Table_1_Development and Validation of a 18F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal–Hilar Lymph Nodes in Non-Small-Cell Lung Cancer.docx

BackgroundAccurate evaluation of lymph node (LN) status is critical for determining the treatment options in patients with non-small cell lung cancer (NSCLC). This study aimed to develop and validate a 18F-FDG PET-based radiomic model for the identification of metastatic LNs from the hypermetabolic mediastinal–hilar LNs in NSCLC.
MethodsWe retrospectively reviewed 259 patients with hypermetabolic LNs who underwent pretreatment 18F-FDG PET/CT and were pathologically confirmed as NSCLC from two centers. Two hundred twenty-eight LNs were allocated to a training cohort (LN = 159) and an internal validation cohort (LN = 69) from one center (7:3 ratio), and 60 LNs were enrolled to an external validation cohort from the other. Radiomic features were extracted from LNs of PET images. A PET radiomics signature was constructed by multivariable logistic regression after using the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation. The PET radiomics signature (model 1) and independent predictors from CT image features and clinical data (model 2) were incorporated into a combined model (model 3). A nomogram was plotted for the complex model, and the performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness.
ResultsThe area under the curve (AUC) values of model 1 were 0.820, 0.785, and 0.808 in the training, internal, and external validation cohorts, respectively, showing good diagnostic efficacy for lymph node metastasis (LNM). Furthermore, model 2 was able to discriminate metastatic LNs in the training (AUC 0.780), internal (AUC 0.794), and external validation cohorts (AUC 0.802), respectively. Model 3 showed optimal diagnostic performance among the three cohorts, with an AUC of 0.874, 0.845, and 0.841, respectively. The nomogram based on the model 3 showed good discrimination and calibration.
ConclusionsOur study revealed that PET radiomics signature, especially when integrated with CT imaging features, showed the ability to identify true and false positives of mediastinal–hilar LNM detected by PET/CT in patients with NSCLC, which would help clinicians to make individual treatment decisions.

背景：准确评估淋巴结（Lymph Node, LN）状态，是非小细胞肺癌（non-small cell lung cancer, NSCLC）患者治疗方案选择的关键环节。本研究旨在构建并验证基于18F-脱氧葡萄糖正电子发射断层成像（18F-FDG PET）的放射组学模型，以识别非小细胞肺癌患者纵隔-肺门高代谢淋巴结中的转移性淋巴结。
方法：本研究回顾性分析了来自两家中心的259例患者，这些患者均经病理证实为非小细胞肺癌，且接受了治疗前18F-FDG PET/CT检查，同时存在高代谢淋巴结。其中一家中心的228枚淋巴结按7:3的比例分为训练队列（159枚淋巴结）与内部验证队列（69枚淋巴结）；另一家中心纳入60枚淋巴结作为外部验证队列。研究人员从PET图像的淋巴结区域提取放射组学特征。通过最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）结合10折交叉验证，经多变量logistic回归构建PET放射组学特征标签（模型1）。将PET放射组学特征标签（模型1）与来自CT图像特征及临床数据的独立预测因子相结合，构建联合模型（模型3）。针对该复合模型绘制列线图，并通过区分度、校准度及临床实用性评估列线图的性能。
结果：模型1在训练队列、内部验证队列及外部验证队列中的曲线下面积（Area Under the Curve, AUC）分别为0.820、0.785及0.808，对淋巴结转移（Lymph Node Metastasis, LNM）展现出良好的诊断效能。进一步分析显示，模型2在训练队列（AUC=0.780）、内部验证队列（AUC=0.794）及外部验证队列（AUC=0.802）中同样可有效区分转移性淋巴结。模型3在三个队列中均展现出最优的诊断性能，其曲线下面积分别为0.874、0.845及0.841。基于模型3构建的列线图具备良好的区分度与校准度。
结论：本研究表明，PET放射组学特征标签，尤其是结合CT影像特征后，能够有效识别非小细胞肺癌患者PET/CT检出的纵隔-肺门淋巴结转移的真假阳性结果，可为临床医师制定个体化治疗决策提供助力。