Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Department of Ultrasonography, Peking University First Hospital, Xi Cheng District, 100034, Beijing, China.
Eur Radiol. 2024 Jan;34(1):90-102. doi: 10.1007/s00330-023-09957-7.
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.
利用 MRI 上的非对比序列探讨基于影像组学特征预测无功能胰腺神经内分泌肿瘤（NF-PNET）患者组织学分级的潜力。
回顾性分析了在 5 个中心接受 MRI 检查的 228 例 NF-PNET 患者的数据。中心 1（n=115）的数据构成训练队列，中心 2-5（n=113）的数据构成检验队列。从 T2 加权图像和表观扩散系数中提取影像组学特征。应用最小绝对值收缩和选择算子（LASSO）选择最重要的特征并建立影像组学特征模型。通过接受者操作特征曲线（ROC）下面积（AUC）评估模型。
使用肿瘤边界、增强均匀性和血管侵犯构建了放射学模型，将 NF-PNET 患者分为 1 级和 2/3 级，在训练组和检验组的 AUC 分别为 0.884 和 0.684。建立了一个包含 4 个特征的影像组学模型，在训练组和检验组的 AUC 分别为 0.941 和 0.871。融合了影像组学特征和放射学特征的融合模型在训练集（AUC=0.956）和检验集（AUC=0.864）中均表现出良好的性能。
该研究开发的模型将影像组学特征与放射学特征相结合，可作为 NF-PNET 术前预测分级的一种非侵入性、可靠且准确的工具。
本研究表明，基于非对比 MR 序列的融合模型可用于预测 NF-PNET 患者的组织学分级。影像组学模型可能是 NF-PNET 的一种新的有效生物学标志物。
在预测无功能胰腺神经内分泌肿瘤（NF-PNET）1 级和 2/3 级方面，影像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的模型。该模型在四个外部测试队列中的良好表现表明，预测 NF-PNET 分级的影像组学模型和融合模型具有稳健性和可靠性，这意味着这两个模型可在临床环境中使用，并有助于外科医生进行风险分层决策。影像组学特征是从非对比 T2 加权成像（T2WI）和弥散加权成像（DWI）序列中选择的，这意味着在对 NF-PNET 进行分级时不需要使用造影剂。
