Background/Objectives: Accurate identification of grade 1 (G1) pancreatic neuroendocrine tumors (PanNETs) is crucial due to their rising incidence and emerging nonsurgical management strategies. This study evaluated whether combining conventional CT imaging features, CT radiomics features, and clinical data improves differentiation of G1 PanNETs from higher-grade tumors (G2/G3 PanNETs and pancreatic neuroendocrine carcinomas [PanNECs]) compared to using these features individually. Methods: A retrospective analysis included 133 patients with pathologically confirmed PanNETs or PanNECs (70 males, 63 females; mean age, 58.5 years) who underwent pancreas protocol CT. A total of 28 conventional imaging features, 4892 radiomics features, and clinical data (age, gender, and tumor location) were analyzed using a support vector machine (SVM) model. Data were divided into 70% training and 30% testing sets. Results: The SVM model using the top 10 conventional imaging features (e.g., suspicious lymph nodes and hypoattenuating tumors) achieved 75% sensitivity, 81% specificity, and 79% accuracy for identifying higher-grade tumors (G2/G3 PanNETs and PanNECs). The top 10 radiomics features yielded 94% sensitivity, 46% specificity, and 69% accuracy. Combining all features (imaging, radiomics, and clinical data) improved performance, with 94% sensitivity, 69% specificity, 79% accuracy, and an F1-score of 0.77. The radiomics score demonstrated an AUC of 0.85 in the training and 0.83 in the testing set. Conclusions: Conventional imaging features provided higher specificity, while radiomics offered greater sensitivity for identifying higher-grade tumors. Integrating all three features improved diagnostic accuracy, highlighting their complementary roles. This combined model may serve as a valuable tool for distinguishing higher-grade tumors from G1 PanNETs and potentially guiding patient management.
背景/目的：随着1级（G1）胰腺神经内分泌肿瘤（PanNETs）发病率上升及非手术治疗策略的出现，其准确识别至关重要。本研究评估了结合常规CT影像特征、CT影像组学特征及临床数据，相较于单独使用这些特征，是否能提高G1 PanNETs与高级别肿瘤（G2/G3 PanNETs及胰腺神经内分泌癌[PanNECs]）的鉴别能力。方法：一项回顾性分析纳入了133例经病理证实的PanNETs或PanNECs患者（男性70例，女性63例；平均年龄58.5岁），所有患者均接受了胰腺方案CT检查。使用支持向量机（SVM）模型分析了28个常规影像特征、4892个影像组学特征及临床数据（年龄、性别和肿瘤位置）。数据按70%训练集和30%测试集划分。结果：采用前10个常规影像特征（如可疑淋巴结和低密度肿瘤）的SVM模型在识别高级别肿瘤（G2/G3 PanNETs和PanNECs）方面，达到了75%的敏感性、81%的特异性和79%的准确率。前10个影像组学特征则实现了94%的敏感性、46%的特异性和69%的准确率。结合所有特征（影像、影像组学和临床数据）后性能提升，敏感性达94%，特异性69%，准确率79%，F1分数为0.77。影像组学评分在训练集和测试集中的曲线下面积（AUC）分别为0.85和0.83。结论：常规影像特征在识别高级别肿瘤时具有更高的特异性，而影像组学则提供了更高的敏感性。整合所有三类特征可提高诊断准确性，突显了它们的互补作用。该组合模型可能成为区分高级别肿瘤与G1 PanNETs、并潜在指导患者管理的有价值工具。
肿瘤(癌症)患者之家