基于动脉期增强CT的影像组学模型可可靠预测非小细胞肺癌中的PD-L1表达及免疫治疗预后：一项回顾性多中心队列研究

Radiomics Models Derived From Arterial-Phase-Enhanced CT Reliably Predict Both PD-L1 Expression and Immunotherapy Prognosis in Non-small Cell Lung Cancer: A Retrospective, Multicenter Cohort Study.

作者信息

Liu Zhenhua, Yao Yimin, Zhao Miaomiao, Zhao Qi, Xue Jiao, Huang Yuhui, Qin Songbing

机构信息

Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou 215000, China; Department of Radiotherapy, Yancheng First Hospital Affiliated Hospital of Nanjing University Medical School, The First people's Hospital of Yancheng, 66 Renmin Road, Yancheng 224005, China; National Clinical Research Center for Hematologic Diseases, Cyrus Tang Medical Institute, Collaborative Innovation Center of Hematology, State Key Laboratory of Radiation Medicine and Prevention, Soochow University, Suzhou 215123, China.

Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou 215000, China.

出版信息

Acad Radiol. 2025 Jan;32(1):493-505. doi: 10.1016/j.acra.2024.07.028. Epub 2024 Jul 31.

DOI:10.1016/j.acra.2024.07.028

PMID:39084935

Abstract

RATIONALE AND OBJECTIVES

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC) and programmed cell death-ligand 1 (PD-L1) is a companion biomarker. This study aims to use baseline arterial-phase enhanced CT (APECT) to construct efficient radiomic models for predicting PD-L1 expression and immunotherapy prognosis in NSCLC.

MATERIALS AND METHODS

We extracted radiomics features from the baseline APECT images of 204 patients enrolled in a published multicenter clinical trial that commenced on August 23, 2018, and concluded on November 15, 2019 (ClinicalTrials.gov: NCT03607539). Of these patients, 146 patients from selected centers were assigned to the training cohort. The least absolute shrinkage and selection operator (LASSO) method was used to reduce dimensionality of radiomics features and calculate tumor scores. Models were created using naive bayes, decision trees, XGBoost, and random forest algorithms according to tumor scores. These models were then validated in an independent validation cohort comprising 58 patients from the remaining centers.

RESULTS

The random forest algorithm outperformed the other methods. In the three-classification scenario, the random forest model achieving the area under the curve (AUC) values of 0.98 and 0.94 in the training and validation cohorts, respectively. In the two-classification scenario, the random forest model achieved AUCs of 0.99 (95%CI: 0.97-1.0, P < 0.0001) and 0.93 (95%CI: 0.83-0.98, P < 0.0001) in the training and validation cohorts, respectively. Furthermore, patients classified as PD-L1 high-expression by this model can predict treatment response (AUC=0.859, 95%CI: 0.7-0.96, P < 0.001) and improved survival (HR=0.2, 95%CI: 0.08-0.53, P = 0.001) only in validation sintilimab arm.

CONCLUSION

Radiomics models based on APECT represent a potential non-invasive approach to robustly predict PD-L1 expression and ICI treatment outcomes in patients with NSCLC, which could significantly improve precision cancer immunotherapy.

摘要

原理与目的

免疫检查点抑制剂（ICI）彻底改变了非小细胞肺癌（NSCLC）的治疗方式，程序性细胞死亡配体1（PD-L1）是一种伴随生物标志物。本研究旨在利用基线动脉期增强CT（APECT）构建有效的放射组学模型，以预测NSCLC患者的PD-L1表达及免疫治疗预后。

材料与方法

我们从204例患者的基线APECT图像中提取放射组学特征，这些患者参与了一项已发表的多中心临床试验，该试验于2018年8月23日开始，2019年11月15日结束（ClinicalTrials.gov：NCT03607539）。其中，来自选定中心的146例患者被分配到训练队列。采用最小绝对收缩和选择算子（LASSO）方法降低放射组学特征的维度并计算肿瘤评分。根据肿瘤评分，使用朴素贝叶斯、决策树、XGBoost和随机森林算法创建模型。然后在由其余中心的58例患者组成的独立验证队列中对这些模型进行验证。

结果

随机森林算法优于其他方法。在三分类场景中，随机森林模型在训练队列和验证队列中的曲线下面积（AUC）值分别为0.98和0.94。在二分类场景中，随机森林模型在训练队列和验证队列中的AUC分别为0.99（95%CI：0.97 - 1.0，P < 0.0001）和0.93（95%CI：0.83 - 0.98，P < 0.0001）。此外，通过该模型分类为PD-L1高表达的患者仅在验证信迪利单抗组中可预测治疗反应（AUC = 0.859，95%CI：0.7 - 0.96，P < 0.001）和改善生存（HR = 0.2，95%CI：0.08 - 0.53，P = 0.001）。