| 师佳佳,张艺凡,陈云锦,郝辉,俞富龙,高剑波,万娅敏.多参数光谱CT鉴别G2~3级胰腺神经内分泌瘤与胰腺神经内分泌癌[J].中国医学影像技术,2024,40(11):1720~1724 |
| 多参数光谱CT鉴别G2~3级胰腺神经内分泌瘤与胰腺神经内分泌癌 |
| Multi-parameter spectral CT for differentiating grade G2—3 pancreatic neuroendocrine tumor and pancreatic neuroendocrine carcinoma |
| 投稿时间：2024-01-16 修订日期：2024-08-16 |
| DOI：10.13929/j.issn.1003-3289.2024.11.018 |
| 中文关键词: 神经内分泌肿瘤 胰腺肿瘤 光谱分析 诊断,鉴别 |
| 英文关键词:neuroendocrine tumors pancreatic neoplasms spectrum analysis diagnosis, differential |
| 基金项目: |
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| 中文摘要: |
| 目的 观察多参数光谱CT鉴别G2~3级胰腺神经内分泌瘤(pNET)与胰腺神经内分泌癌(pNEC)的价值。方法 回顾性分析35例pNET (pNET组,含G2级25例、G3级10例)及17例pNEC (pNEC组)术前双层探测器光谱CT (DLCT),分别将单因素分析显示组间差异有统计学意义的常规CT及光谱CT参数纳入多因素logistic回归,筛选鉴别G2~3级pNET与pNEC的独立预测因子,构建常规CT模型及光谱CT模型,并基于二者构建联合模型;评估各模型鉴别G2~3级pNET与pNEC的效能。结果 常规CT病灶静脉期CT值(OR=0.939,P=0.025)及血管侵犯(OR=5.049,P=0.027)均为独立预测因子,以之构建的常规CT模型鉴别G2~3级pNET与pNEC的曲线下面积(AUC)为0.808。光谱CT所示静脉期标准碘密度(OR=0.603)及静脉期标准有效原子序数(OR=0.847)均为独立预测因子(P均＜0.05),以之构建的光谱CT模型的AUC为0.894,高于常规CT模型(Z=2.127,P=0.033)。联合模型的AUC为0.924,高于常规CT模型(Z=2.302,P=0.021)而与光谱CT模型差异无统计学意义(Z=0.827,P=0.408)。结论 多参数光谱CT能有效鉴别G2~3级pNET与pNEC。 |
| 英文摘要: |
| Objective To explore the value of multi-parameter spectral CT for differentiating grade G2－3 pancreatic neuroendocrine tumor (pNET) and pancreatic neuroendocrine carcinoma (pNEC). Methods Preoperative double-layer detector spectral CT (DLCT) data of 35 patients with pNET (pNET group, including 25 cases of G2 grade and 10 cases of G3 grade) and 17 patients with pNEC (pNEC group) were retrospectively analyzed. Conventional CT and spectral CT parameters were compared between groups, and those being significant different between groups according to univariate analysis were respectively incorporated into multivariate logistic regression to select the independent predictors for identifying grade G2－3 pNET and pNEC. Conventional CT model and spectral CT model were constructed, and the combined model was constructed based on the two. The efficacy of each model for distinguishing grade G2－3 pNET and pNEC was evaluated. Results CT values of lesions during venous phase (OR=0.939, P=0.025) and vascular invasion (OR=5.049, P=0.027) shown on conventional CT were both independent predictors, and conventional CT model was constructed, its area under the curve (AUC) for distinguishing grade G2－3 pNET and pNEC was 0.808. Normalized iodine concentration during venous phase (OR=0.603) and normalized effective atomic number during venous phase (OR=0.847) on spectral CT were both independent predictors (both P＜0.05), and spectral CT model was constructed. The AUC of spectral CT model was 0.894, higher than that of conventional CT model (Z=2.127, P=0.033). The AUC of combined model was 0.924, higher than that of conventional CT model (Z=2.302, P=0.021) but not significantly different with that of spectral CT model (Z=0.827, P=0.408). Conclusion Multi-parameter spectral CT could effectively differentiate grade G2—G3 grade pNET and pNEC. |
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