Background/Objectives:Checkpoint inhibitors (ICIs) are key therapies for NSCLC, but current selection criteria, such as excluding mutation carriers and assessing PD-L1, lack sensitivity. As a result, many patients receive costly treatments with limited benefit. Therefore, this study aimed to predict which NSCLC patients would achieve durable survival (≥24 months) with immunotherapy.Methods:A comprehensive ensemble radiomics approach was applied to pretreatment CT scans to prognosticate overall survival (OS) and predict progression-free survival (PFS) in a cohort of 220 consecutive patients with inoperable NSCLC treated with first-line ICIs (pembrolizumab or atezolizumab, nivolumab or prolgolimab) as monotherapy or in combination. The radiomics pipeline evaluated four normalization methods (none, min-max, Z-score, mean), four feature selection techniques (ANOVA, RFE, Kruskal–Wallis, Relief), and ten classifiers (e.g., SVM, random forest). Using two to eight radiomics features, 1680 models were built in the Feature Explorer (FAE) Python package.Results:Three feature sets were evaluated: clinicopathological (CP) only, radiomics only, and a combined set, using 6- and 12-month PFS and 24-month OS endpoints. The top 15 models were ensembled by averaging their probability scores. The best performance was achieved at 24-month OS with the combined CP and radiomics ensemble (AUC = 0.863, accuracy = 85%), followed by radiomics-only (AUC = 0.796, accuracy = 82%) and CP-only (AUC = 0.671, accuracy = 76%). Predictive performance was lower for 6-month (AUC = 0.719) and 12-month PFS (AUC = 0.739) endpoints.Conclusions:Our radiomics pipeline improved selection of NSCLC patients for immunotherapy and could spare non-responders unnecessary toxicity while enhancing cost-effectiveness.
背景/目的：免疫检查点抑制剂（ICIs）是非小细胞肺癌（NSCLC）的关键疗法，但当前的选择标准，如排除突变携带者和评估PD-L1表达，敏感性不足。这导致许多患者接受了昂贵但获益有限的治疗。因此，本研究旨在预测哪些NSCLC患者能从免疫治疗中获得持久生存（≥24个月）。 方法：本研究采用综合性集成放射组学方法，对220例连续入组的接受一线ICI单药或联合治疗（帕博利珠单抗或阿替利珠单抗、纳武利尤单抗或普洛利单抗）的不可手术NSCLC患者治疗前CT图像进行分析，以预测总生存期（OS）和无进展生存期（PFS）。放射组学流程评估了四种标准化方法（无标准化、最小-最大、Z分数、均值）、四种特征选择技术（方差分析、递归特征消除、Kruskal-Wallis检验、Relief算法）以及十种分类器（如支持向量机、随机森林）。利用Python软件包FAE，使用2至8个放射组学特征构建了1680个模型。 结果：研究评估了三个特征集：仅临床病理学（CP）特征、仅放射组学特征以及两者结合的特征集，并采用6个月和12个月PFS以及24个月OS作为终点。通过平均其概率得分，对排名前15的模型进行了集成。最佳性能出现在24个月OS终点上，结合CP与放射组学特征的集成模型表现最优（AUC = 0.863，准确率 = 85%），其次是仅放射组学模型（AUC = 0.796，准确率 = 82%）和仅CP模型（AUC = 0.671，准确率 = 76%）。对于6个月（AUC = 0.719）和12个月PFS（AUC = 0.739）终点，预测性能较低。 结论：我们的放射组学流程优化了NSCLC患者接受免疫治疗的选择，可能使无应答者避免不必要的毒性，同时提高了成本效益。
肿瘤(癌症)患者之家