In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS). The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell’s concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2. CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.

中文翻译：

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结合基于计算机断层扫描成像的放射组学和临床病理学特征预测免疫检查点抑制剂在肺癌中的临床获益

在这项研究中，我们测试了从基线免疫治疗前计算机断层扫描 (CT) 图像中提取的放射组学特征和临床病理学特征的组合是否可用作预测非小细胞肺癌 (NSCLC) 患者临床获益的新型非侵入性生物标志物用免疫检查点抑制剂（ICIs）治疗。对 92 名接受过 ICI 治疗的连续肺癌患者的数据进行了回顾性分析。总共从预处理 CT 图像中选择了 88 个放射组学特征，用于构建随机森林模型。Radiomics 模型 1 是基于 Rad-score 构建的。使用多元逻辑回归分析，Rad 评分和显着预测因子被整合到一个单一的预测模型（放射组学列线图模型 1）中，以预测 ICI 的持久临床益处 (DCB)。放射组学模型 2 基于与放射组学模型 1 相同的 Rad-score 开发。使用多变量 Cox 比例风险回归分析、Rad-score 和独立危险因素，构建放射组学列线图模型 2 以预测无进展生存期 (PFS) ）。这些模型成功地预测了将从 ICI 中受益的患者。对于放射组学模型 1，训练组和验证组的接受者操作特征曲线下面积值分别为 0.848 和 0.795，而对于放射组学列线图模型 1，该值分别为 0.902 和 0.877。对于 PFS 预测，使用放射组学模型 2 时，训练组和验证组的 Harrell 一致性指数分别为 0.717 和 0.760，而使用放射组学列线图模型 2 时分别为 0.749 和 0.791。启动免疫治疗，以确定最有可能从治疗中受益的非小细胞肺癌患者。这可以指导晚期NSCLC的个体化治疗策略。