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首页> 外文期刊>The Laryngoscope: A Medical Journal for Clinical and Research Contributions in Otolaryngology, Head and Neck Medicine and Surgery, Facial Plastic and Reconstructive Surgery .. >Machine Learning Based RadiomicHPVPhenotyping of OropharyngealSCC: A Feasibility Study UsingMRI
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Machine Learning Based RadiomicHPVPhenotyping of OropharyngealSCC: A Feasibility Study UsingMRI

机译：基于机器学习的OropharyngealsCC的radiomichpvphenotyping：MRI的可行性研究

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Objectives To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status. Study Design Retrospective cohort study. Methods Pretreatment MRI data from 62 consecutive patients with oropharyngeal SCC were retrospectively reviewed, and chronologically allocated to training (n = 43) and test sets (n = 19). Enhancing tumors were semi-automatically segmented on each slice of the postcontrast T1WI to span the entire tumor volume, after registration of T2WI to postcontrast T1WI; 170 radiomic features were extracted from the entire tumor volume. Relevant features were selected and radiomics models were trained using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation, after subsampling of training sets using synthetic minority over-sampling technique to mitigate data imbalance. The selected features, weighted by their respective coefficients, were combined linearly to yield a radiomics score. The diagnostic performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC). Results Six radiomic features, which revealed strong association with HPV status of oropharyngeal SCC, were selected using LASSO. The radiomics model yielded excellent performance on the training set (AUC, 0.982 [95% CI, 0.942-1.000]) and moderate performance on the test set (AUC, 0.744 [95% CI, 0.496-0.991]) for differentiating oropharyngeal SCC according to HPV status. Conclusions Radiomics-based MRI phenotyping differentiates oropharyngeal SCC according to HPV status, and thus, is a potential imaging biomarker. Level of Evidence 3Laryngoscope, 2020
机译：目的探讨基于放射磁共振成像特征的预测模型是否能根据人乳头瘤病毒（HPV）状态区分口咽鳞状细胞癌（SCC）。研究设计：回顾性队列研究。方法回顾性分析62例口咽鳞状细胞癌患者的术前MRI资料，按时间顺序分为训练组（n=43）和测试组（n=19）。在将T2WI注册到对比后T1WI后，在对比后T1WI的每个切片上半自动分割增强肿瘤，以覆盖整个肿瘤体积；从整个肿瘤体积中提取170个放射特征。选择相关特征，并使用最小绝对收缩和选择算子（LASSO）逻辑回归模型（带有10倍交叉验证）对放射组学模型进行训练，然后使用合成少数超抽样技术对训练集进行二次抽样，以缓解数据不平衡。所选特征通过各自的系数进行加权，然后线性组合，得出放射组学评分。使用受试者工作特征曲线（AUC）下面积评估放射评分的诊断性能。结果利用LASSO技术筛选出6种与口咽鳞状细胞癌HPV感染状态密切相关的放射特征。放射组学模型在训练集（AUC，0.982[95%CI，0.942-1.000]）上表现优异，在测试集（AUC，0.744[95%CI，0.496-0.991]）上根据HPV状态区分口咽SCC表现中等。结论基于放射组学的MRI分型根据HPV状态区分口咽鳞状细胞癌，是一种潜在的影像学生物标志物。证据水平3喉镜，2020年

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