Table_2_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）患者制定治疗方案的关键环节。本研究旨在构建并验证基于18F-氟代脱氧葡萄糖正电子发射断层显像（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例经病理确诊为非小细胞肺癌、且治疗前接受过18F-FDG PET/CT检查的高代谢淋巴结患者。其中一家中心的228枚淋巴结按7:3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）；另一家中心的60枚淋巴结纳入外部验证队列。从PET图像的淋巴结区域提取放射组学特征。采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）结合10折交叉验证筛选特征后，通过多变量logistic回归构建PET放射组学特征。将PET放射组学特征（模型1）、CT影像特征与临床资料来源的独立预测因子（模型2）整合为联合模型（模型3）。针对该复合模型绘制列线图，并通过区分度、校准度与临床实用性评估其性能。
结果：模型1在训练队列、内部验证队列与外部验证队列中的受试者工作特征曲线下面积（area under the curve, AUC）分别为0.820、0.785与0.808，对淋巴结转移（lymph node metastasis, LNM）展现出良好的诊断效能。此外，模型2在训练队列（AUC=0.780）、内部验证队列（AUC=0.794）与外部验证队列（AUC=0.802）中亦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诊断性能，其AUC分别为0.874、0.845与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，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阳性与假阳性结果，可为临床医生制定个体化治疗决策提供辅助支持。