Background: In treatment of oropharyngeal squamous cell carcinoma (OPSCC), human papillomavirus status (HPV) plays a crucial role. The HPV-positive subtype tends to affect younger patients and is associated with a more favorable prognosis. HPV-associated lesions have been described in the parotid gland, which is included in routine imaging for OPSCC. This work aims to explore the ability of an ML system to classify HPV status based on imaging of the parotid gland, which is routinely depicted on staging imaging. Methods: Using a radiomics approach, we investigate the ability of five contemporary machine learning (ML) models to distinguish between HPV-positive and HPV-negative OPSCC based on non-contrast computed tomography (CT) data of tumor volume (TM), locoregional lymph node metastasis (LNM), and the parotid gland (Parotid). After exclusion of cases affected by streak artefacts, 53 patients (training set: 39; evaluation set: 14) were retrospectively evaluated. Classification performances were tested for significance against random optimistic results. Results: The best results are AUC 0.71 by XGBoost (XGB) for TM, AUC 0.82 by multi-layer perceptron (MLP) for LNM, AUC 0.76 by random forest (RF) for Parotid, and AUC 0.86 by XGB for a combination of all three regions of interest (ROIs). Conclusions: The results suggest involvement of the parotid gland in HPV infections of the oropharyngeal region. While the role of HPV in parotid lesions is under active discussion, the migration of the virus from the oral cavity to the parotid gland seems plausible. The imaging of the parotid gland offers the benefit of fewer streak artifacts due to teeth and dental implants and the potential to screen for HPV in cases of an absent or unlocatable tumor. Future investigation can be directed to validation of the results in independent datasets and to the potential of improvement of current classification models by addition of information based on the parotid gland.
背景：在口咽鳞状细胞癌（OPSCC）的治疗中，人乳头瘤病毒（HPV）状态起着关键作用。HPV阳性亚型通常影响较年轻患者，且预后更为良好。已有研究报道腮腺中存在HPV相关病变，而腮腺是OPSCC常规影像学检查的覆盖区域。本研究旨在探讨基于常规分期影像中显示的腮腺图像，利用机器学习（ML）系统对HPV状态进行分类的能力。方法：采用影像组学方法，我们评估了五种当代机器学习模型基于非增强计算机断层扫描（CT）数据区分HPV阳性与HPV阴性OPSCC的能力，分析区域包括肿瘤体积（TM）、区域淋巴结转移（LNM）及腮腺。在排除受条纹伪影影响的病例后，对53例患者（训练集39例；评估集14例）进行回顾性分析。分类性能通过随机乐观结果进行显著性检验。结果：最佳分类效果为：XGBoost（XGB）模型对TM区域的曲线下面积（AUC）达0.71，多层感知器（MLP）对LNM区域的AUC为0.82，随机森林（RF）对腮腺区域的AUC为0.76，而XGB模型对三个感兴趣区域（ROIs）联合分析的AUC可达0.86。结论：研究结果提示腮腺可能参与口咽部HPV感染过程。虽然HPV在腮腺病变中的作用尚存争议，但病毒从口腔迁移至腮腺的路径具有合理性。腮腺成像具有受牙齿及牙科植入物产生的条纹伪影干扰较少的技术优势，且在肿瘤缺失或难以定位时具备HPV筛查潜力。未来研究可致力于在独立数据集中验证该结果，并探索通过纳入腮腺信息改进现有分类模型的可行性。
肿瘤(癌症)患者之家