为了评估非小细胞肺癌（NSCLC）淋巴结（LN）状况，本研究基于临床因素、三维超短回波时间（3D-UTE）和PET放射组学特征，通过机器学习建立了基于[18F]FDG PET/3D-UTE模型。共有145例NSCLC患者（训练组101例，测试组44例）接受了全身[18F]FDG PET/CT和胸部[18F]FDG PET/MRI。对术前临床因素、3D-UTE、CT和PET放射组学特征进行了分析。采用Mann-Whitney U检验、LASSO回归和SelectKBest进行特征提取。使用五种机器学习算法建立了预测模型，并通过受试者工作特征曲线（ROC）、DeLong检验、校准曲线和决策曲线分析（DCA）进行评估。最终，使用包含四个临床因素、六个3D-UTE和六个PET放射组学特征的随机森林预测模型作为PET/3D-UTE的最终模型，其训练集和测试集的AUC分别为0.912和0.791。该模型不仅在不同程度上改善了单独模型（包括临床、3D-UTE和PET）的效果（AUC-训练=0.838，0.834和0.828，AUC-测试=0.756，0.745和0.768），而且达到了与最佳的PET/CT模型（AUC-训练=0.890，AUC-测试=0.793）相似的诊断效能。校准曲线和DCA分析表明该模型具有良好的一致性（C指数为0.912）和临床应用价值。基于临床因素、3D-UTE和PET放射组学特征的[18F]FDG PET/3D-UTE模型能够无创评估NSCLC的LN状况。[18F]FDG PET/3D-UTE模型是一种新颖的、辐射负担较小的非小细胞肺癌淋巴结状态的评估方法。本研究发现，基于PLS-DA分类器的3D-UTE放射组学模型与NSCLC的LN状况显著相关，并具有与临床、CT和PET模型相似的诊断表现。 [18F]FDG PET/3D-UTE模型基于RF分类器，能够无创评估NSCLC的LN状况，并且与临床、3D-UTE和PET模型相比，具有改善的诊断表现。在NSCLC淋巴结状况评估中，[18F]FDG PET/3D-UTE模型具有与包括临床因素、CT和PET放射组学特征的[18F]FDG PET/CT模型相似的诊断效能。© 2023年，作者（署名授权给欧洲放射学学会）。

To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC).A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA).A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively.The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC.A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice.• The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.© 2023. The Author(s), under exclusive licence to European Society of Radiology.