(1) Background: Advanced-stage lung cancer poses significant management challenges. The goal of this study was to identify crucial clinical and PET radiomics features that enable prognostic stratification for predicting outcomes. (2) Methods: PET radiomics features of the primary lung lesions were extracted from 99 patients with stage IVB NSCLC, and the robustness of these PET radiomics features was evaluated against uncertainties stemming from extraction parameters and contour variation. We trained three survival risk models (clinical, radiomics, and a composite) through a penalized Cox model framework. We also created a Balanced Random Forest classification predictive model, using the selected features, to predict 1-year survival. (3) Results: We identified 367 common PET radiomics features that exhibited robustness to perturbations introduced by contour variation and extraction parameters. Our findings indicated that both the radiomics and the composite model outperformed the clinical model in stratifying the risk for survival with statistical significance. In predicting 1-year survival, the radiomics model and the composite model also achieved better predicting accuracies compared to the clinical model. (4) Conclusions: Robust PET radiomics analysis successfully facilitated the stratification of patient risk for survival outcomes and predicted 1-year survival in stage IVB NSCLC.
（1）背景：晚期肺癌的治疗面临重大挑战。本研究旨在识别关键的临床特征与PET影像组学特征，以实现预后分层并预测治疗结局。（2）方法：本研究从99例IVB期非小细胞肺癌患者中提取原发肺部病灶的PET影像组学特征，并评估这些特征对提取参数与勾画变异所产生不确定性的稳健性。通过惩罚性Cox模型框架，我们构建了三种生存风险预测模型（临床模型、影像组学模型及复合模型）。同时基于筛选特征构建平衡随机森林分类预测模型，用于预测患者1年生存期。（3）结果：我们识别出367个对勾画变异和提取参数扰动具有稳健性的PET影像组学特征。研究发现，在生存风险分层方面，影像组学模型与复合模型均显著优于临床模型。在1年生存期预测中，影像组学模型与复合模型的预测精度亦优于临床模型。（4）结论：稳健的PET影像组学分析能有效实现IVB期非小细胞肺癌患者的生存风险分层，并成功预测1年生存期。
肿瘤(癌症)患者之家