Purpose: This study evaluates the impact of harmonization and multi-region feature integration on survival prediction in non-small cell lung cancer (NSCLC) patients. We assess the prognostic utility of handcrafted radiomics and pretrained deep features from thoracic CT images, integrating them with clinical data using a multicentre dataset. Methods: Survival models were built using handcrafted radiomic and deep features from lung, tumor, mediastinal nodes, coronary arteries, and coronary artery calcium (CAC) scores from 876 patients across five centres. CT features were harmonized using ComBat, reconstruction kernel normalization (RKN), and RKN-ComBat. Models were constructed at the region of interest (ROI) level and through ensemble strategies. Regularized Cox models estimated overall survival, with performance assessed via the concordance index (C-index), 5-year time-dependent area under the curve (t-AUC), and hazard ratios. SHAP values interpreted feature contributions, while consensus analysis categorized predicted survival probabilities at fixed time points. Results: TNM staging showed prognostic value (C-index = 0.67; hazard ratio = 2.70; t-AUC = 0.85). The clinical and tumor texture radiomics model with ComBat yielded high performance (C-index = 0.76; t-AUC = 0.88). FM deep features from 50 voxel cubes also showed predictive value (C-index = 0.76; t-AUC = 0.89). An ensemble model combining tumor, lung, mediastinal node, CAC, and FM features achieved a C-index of 0.71 and t-AUC of 0.79. Consensus analysis identified a high-confidence patient subset, resulting in a model with a 5-year t-AUC of 0.92, sensitivity of 96.8%, and specificity of 70.0%. Conclusion: Harmonization and multi-region feature integration enhance survival prediction in NSCLC patients using CT imaging, supporting individualized risk stratification in multicentre settings.

翻译：目的：本研究评估特征标准化与多区域特征整合对非小细胞肺癌（NSCLC）患者生存预测的影响。通过多中心数据集，我们评估了基于胸部CT影像的手工影像组学特征与预训练深度特征的预后价值，并将其与临床数据整合。方法：利用来自五个中心876例患者的肺部、肿瘤、纵隔淋巴结、冠状动脉及冠状动脉钙化（CAC）评分数据，构建基于手工影像组学特征和深度特征的生存预测模型。CT特征通过ComBat、重建核标准化（RKN）及RKN-ComBat方法进行标准化处理。分别在感兴趣区域（ROI）层面及通过集成策略构建模型。采用正则化Cox模型评估总生存期，通过一致性指数（C-index）、5年时间依赖性曲线下面积（t-AUC）和风险比评估性能。利用SHAP值解释特征贡献度，同时通过共识分析对固定时间点的预测生存概率进行分类。结果：TNM分期显示预后价值（C-index = 0.67；风险比 = 2.70；t-AUC = 0.85）。采用ComBat标准化的临床与肿瘤纹理影像组学模型表现出较高性能（C-index = 0.76；t-AUC = 0.88）。基于50体素立方体的基础模型深度特征同样具有预测价值（C-index = 0.76；t-AUC = 0.89）。融合肿瘤、肺部、纵隔淋巴结、CAC及基础模型特征的集成模型获得C-index为0.71，t-AUC为0.79。共识分析识别出高置信度患者亚组，由此构建的模型5年t-AUC达0.92，敏感性为96.8%，特异性为70.0%。结论：特征标准化与多区域特征整合能提升基于CT影像的NSCLC患者生存预测性能，支持多中心环境下的个体化风险分层。