Xie Yu, Abaydulla Elyar, Zhang Song, Liu Haobai, Hang Hexing, Li Qi, Qiu Yudong, Cheng Hao
Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Abdom Radiol (NY). 2025 Mar 19. doi: 10.1007/s00261-025-04865-4.
To establish a preoperative prediction model for pathological grade of PanNETs based on computed tomography (CT), magnetic resonance imaging (MRI) and endoscopic ultrasonography (EUS).
Clinical data of 58 patients with pathologically confirmed PanNETs were included in this retrospectively study and they were divided into grade 1 and grade 2/3. CT, MRI and EUS images were collected within one week before surgery. A clinical predictive model based on the independent clinical risk factors and significant radiological features was established. The area under receiver operating characteristic curve (AUC) was performed to assess the model.
Gender, pancreatic duct dilatation (PDD) and portal enhancement ratio (PER) were the independent predictors for PanNETs grading (P < 0.05). PanNETs grade 1 and grade 2/3 had statistical difference in elastography score (P = 0.001). The combination of gender, PDD and PER had better predictive efficiency than each of these three predictors alone, with a high AUC of 0.925. The elastography score also achieved an AUC of 0.838.
We proposed a comprehensive model based on preoperative CT, MRI and EUS to predict grade 1 and grade 2/3 of PanNETs and better informs clinicians on individualized diagnosis and treatment of patients with PanNETs.
基于计算机断层扫描（CT）、磁共振成像（MRI）和内镜超声（EUS）建立胰腺神经内分泌肿瘤（PanNETs）病理分级的术前预测模型。
本回顾性研究纳入58例经病理确诊的PanNETs患者，将其分为1级和2/3级。在手术前一周内收集CT、MRI和EUS图像。基于独立的临床危险因素和显著的影像学特征建立临床预测模型。采用受试者工作特征曲线下面积（AUC）评估该模型。
性别、胰管扩张（PDD）和门静脉强化率（PER）是PanNETs分级的独立预测因素（P < 0.05）。1级和2/3级PanNETs在弹性成像评分上有统计学差异（P = 0.001）。性别、PDD和PER联合使用的预测效率优于这三个预测因素单独使用，AUC高达0.925。弹性成像评分的AUC也达到了0.838。
我们提出了一种基于术前CT、MRI和EUS的综合模型来预测PanNETs的1级和2/3级，能更好地为临床医生提供PanNETs患者个体化诊断和治疗的信息。
