本研究的目的是使用计算机断层扫描（CT）数据，开发放射组学特征的签名，以预测手术前非功能性胰腺神经内分泌肿瘤（NF-PNETs）的级别。在2010年至2019年期间，对行NF-PNETs切除手术的患者进行了回顾性研究。从胰腺CT检查的动脉期和静脉期提取了2436个放射组学特征。与手术标本中观察到的最终病理分级相关的放射组学特征经过联合互信息最大化进行层次特征选择和放射组学签名的开发。优登指数用于确定确定肿瘤分级的最佳截断值。训练和内部验证了随机森林预测模型。将该工具在预测肿瘤分级方面的性能与EUS-FNA采样作为参考标准进行了比较。共纳入270例患者，使用开发队列（n = 201）开发了基于10个选择特征的融合放射组学签名。共有149名男性和121名女性，平均年龄59.4±12.3（标准偏差）岁（范围：23.3-85.0岁）。在一个包含新的69例患者的内部验证中，观察到了较强的区域曲线下面积（AUC）为0.80（95%置信区间[CI]：0.71-0.90），相应的敏感性和特异性分别为87.5%（95% CI：79.7-95.3）和73.3%（95% CI：62.9-83.8）。在研究人群中，共有143例患者（52.9%）进行了EUS-FNA。26名患者的活检结果为非诊断性（18.2%），42名患者由于样本不足而无法分级（29.4%）。在对75例进行分级的患者中（52.4%），与EUS-FNA相比，放射组学签名的AUC无差异（0.69vs.0.67；P=0.723），然而观察到更高的敏感性（即准确识别G2/3病变的能力）（80.8%vs.42.3%；P<0.001）。使用提出的放射组学签名对PNET患者的肿瘤分级进行非侵入性评估显示出高准确度。前瞻性验证和优化可以克服在PNET患者的肿瘤分级评估中常见的诊断不确定性，并促进临床决策。版权所有 © 2023. Elsevier Masson SAS出版。

The purpose of this study was to develop a radiomics-signature using computed tomography (CT) data for the preoperative prediction of grade of nonfunctional pancreatic neuroendocrine tumors (NF-PNETs).A retrospective study was performed on patients undergoing resection for NF-PNETs between 2010 and 2019. A total of 2436 radiomic features were extracted from arterial and venous phases of pancreas-protocol CT examinations. Radiomic features that were associated with final pathologic grade observed in the surgical specimens were subjected to joint mutual information maximization for hierarchical feature selection and the development of the radiomic-signature. Youden-index was used to identify optimal cutoff for determining tumor grade. A random forest prediction model was trained and validated internally. The performance of this tool in predicting tumor grade was compared to that of EUS-FNA sampling that was used as the standard of reference.A total of 270 patients were included and a fusion radiomic-signature based on 10 selected features was developed using the development cohort (n = 201). There were 149 men and 121 women with a mean age of 59.4 ± 12.3 (standard deviation) years (range: 23.3-85.0 years). Upon internal validation in a new set of 69 patients, a strong discrimination was observed with an area under the curve (AUC) of 0.80 (95% confidence interval [CI]: 0.71-0.90) with corresponding sensitivity and specificity of 87.5% (95% CI: 79.7-95.3) and 73.3% (95% CI: 62.9-83.8) respectively. Of the study population, 143 patients (52.9%) underwent EUS-FNA. Biopsies were non-diagnostic in 26 patients (18.2%) and could not be graded due to insufficient sample in 42 patients (29.4%). In the cohort of 75 patients (52.4%) in whom biopsies were graded the radiomic-signature demonstrated not different AUC as compared to EUS-FNA (AUC: 0.69 vs. 0.67; P = 0.723), however greater sensitivity (i.e., ability to accurately identify G2/3 lesion was observed (80.8% vs. 42.3%; P < 0.001).Non-invasive assessment of tumor grade in patients with PNETs using the proposed radiomic-signature demonstrated high accuracy. Prospective validation and optimization could overcome the commonly experienced diagnostic uncertainty in the assessment of tumor grade in patients with PNETs and could facilitate clinical decision-making.Copyright © 2023. Published by Elsevier Masson SAS.