Purpose

To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2–3) in patients with pancreatic neuroendocrine tumor (PNET).

Materials and methods

Thirty-three PNETs (22 G1 and 11 G2–3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs).

Results

G2–G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively.

Conclusion

Texture analysis may aid in differentiating between G1 and G2–3 PNET.

中文翻译：

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肌理分析在增强对比计算机断层扫描和表观扩散系数图上对胰腺神经内分泌肿瘤分级的有用性。

目的

为了确定对比增强计算机断层扫描（CECT）和表观扩散系数（ADC）图的纹理分析是否可以预测胰腺神经内分泌肿瘤（PNET）患者的肿瘤分级（G1 vs G2-3）。

材料和方法

回顾性审查了33个PNET（22 G1和11 G2-3）。两名放射科医生分别从CECT和ADC图的动脉和门静脉相中提取了50个特征。通过接收者的工作特征曲线评估诊断性能，同时通过计算组内相关系数（ICC）确定观察者之间的一致性。

结果

G2-G3肿瘤明显大于G1。在单变量分析中，两个读者之间的十七个特征存在显着差异，ICC> 0.6；每个CECT期和ADC图的特征曲线下最大面积（AUC）为log-sigma 1.0联合能量= 0.855（动脉期），log-sigma 1.5峰度= 0.860（门静脉期）和log-sigma ADC映射的1.0相关性= 0.847。门静脉期的log-sigma 1.5峰度在CECT和ADC图中显示最大的AUC，其灵敏度，特异性和准确性分别为95.5％，72.7％和87.9％。

结论

纹理分析可能有助于区分G1和G2-3 PNET。