探索利用基于MRI的非对比序列的放射组学特征预测非功能性胰腺神经内分泌肿瘤(NF-PNET)患者的组织学分级的潜力。在5个中心进行MRI的228名NF-PNET患者进行了回顾性分析。中心1的数据(n = 115)构成了训练队列，中心2-5的数据(n = 113)构成了测试队列。从T2加权图像和表观扩散系数中提取了放射组学特征。应用最小绝对收缩和选择算子来选择最重要的特征并开发放射组学特征。采用受试者工作特征曲线下面积(AUC)评估模型。将肿瘤边界、增强均匀性和血管侵犯用于构建放射学模型，将NF-PNET患者分为1级和2/3级组，训练和测试组的AUC分别为0.884和0.684。构建了包含4个特征的放射组学模型，训练和测试队列的AUC分别为0.941和0.871。将放射组学特征和放射学特征结合的融合模型在训练队列(AUC = 0.956)和测试队列(AUC = 0.864)中表现良好。将放射组学特征与放射学特征相结合的开发模型可用作预测NF-PNETs的手术前分级的非侵入性、可靠且精确的工具。我们的研究揭示了基于非对比MRI序列的融合模型可用于术前预测组织学分级。放射组学模型可能是NF-PNETs的一种新的有效生物标记。放射组学模型和融合模型的诊断性能优于基于临床信息和放射学特征预测非功能性胰腺神经内分泌肿瘤(NF-PNETs) 1级和2/3级的模型。四个外部测试队列中模型的良好性能表明，用于预测NF-PNETs分级的放射组学模型和融合模型是稳健可靠的，表明这两个模型可以在临床环境中使用，并有助于外科医生的风险分层决策。放射组学特征是从非对比T2加权图像(T2WI)和弥散加权成像(DWI)序列中选择的，这意味着在对NF-PNETs进行分级时不需要使用对比剂。©2023。作者(们)。

To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI.Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2-5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models.Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively.The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs.Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs.The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.© 2023. The Author(s).