Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result
Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result
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DOI：
10.1016/j.acra.2019.04.016
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发表时间：
2020-02-01
影响因子：
4.8
通讯作者：
Yao, Xiuzhong
中科院分区：
文献类型：
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作者：
Jiang, Mengmeng;Sun, Dazhen;Yao, Xiuzhong
基本原理和目标：探讨放射组学特征衍生方法在评估非小细胞肺癌（NSCLC）患者PD-L1表达状态中的潜在价值。在PET/CT图像采集后进行肿瘤分割以选择NSCLC病例的基本原发病灶。提取特征，然后使用自动相关性确定进行过滤，并基于其与PD-L1表达状态的相关性使用LASSO模型进行最小化。最后，我们分别利用CT、PET和PET/CT图像的特征建立了预测模型，以区分特定PD-L1类型的不同状态。结果：共筛选出24个与PD-L1表达水平显著相关的特征，建立了基于CT、PET、PET/CT特征的PD-L1表达模型，并进行了比较。对于PD-L1（SP142）表达水平超过1%的预测值，包含CT、PET和PET/CT图像放射组学特征的模型的AUC分别为0.97、0.61和0.97;超过50%预测值的模型的AUC分别为0.80、0.65和0.77。对于PD-L1（28-8）表达水平预测，超过1%表达的预测模型得分分别为0.86、0.62和0.85;超过50%表达的特征分别达到AUC得分0.91、0.75和0.88。基于放射组学的预测方法，尤其是CT衍生的预测模型，可以相对准确地预测NSCLC患者的PD-L1表达状态。这可能有助于指导临床免疫治疗，值得进一步分析。
Rationale and Objectives: To explore the potential value of radiomic features-derived approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) patients.Materials and Methods: A cohort of 399 stage 1-IV NSCLC patients were enrolled. Tumor segmentation was performed to select essential primary lesions of NSCLC cases after PET/CT images acquisition. Features were extracted, then filtered with automatic relevance determination and minimized with LASSO model based on its relevance of PD-L1 expression status. Finally, we built predictive models with features from the CT, the PET, and the PET/CT images, respectively, for differentiating different status of specific PD-L1 types. Fivefold cross validation was practiced to evaluate the signatures' accuracy, and the receiver operating characteristic as well as the corresponding area under the curve (AUC) was reckoned for each model.Results: With the total of 24 selected features which were significantly associated with PD-L1 expression levels, models based on CT-, PET-, PET/CT-derived features were built and compared. For PD-L1 (SP142) expression level over 1% prediction, models that comprised radiomic features from the CT, the PET, and the PET/CT images resulted in an AUC of 0.97, 0.61, and 0.97, respectively; models for over 50% prediction resulted with AUC of 0.80, 0.65, and 0.77. For PD-L1 (28-8) expression level prediction, predictive models of over 1% expression scored at 0.86, 0.62, and 0.85; and signatures of over 50% expression reached the score of AUCs at 0.91, 0.75, and 0.88, respectively.Conclusion: The radiomic-based predictive approach, especially CT-derived predictive model, may anticipate PD-L1 expression status in NSCLC patients relatively accurate. It may be helpful in guiding immunotherapy in clinical practice and deserves further analysis.