DataSheet_1_Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR.pdf

BackgroundPancreatic 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.Materials and MethodsTotally 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 t-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.ResultsNo significant difference between G1 and G2 groups (P > 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 (P = 0.021); the range feature (P = 0.039). Multiple logistic regression analysis based on univariable model showed the maximum intensity feature (P=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.ConclusionsThe maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.

背景：胰腺神经内分泌肿瘤（PNETs）的分级对于PNETs的治疗策略至关重要。本研究旨在从MR图像中寻找定量影像组学特征，以预测PNETs的分级。材料与方法：总共纳入48名患者，但病理肿瘤分级为51个病灶，分为低级（G1）组和中级（G2）组。在3D-T1加权序列的增强与否中，ROI均通过手动逐层分割。利用独立样本t检验分析G1组和G2组之间影像组学特征的统计学差异。通过逻辑回归分析，寻找区分G1组和G2组的最佳预测因子。最后，构建受试者工作特征（ROC）曲线以评估各模型的诊断性能。结果：在非增强的3D-T1图像中，G1组和G2组之间未发现显著差异（P > 0.05）。在动脉相分析中，G1组和G2组之间存在显著差异：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元逻辑回归分析显示，最大强度特征（P=0.023，OR = 0.621，95% CI: 0.433–0.858）是G1组相对于G2组的独立预测因子，曲线下面积（AUC）为0.695。结论：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。