0.001~0.103，P=0.009），AUC为0.774，灵敏度为71.9%，特异度为70.0%。联合模型的AUC为0.857，灵敏度为74.1%，特异度为88.0%，优于临床模型（Z=2.92，P=0.004）和光谱CT定量参数模型（Z=2.07，P=0.046）。 结论 基于病灶的光谱CT多参数分析能有效预测PTC患者的初始复发风险，且联合临床和病灶形态学特征能进一步提高预测效能。 Objective To investigate the value of multi-parametric analysis based on dual-layer detector spectral CT (DLCT) in predicting the initial recurrence risk for papillary thyroid carcinoma (PTC). Methods From November 2021 to October 2022, 102 PTC patients confirmed by pathology were retrospectively collected at the First Affiliated Hospital of Nanjing Medical University in this cross-sectional study. There were 25 males and 77 females, with an age of (42±13) years old. The initial recurrence risk assessment for PTC patients was categorized into a low-risk group (75 cases) and an intermediate-high-risk group (27 cases). Clinical data, including age, gender, body mass index, history of nodular goiter, history of Hashimoto thyroiditis, and preoperative thyroid function, were collected. Tumor morphological features, including size, location, shape, aspect ratio, the degree of thyroid capsule contact, calcification, and cystic change, were evaluated. Quantitative DLCT parameters, including iodine concentration (IC), standardized iodine concentration (NIC), effective atomic number (Zeff), standardized effective atomic number (NZeff), electronic density (ED), CT values under different energy levels (40-200 keV, 30 keV intervals) and slope of energy spectrum curve (λHU) both in the arterial and venous phase were measured. The differences in clinical, morphological features, and spectral CT quantitative parameters between the two groups were compared using independent sample ttest, Mann-Whitney U test, or χ2 test. Multivariate logistic regression analyses were used to construct three models based on clinical and morphological features, quantitative DLCT parameters and their combination, respectively. The receiver operating characteristic curve was used to evaluate the predictive performance of these models for the initial recurrence risk of PTC patients, and the area under the curve (AUC) was compared using the DeLong test. Results Significant differences were found in gender, lesion long diameter, lesion short diameter and calcification between the low-risk group and intermediate-high-risk groups (P<0.05). The arterial phase IC, arterial phase Zeff, arterial phase λHU, arterial phase CT40 keV, venous phase NIC and venous phase NZeff in intermediate-high-risk group were significantly lower than those in the low-risk group (P<0.05). The logistic regression analysis revealed that the clinical model included gender (OR=2.895, 95%CI 1.047-8.002, P=0.040) and lesion long diameter (OR=1.142, 95%CI 1.042-1.251, P=0.004), with an AUC of 0.720, sensitivity of 63.0%, and specificity of 78.7% in predicting the initial recurrence risk of PTC patients. The DLCT quantitative parameter model included arterial phase IC (OR=0.580, 95%CI 0.370-0.908, P=0.017), venous phase NIC (OR=0.077, 95%CI 0.011-0.536, P=0.010), and venous phase NZeff (OR=0.002, 95%CI 0.001-0.103, P=0.009), with an AUC of 0.774, sensitivity of 71.9%, and specificity of 70.0%. The AUC of the combined model was 0.857, with a sensitivity of 74.1%, and specificity of 88.0%, outperforming the clinical model (Z=2.92, P=0.004) and the DLCT quantitative parameter model (Z=2.07, P=0.046). Conclusion Multi-parametric analysis based on DLCT can help predict the initial recurrence risk for PTC, and combining it with clinical and morphological features, the predictive accuracy can be improved.
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