PanNET 是一组罕见的胰腺肿瘤，表现出异质的组织病理学和临床行为。淋巴结疾病已被确定为 PanNET 中患者预后的最强预测因素之一。术前缺乏对淋巴结疾病的准确评估是这些患者治疗的主要限制，特别是那些患有小（< 2 cm）低级别肿瘤的患者。该研究的目的是评估放射组学特征 (RF) 术前预测胰腺神经内分泌肿瘤 (PanNET) 中淋巴结疾病存在的能力。使用机构数据库来识别接受切除的无功能 PanNET 患者。获得胰腺协议计算机断层扫描，手动分割，并提取 RF。使用最小冗余最大相关性分析对分层特征选择进行分析。约登指数用于确定预测淋巴结疾病的最佳截止值。使用 RF 和临床病理学特征训练随机森林预测模型并进行内部验证。在研究中纳入的 320 名患者中，根据手术标本的组织病理学评估，92 名患者 (28.8%) 患有淋巴结疾病。开发了基于十个选定射频的放射组学特征。预测淋巴结疾病的临床病理学特征包括肿瘤分级和大小。经过内部验证，放射组学和临床特征组合模型在识别淋巴结疾病方面表现出足够的性能（AUC 0.80）。该模型准确识别了 85% 的小肿瘤 (< 2 cm) 患者的淋巴结疾病。使用射频和临床病理特征对淋巴结疾病进行无创术前评估是可行的。© 2024。外科肿瘤学会。

PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs).An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally.Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm).Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.© 2024. Society of Surgical Oncology.