The aim of our retrospective study is to develop and assess an imaging-based model utilizing18F-FDG PET parameters for predicting the five-year survival in non-small-cell lung cancer (NSCLC) patients after curative surgery. A total of 361 NSCLC patients who underwent curative surgery were assigned to the training set (n= 253) and the test set (n= 108). The LASSO regression model was used to construct a PET-based risk score for predicting five-year survival. A hybrid model that combined the PET-based risk score and clinical variables was developed using multivariate logistic regression analysis. The predictive performance was determined by the area under the curve (AUC). The individual features with the best predictive performances were co-occurrence_contrast (AUC = 0.675) and SUL peak (AUC = 0.671). The PET-based risk score was identified as an independent predictor after adjusting for clinical variables (OR 5.231, 95% CI 1.987–6.932;p= 0.009). The hybrid model, which integrated clinical variables, significantly outperformed the PET-based risk score alone in predictive accuracy (AUC = 0.771 vs. 0.696,p= 0.022), a finding that was consistent in the test set. The PET-based risk score, especially when integrated with clinical variables, demonstrates good predictive ability for five-year survival in NSCLC patients following curative surgery.
本研究旨在开发并评估一种基于18F-FDG PET影像参数的预测模型，用于预测非小细胞肺癌（NSCLC）患者根治性手术后的五年生存率。研究共纳入361例接受根治性手术的NSCLC患者，将其分为训练集（n=253）和测试集（n=108）。采用LASSO回归模型构建基于PET影像的风险评分以预测五年生存率，并通过多因素逻辑回归分析建立融合PET风险评分与临床变量的混合模型。通过曲线下面积（AUC）评估模型的预测效能。结果显示，最具预测效能的影像特征为共生矩阵对比度（AUC=0.675）和标准摄取值峰值（AUC=0.671）。经临床变量校正后，PET风险评分被确定为独立预测因子（OR=5.231，95% CI 1.987–6.932；p=0.009）。融合临床变量的混合模型在预测准确性上显著优于单纯PET风险评分（AUC=0.771 vs. 0.696，p=0.022），该结论在测试集中得到一致性验证。研究表明，基于PET影像的风险评分，特别是与临床变量整合后，对NSCLC患者根治性术后的五年生存率具有良好的预测能力。
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