Moiseenko Fedor, Radulovic Marko, Tsvetkova Nadezhda, Chernobrivceva Vera, Gabina Albina, Oganesian Any, Makarkina Maria, Elsakova Ekaterina, Krasavina Maria, Barsova Daria, Artemeva Elizaveta, Khenshtein Valeria, Levchenko Natalia, Chubenko Viacheslav, Egorenkov Vitaliy, Volkov Nikita, Bogdanov Alexei, Moiseyenko Vladimir
N.P Napalkov Saint Petersburg Clinical Research and Practical Centre for Specialized Types of Medical Care (Oncological), Leningradskaya Str. 68A, 197758 Saint Petersburg, Russia.
N.N. Petrov National Medical Research Center of Oncology, Ministry of Public Health of the Russian Federation, Leningradskaya Str. 68, 197758 Saint Petersburg, Russia.
Cancers (Basel). 2025 May 27;17(11):1790. doi: 10.3390/cancers17111790.
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. 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. 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. Our radiomics pipeline improved selection of NSCLC patients for immunotherapy and could spare non-responders unnecessary toxicity while enhancing cost-effectiveness.
检查点抑制剂（ICIs）是治疗非小细胞肺癌（NSCLC）的关键疗法，但目前的选择标准，如排除突变携带者和评估程序性死亡受体配体1（PD-L1），缺乏敏感性。因此，许多患者接受了昂贵的治疗，却获益有限。因此，本研究旨在预测哪些NSCLC患者接受免疫治疗后能实现持久生存（≥24个月）。一种综合的整体放射组学方法应用于治疗前的计算机断层扫描（CT），以预测220例接受一线ICIs（帕博利珠单抗或阿替利珠单抗、纳武利尤单抗或普罗吉莫单抗）单药治疗或联合治疗的不可切除NSCLC连续患者队列的总生存期（OS）并预测无进展生存期（PFS）。放射组学流程评估了四种归一化方法（无、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、克鲁斯卡尔-沃利斯检验、Relief）和十种分类器（如支持向量机、随机森林）。利用两到八个放射组学特征，在Feature Explorer（FAE）Python包中构建了1680个模型。评估了三个特征集：仅临床病理（CP）、仅放射组学以及一个组合集，并使用6个月和12个月的PFS以及24个月的OS作为终点。通过对前15个模型的概率得分求平均值进行整合。联合CP和放射组学整合在24个月OS时表现最佳（曲线下面积[AUC]=0.863，准确率=85%），其次是仅放射组学（AUC=0.796，准确率=82%）和仅CP（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。我们的放射组学流程改善了NSCLC患者免疫治疗的选择，可使无反应者避免不必要毒性，同时提高成本效益。
