Yuan Jiaxin, Liu Jiawei, Wen Tingting, Wang Liqin, Peng Zhenpeng, Zhang Ning, Feng Shi-Ting, Yu Jinhui, Shi Siya, Luo Yanji
Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Insights Imaging. 2025 Jun 12;16(1):119. doi: 10.1186/s13244-025-02008-3.
To prospectively investigate the pancreatic stiffness (c) and fluidity (φ) of pancreatic neuroendocrine neoplasms (pNENs), measured using multifrequency magnetic resonance elastography (MRE), and evaluate their performance in predicting pNENs pathological grade.
This study included 96 untreated patients with pathologically confirmed pNENs who underwent multifrequency MRE within 2 weeks before surgery between September 2021 and November 2023. Independent predictors of pathological grade were identified using multivariate regression analysis, and predictive performance was assessed using receiver operating characteristic curves.
The study included 76 patients with low-grade pNENs (45 men; mean age: 48.7 ± 14.0 years; Grade 1: 34 patients, Grade 2: 42 patients) and 20 patients with high-grade pNENs (10 men; mean age: 54.4 ± 13.8 years; Grade 3: 15 patients, neuroendocrine carcinoma: 5 patients). The two radiologists showed substantial or near-perfect interobserver agreement in evaluating the quantitative parameters. The multivariate regression analysis identified c and relative enhancement in the portal venous phase (V) as independent predictors of pathological grade. The combined model (V + c) had the best predictive performance (area under the curve (AUC) = 0.930; sensitivity: 95.0%; specificity: 82.9%) and outperformed V (AUC = 0.806, p = 0.010), c (AUC = 0.847, p = 0.021), and φ (AUC = 0.709, p = 0.003) alone, as well as other clinical and conventional MRI parameters (all p < 0.05) in Delong's test.
Tumour stiffness quantified via multifrequency MRE improved the predictive performance for the pathological grade of pNENs when combined with conventional MRI parameters.
Tumour stiffness quantified using multifrequency magnetic resonance elastography provides a non-invasive, preoperative method for predicting the pathological grade of pancreatic neuroendocrine neoplasms. Predictive performance improves when combined with conventional MRI parameters, facilitating clinical decision-making and prognostic prediction.
Multifrequency magnetic resonance elastography (MRE) can indicate stiffness and fluidity of pancreatic neuroendocrine neoplasms (pNENs). Tumour stiffness combined with conventional MRI parameters can independently predict pNENs pathological grade. Multifrequency MRE can serve as a biomarker for the prediction of pNENs pathological grade.
前瞻性研究使用多频磁共振弹性成像（MRE）测量的胰腺神经内分泌肿瘤（pNENs）的胰腺硬度（c）和流动性（φ），并评估其在预测pNENs病理分级中的性能。
本研究纳入了96例未经治疗且病理确诊为pNENs的患者，这些患者在2021年9月至2023年11月期间手术前2周内接受了多频MRE检查。使用多因素回归分析确定病理分级的独立预测因素，并使用受试者工作特征曲线评估预测性能。
该研究包括76例低级别pNENs患者（45例男性；平均年龄：48.7±14.0岁；1级：34例患者，2级：42例患者）和20例高级别pNENs患者（10例男性；平均年龄：54.4±13.8岁；3级：15例患者，神经内分泌癌：5例患者）。两位放射科医生在评估定量参数时显示出实质性或近乎完美的观察者间一致性。多因素回归分析确定c和门静脉期相对强化（V）为病理分级的独立预测因素。联合模型（V+c）具有最佳预测性能（曲线下面积（AUC）=0.930；敏感性：95.0%；特异性：82.9%），在德龙检验中优于单独的V（AUC=0.806，p=0.010）、c（AUC=0.847，p=0.021）和φ（AUC=0.709，p=0.003），以及其他临床和传统MRI参数（所有p<0.05）。
通过多频MRE量化的肿瘤硬度与传统MRI参数联合时，可提高pNENs病理分级的预测性能。
使用多频磁共振弹性成像量化的肿瘤硬度为预测胰腺神经内分泌肿瘤的病理分级提供了一种非侵入性的术前方法。与传统MRI参数联合时预测性能提高，有助于临床决策和预后预测。
多频磁共振弹性成像（MRE）可显示胰腺神经内分泌肿瘤（pNENs）的硬度和流动性。肿瘤硬度与传统MRI参数联合可独立预测pNENs病理分级。多频MRE可作为预测pNENs病理分级的生物标志物。
