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目的:探究非小细胞肺癌(NSCLC)患者治疗前基线n 18F-脱氧葡萄糖(FDG)PET/CT显像的影像组学特征对其生存预后的预测价值。n 方法:回顾性分析2016年1月至2018年8月于河北医科大学第四医院行治疗前n 18F-FDG PET/CT显像的孤立性NSCLC患者300例[男189例、女111例，年龄(62.3±9.0)岁];采用R语言将300例患者以7∶3比例随机分为训练组210例，验证组90例。采用LIFEx软件包提取300例患者NSCLC原发灶的PET和CT影像组学特征，在训练组中以最小绝对收缩和选择算子(LASSO)算法结合Cox比例风险回归模型筛选预测总生存(OS)和无进展生存(PFS)的影像组学特征和临床特征，分别建立影像组学模型、临床模型及两者结合的复杂预测模型，计算每例患者影像组学得分(Rad-score)、临床得分及复杂得分。在验证组中验证训练模型。通过一致性指数(C-index)评估各模型预测NSCLC患者预后效能，并基于最佳预测模型开发可视化列线图。n 结果:在训练组中，影像组学模型预测NSCLC患者OS、PFS的C-index分别为0.762和0.724，临床模型对应指标分别为0.834和0.780，复杂模型对应指标分别为0.842和0.787。Cox多因素分析结果显示，Rad-score和复杂得分均是影响OS[风险比(n HR)值：1.804、9.996，95% n CI：1.023～3.184、4.582～21.808，均n P<0.05]和PFS(n HR值：1.771、5.627，95% n CI：1.138～2.756、3.429～9.234，均n P<0.05)的独立预后因素。n 结论:治疗前n 18F-FDG PET/CT影像组学能够预测NSCLC患者OS和PFS；基于影像组学与临床模型结合的复杂模型预测NSCLC患者预后效能较高，由此得到的列线图对于辅助临床决策简单便捷。n “,”Objective:To investigate the value of baseline n 18F-fluorodeoxyglucose (FDG) PET/CT radiomics features in predicting the prognosis of non-small cell lung cancer (NSCLC) before treatment.n Methods:From January 2016 to August 2018, a total of 300 patients with solitary NSCLC (189 males, 111 females, age (62.3±9.0) years) who underwent n 18F-FDG PET/CT imaging before treatment in Fourth Hospital of Hebei Medical University were retrospectively analyzed. According to the ratio of 7∶3 (R language), 300 patients were randomly divided into training group (n n=210) and validation group (n n=90). LIFEx software package was used to extract the PET and CT radiomics features of primary focus in 300 NSCLC patients. The least absolute shrinkage and selection operator (LASSO) algorithm combined with Cox proportional hazard regression model were used to select radiomics features and clinical features for predicting overall survival (OS) and progression-free survival (PFS) in training group. Then radiomics model, clinical model and complex model which integrated the two were established and the radiomics score (Rad-score), clinical score and complex score of each patient were calculated. Data of validation group was used to validate each training model. Efficiencies of each model in predicting the prognosis of patients with NSCLC were further evaluated by the concordance index (C-index), and a nomogram was developed based on the best prediction model.n Results:In training group, the C-indices of predicting OS and PFS in NSCLC patients of radiomics model were 0.762 and 0.724 respectively, which were 0.834 and 0.780 respectively in clinical model, and were 0.842 and 0.787 respectively in complex model. Cox multivariate analysis showed that both Rad-score and complex score were independent prognostic factors for OS (hazard ratio (n HR): 1.804, 9.996, 95% n CI: 1.023-3.184, 4.582-21.808, both n P<0.05) and PFS (n HR: 1.771, 5.627, 95% n CI: 1.138-2.756, 3.429-9.234, both n P<0.05).n Conclusions:Pre-treatment n 18F-FDG PET/CT radiomics can predict OS and PFS of NSCLC patients. The complex model based on the combination of radiomics and clinical model is effective in predicting the prognosis of NSCLC patients, and the nomogram of complex model is simple and convenient to assist clinical decision-making.n