Predicting programmed death-ligand 1 expression level in non-small cell lung cancer using a combination of peritumoral and intratumoral radiomic features on computed tomography
Predicting programmed death-ligand 1 expression level in non-small cell lung cancer using a combination of peritumoral and intratumoral radiomic features on computed tomography
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DOI：
10.1088/2057-1976/ac4d43
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发表时间：
2022-03-01
影响因子：
1.4
通讯作者：
Tanaka, Hidekazu
中科院分区：
文献类型：
--
作者：
Shiinoki, Takehiro;Fujimoto, Koya;Tanaka, Hidekazu
在这项研究中，我们研究了利用计算机断层扫描（CT）图像上肿瘤内和肿瘤周围肿瘤的放射学特征预测程序性死亡配体1 （PD-L1）表达水平的可能性。我们回顾性分析了161例非小细胞肺癌患者。我们在CT图像上提取肿瘤内和肿瘤周围区域的放射学特征。利用零重要性、最小绝对收缩和选择算子模型选择优化的特征子集，构建PD-L1表达水平预测模型。采用五重交叉验证的LightGBM构建预测模型，评价受试者工作特性。计算训练组和测试组相应的曲线下面积（AUC）。基于共识聚类计算歧义聚类对的比例，以评价所选特征的有效性。此外，还计算了训练组和测试组的Radscore。对于PD-L1表达水平高于1%，包括肿瘤内区域放射组学特征以及肿瘤内和肿瘤周围区域放射组学特征组合的预测模型在训练组和测试组中的AUC分别为0.83和0.87,0.64和0.74。相比之下，50%以上预测模型的AUC分别为0.80、0.97和0.74、0.83。根据PD-L1表达水平>= 50%或>= 1%将所选特征分为两个亚组。当结合肿瘤内和肿瘤周围区域的放射学特征时，亚组1的Radscore在统计学上高于亚组2。我们利用CT图像构建了PD-Ll表达水平的预测模型。结合瘤内和瘤周放射学特征的模型比仅瘤内放射学特征的模型具有更高的准确性。
In this study, we investigated the possibility of predicting expression levels of programmed death-ligand 1 (PD-L1) using radiomic features of intratumoral and peritumoral tumors on computed tomography (CT) images. We retrospectively analyzed 161 patients with non-small cell lung cancer. We extracted radiomic features for intratumoral and peritumoral regions on CT images. The null importance, least absolute shrinkage, and selection operator model were used to select the optimized feature subset to build the prediction models for the PD-L1 expression level. LightGBM with five-fold cross-validation was used to construct the prediction model and evaluate the receiver operating characteristics. The corresponding area under the curve (AUC) was calculated for the training and testing cohorts. The proportion of ambiguously clustered pairs was calculated based on consensus clustering to evaluate the validity of the selected features. In addition, Radscore was calculated for the training and test cohorts. For expression level of PD-L1 above 1%, prediction models that included radiomic features from the intratumoral region and a combination of radiomic features from intratumoral and peritumoral regions yielded an AUC of 0.83 and 0.87 and 0.64 and 0.74 in the training and test cohorts, respectively. In contrast, the models above 50% prediction yielded an AUC of 0.80, 0.97, and 0.74, 0.83, respectively. The selected features were divided into two subgroups based on PD-L1 expression levels >= 50% or >= 1%. Radscore was statistically higher for subgroup one than subgroup two when radiomic features for intratumoral and peritumoral regions were combined. We constructed a predictive model for PD-Ll expression level using CT images. The model using a combination of intratumoral and peritumoral radiomic features had a higher accuracy than the model with only intratumoral radiomic features.