为了以非侵入性方式预测胰腺神经内分泌肿瘤（pNETs）的病理分级，我们开发了一种基于计算机断层扫描（CT）放射组学的可解释机器学习（ML）模型。我们纳入了2010年至2022年间接受增强腹部CT检查的pNETs患者作为回顾性研究对象。提取了放射组学特征，并开发了五个基于放射组学的ML模型，分别是逻辑回归（LR）、随机森林（RF）、支持向量机（SVM）、XGBoost和GaussianNB。使用独立于时间的测试集评估了这些模型的性能，计算了灵敏度、特异度、准确度和受试者工作特征曲线下面积（AUC）等指标。将放射组学模型的准确性与针刺活检进行对比。利用Shapley Additive Explanation（SHAP）工具和放射组学与生物学特征之间的相关性来探索模型的可解释性。本研究纳入了122名患者（平均年龄：50±14岁；男性53名）作为训练集，随后纳入了100名患者（平均年龄：48±13岁；男性50名）作为测试集。其中，LR、SVM、RF、XGBoost和GaussianNB的AUC分别为0.758、0.742、0.779、0.744和0.745，相应的准确度分别为73.0%、70.0%、77.0%、71.9%和72.9%。SHAP工具确定了两个静脉期特征是最重要的，这些特征在Ki-67指数或有丝分裂计数亚组中显示出显著差异（p<0.001）。一种可解释的基于放射组学的RF模型可以有效区分pNETs的G1和G2/3，具有良好的可解释性。本研究开发的放射组学可解释模型在临床上具有显著的相关性，它提供了一种评估胰腺神经内分泌肿瘤病理分级的非侵入性方法，并有望成为传统组织活检的重要补充工具。•开发了一种基于放射组学的可解释模型，用于预测pNETs的病理分级，并通过准确性与术前针刺活检进行了比较。•该模型，基于CT放射组学，具有良好的可解释性。•放射组学模型有望作为一种有价值的术前针刺活检补充技术，但不应视为活检的替代品。© 2023. 作者。

To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner.Patients with pNETs who underwent contrast-enhanced abdominal CT between 2010 and 2022 were included in this retrospective study. Radiomics features were extracted, and five radiomics-based ML models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), XGBoost, and GaussianNB, were developed. The performance of these models was evaluated using a time-independent testing set, and metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were calculated. The accuracy of the radiomics model was compared to that of needle biopsy. The Shapley Additive Explanation (SHAP) tool and the correlation between radiomics and biological features were employed to explore the interpretability of the model.A total of 122 patients (mean age: 50 ± 14 years; 53 male) were included in the training set, whereas 100 patients (mean age: 48 ± 13 years; 50 male) were included in the testing set. The AUCs for LR, SVM, RF, XGBoost, and GaussianNB were 0.758, 0.742, 0.779, 0.744, and 0.745, respectively, with corresponding accuracies of 73.0%, 70.0%, 77.0%, 71.9%, and 72.9%. The SHAP tool identified two features of the venous phase as the most significant, which showed significant differences among the Ki-67 index or mitotic count subgroups (p < 0.001).An interpretable radiomics-based RF model can effectively differentiate between G1 and G2/3 of pNETs, demonstrating favorable interpretability.The radiomics-based interpretable model developed in this study has significant clinical relevance as it offers a non-invasive method for assessing the pathological grade of pancreatic neuroendocrine tumors and holds promise as an important complementary tool to traditional tissue biopsy.• A radiomics-based interpretable model was developed to predict the pathological grade of pNETs and compared with preoperative needle biopsy in terms of accuracy. • The model, based on CT radiomics, demonstrated favorable interpretability. • The radiomics model holds potential as a valuable complementary technique to preoperative needle biopsy; however, it should not be considered a replacement for biopsy.© 2023. The Author(s).