Introduction

Immunotherapy has improved outcomes for patients with non-small cell lung cancer (NSCLC), yet durable clinical benefit (DCB) is experienced in only a fraction of patients. Here, we test the hypothesis that radiomics features from baseline pretreatment ¹⁸F-FDG PET/CT scans can predict clinical outcomes of NSCLC patients treated with checkpoint blockade immunotherapy.

Methods

This study included 194 patients with histologically confirmed stage IIIB-IV NSCLC with pretreatment PET/CT images. Radiomics features were extracted from PET, CT, and PET+CT fusion images based on minimum Kullback–Leibler divergence (KLD) criteria. The radiomics features from 99 retrospective patients were used to train a multiparametric radiomics signature (mpRS) to predict DCB using an improved least absolute shrinkage and selection operator (LASSO) method, which was subsequently validated in both retrospective (N = 47) and prospective test cohorts (N = 48). Using these cohorts, the mpRS was also used to predict progression-free survival (PFS) and overall survival (OS) by training nomogram models using multivariable Cox regression analyses with additional clinical characteristics incorporated.

Results

The mpRS could predict patients who will receive DCB, with areas under receiver operating characteristic curves (AUCs) of 0.86 (95%CI 0.79–0.94), 0.83 (95%CI 0.71–0.94), and 0.81 (95%CI 0.68–0.92) in the training, retrospective test, and prospective test cohorts, respectively. In the same three cohorts, respectively, nomogram models achieved C-indices of 0.74 (95%CI 0.68–0.80), 0.74 (95%CI 0.66–0.82), and 0.77 (95%CI 0.69–0.84) to predict PFS and C-indices of 0.83 (95%CI 0.77–0.88), 0.83 (95%CI 0.71–0.94), and 0.80 (95%CI 0.69–0.91) to predict OS.

Conclusion

PET/CT-based signature can be used prior to initiation of immunotherapy to identify NSCLC patients most likely to benefit from immunotherapy. As such, these data may be leveraged to improve more precise and individualized decision support in the treatment of patients with advanced NSCLC.

中文翻译：

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18 F-FDG PET/CT 图像的放射组学预测晚期 NSCLC 患者对检查点阻断免疫治疗的临床获益

介绍

免疫疗法改善了非小细胞肺癌 (NSCLC) 患者的预后，但只有一小部分患者获得了持久的临床获益 (DCB)。在这里，我们测试了以下假设：基线预处理¹⁸ F-FDG PET/CT 扫描的放射组学特征可以预测接受检查点阻断免疫疗法治疗的 NSCLC 患者的临床结果。

方法

这项研究纳入了 194 名经组织学证实具有治疗前 PET/CT 图像的 IIIB-IV 期 NSCLC 患者。根据最小 Kullback-Leibler 散度 (KLD) 标准从 PET、CT 和 PET+CT 融合图像中提取放射组学特征。使用 99 名回顾性患者的放射组学特征来训练多参数放射组学特征 (mpRS)，以使用改进的最小绝对收缩和选择算子 (LASSO) 方法预测 DCB，该方法随后在回顾性 (N = 47  ) 和前瞻性测试中得到验证队列（N  = 48）。使用这些队列，mpRS 还用于通过使用多变量 Cox 回归分析并结合其他临床特征来训练列线图模型来预测无进展生存期 (PFS) 和总生存期 (OS)。

结果

mpRS 可以预测将接受 DCB 的患者，受试者工作特征曲线 (AUC) 下的面积为 0.86 (95%CI 0.79–0.94)、0.83 (95%CI 0.71–0.94) 和 0.81 (95%CI 0.68–0.92) ）分别在训练、回顾性测试和前瞻性测试队列中。在相同的三个队列中，列线图模型预测 PFS 和 C 的 C 指数分别为 0.74 (95% CI 0.68–0.80)、0.74 (95% CI 0.66–0.82) 和 0.77 (95% CI 0.69–0.84) -预测 OS 的指数为 0.83 (95% CI 0.77–0.88)、0.83 (95% CI 0.71–0.94) 和 0.80 (95% CI 0.69–0.91)。

结论

基于 PET/CT 的特征可在开始免疫治疗之前使用，以识别最有可能从免疫治疗中受益的 NSCLC 患者。因此，可以利用这些数据来改善晚期 NSCLC 患者治疗中更精确和个性化的决策支持。