Li Wei, Xu Chao, Ye Zhaoxiang
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Front Oncol. 2021 Nov 17;11:758062. doi: 10.3389/fonc.2021.758062. eCollection 2021.
Pancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images.
Totally 48 patients but 51 lesions with a pathological tumor grade were subdivided into low grade (G1) group and intermediate grade (G2) group. The ROI was manually segmented slice by slice in 3D-T1 weighted sequence with and without enhancement. Statistical differences of radiomic features between G1 and G2 groups were analyzed using the independent sample -test. Logistic regression analysis was conducted to find better predictors in distinguishing G1 and G2 groups. Finally, receiver operating characteristic (ROC) was constructed to assess diagnostic performance of each model.
No significant difference between G1 and G2 groups ( > 0.05) in non-enhanced 3D-T1 images was found. Significant differences in the arterial phase analysis between the G1 and the G2 groups appeared as follows: the maximum intensity feature ( = 0.021); the range feature ( = 0.039). Multiple logistic regression analysis based on univariable model showed the maximum intensity feature (=0.023, OR = 0.621, 95% CI: 0.433-0.858) was an independent predictor of G1 compared with G2 group, and the area under the curve (AUC) was 0.695.
The maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.
胰腺神经内分泌肿瘤（PNETs）的分级对于PNETs的治疗策略非常重要。本研究旨在寻找用于预测MR图像中PNETs分级的定量影像组学特征。
共有48例患者的51个病灶有病理肿瘤分级，被分为低级别（G1）组和中级别（G2）组。在有增强和无增强的三维T1加权序列中逐片手动分割感兴趣区（ROI）。采用独立样本t检验分析G1组和G2组之间影像组学特征的统计学差异。进行逻辑回归分析以找出区分G1组和G2组的更好预测指标。最后，构建受试者操作特征（ROC）曲线以评估每个模型的诊断性能。
在未增强的三维T1图像中，G1组和G2组之间未发现显著差异（P>0.05）。G1组和G2组在动脉期分析中的显著差异如下：最大强度特征（P = 0.021）；范围特征（P = 0.039）。基于单变量模型的多因素逻辑回归分析显示，与G2组相比，最大强度特征（P = 0.023，OR = 0.621，95%CI：0.433 - 0.858）是G1的独立预测指标，曲线下面积（AUC）为0.695。
MR图像中影像组学特征的最大强度特征有助于预测PNETs分级风险。
