Application of radiomics for the prediction of HPV status for patients with head and neck cancers
Application of radiomics for the prediction of HPV status for patients with head and neck cancers
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DOI：
10.1002/mp.13977
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发表时间：
2020-01-06
期刊：
影响因子：
3.8
通讯作者：
Chetty, Indrin J.
中科院分区：
文献类型：
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作者：
Bagher-Ebadian, Hassan;Lu, Mei;Chetty, Indrin J.
目的对口咽癌患者术前对比增强CT（CE-CT）图像中提取的原发肿瘤进行放射组学分析，以识别鉴别特征，并构建最佳分类器，用于表征和预测人乳头瘤病毒（HPV）状态。材料与方法回顾性研究187例已知HPV感染状态的口咽癌患者（经免疫组化-p16蛋白检测证实）。A组：95例（19例HPV阴性，76例HPV阳性），来自MICAII大挑战。B组：92例患者（52例HPV-和40例HPV+）来自我们的机构。放射组学特征（172）从治疗前诊断CE-CT图像的总肿瘤体积（GTV）中提取。Levene和Kolmogorov-Smirnov检验与绝对双对数相关性（>0.48）用于鉴定HPV+和HPV-组之间的判别特征。判别特征被用来训练和测试八个不同的分类器。使用受试者工作特征下面积（AUC）、阳性预测值和阴性预测值（分别为PPV和NPV）来评估分类器的性能。主成分分析（PCA）应用于判别特征集和7个PC被用来训练和测试广义线性模型（GLM）分类器。结果在172个放射学特征中，只有3类12个放射学特征差异显著（P < 0.05，|BSc|> 0.48）。在训练并应用于预测HPV状态的八个分类器中，GLM对每个判别特征和组合的12个特征表现出最高的性能：AUC/PPV/NPV = 0.878/0.834/0.811。对于A组和B组的未知检验数据集，GLM高预测能力分别为AUC/PPV/NPV = 0.849/0.731/0.788和AUC/PPV/NPV = 0.869/0.807/0.870。通过应用PCA分析消除判别特征之间的相关性后，GLM的AUC、PPV和NPV的性能分别提高了3.3%、2.2%和1.8%。结论HPV阳性患者的GTV信号强度较高，病灶大小较小，球形度/圆度较大，空间强度变异性/异质性较高。结果提示，放射组学特征主要与对比增强诊断CT数据集上肿瘤的空间排列和形态学外观相关，可能潜在地用于HPV状态的分类。
Purpose To perform radiomic analysis of primary tumors extracted from pretreatment contrast-enhanced computed tomography (CE-CT) images for patients with oropharyngeal cancers to identify discriminant features and construct an optimal classifier for the characterization and prediction of human papilloma virus (HPV) status. Materials and methods One hundred and eighty seven patients with oropharyngeal cancers with known HPV status (confirmed by immunohistochemistry-p16 protein testing) were retrospectively studied as follows: Group A: 95 patients (19HPV- and 76HPV+) from the MICAII grand challenge. Group B: 92 patients (52HPV- and 40HPV+) from our institution. Radiomic features (172) were extracted from pretreatment diagnostic CE-CT images of the gross tumor volume (GTV). Levene and Kolmogorov-Smirnov's tests with absolute biserial correlation (>0.48) were used to identify the discriminant features between the HPV+ and HPV- groups. The discriminant features were used to train and test eight different classifiers. Area under receiver operating characteristic (AUC), positive predictive and negative predictive values (PPV and NPV, respectively) were used to evaluate the performance of the classifiers. Principal component analysis (PCA) was applied on the discriminant feature set and seven PCs were used to train and test a generalized linear model (GLM) classifier. Results Among 172 radiomic features only 12 radiomic features (from 3 categories) were significantly different (P < 0.05, |BSC| > 0.48) between the HPV+ and HPV- groups. Among the eight classifiers trained and applied for prediction of HPV status, the GLM showed the highest performance for each discriminant feature and the combined 12 features: AUC/PPV/NPV = 0.878/0.834/0.811. The GLM high prediction power was AUC/PPV/NPV = 0.849/0.731/0.788 and AUC/PPV/NPV = 0.869/0.807/0.870 for unseen test datasets for groups A and B, respectively. After eliminating the correlation among discriminant features by applying PCA analysis, the performance of the GLM was improved by 3.3%, 2.2%, and 1.8% for AUC, PPV, and NPV, respectively. Conclusion Results imply that GTV's for HPV+ patients exhibit higher intensities, smaller lesion size, greater sphericity/roundness, and higher spatial intensity-variation/heterogeneity. Results are suggestive that radiomic features primarily associated with the spatial arrangement and morphological appearance of the tumor on contrast-enhanced diagnostic CT datasets may be potentially used for classification of HPV status.