■利用超声内镜(EUS)图像开发和验证放射组学模型,以区分胰岛素瘤和非功能性胰腺神经内分泌肿瘤(NF-PNETs)。
■共有106名患者,包括61例胰岛素瘤和45例NF-PNETs,包括在这项研究中。患者被随机分配到训练或测试队列。从瘤内和瘤周区域提取影像组学特征,分别。六种机器学习算法被用来训练肿瘤内预测模型,仅使用非零系数特征。研究人员确定了最有效的肿瘤内影像组学模型,随后将其用于开发肿瘤周围和联合影像组学模型。最后,我们构建并评估了胰岛素瘤的预测列线图.
■基于EUS共提取了107个影像组学特征,并且仅保留具有非零系数的特征。在六个肿瘤内影像组学模型中,光梯度升压机(LightGBM)模型表现出优越的性能。此外,建立并评估了肿瘤周影像组学模型.组合模型,整合肿瘤内和肿瘤周围的影像组学特征,在训练队列中表现出相当的表现(AUC=0.876),在测试队列中预测结果的准确度最高(AUC=0.835).德隆测试,校正曲线,和决策曲线分析(DCA)用于验证这些发现。与NF-PNETs相比,胰岛素瘤的直径明显较小。最后,列线图,结合直径和影像组学签名,建造和评估,在训练(AUC=0.929)和测试(AUC=0.913)队列中都有优异的表现。
■开发了一种新颖且有影响力的放射组学模型和列线图,并利用EUS图像对NF-PNETs和胰岛素瘤进行了准确区分。
UNASSIGNED: To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs).
UNASSIGNED: A total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed.
UNASSIGNED: A total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts.
UNASSIGNED: A novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images.