Importance:Patient with head and neck cancer of the oropharynx (HNC-OROP) undergo curative-intent definitive or post-operative radiation therapy. The systemic inflammation response index (SIRI) has independent prognostic capacity in HNC-OROP. We hypothesized that the use of SIRI may produce a parsimonious model of HNC-OROP outcomes.Objective:We aimed to investigate the prognostic utility of systemic inflammatory response index (SIRI) in oropharyngeal head and neck cancer patients who underwent radiation therapy. Design, Setting, and Participants: Random survival forest (RSF) machine learning was used to model survival in 568 oropharyngeal cancer patients in this retrospective cohort study. SIRI was calculated via pre-treatment bloodwork. Model validation was performed in an external cohort of 421 oropharyngeal cancer patients.Exposures:Exposure was curative-intent definitive or post-operative radiation therapy for head and neck cancer of the oropharynx (HNC-OROP).Results:This is a retrospective study with 568 and 421 patients in the Roswell Park and external Ohio State University cohorts. We evaluated full and reduced RSF models and a robust decision tree model. The C-index of the models was 0.758 (RSF full), 0.725 (RSF reduced), and 0.702 (decision tree). The incorporation of SIRI (with performance status and smoking history) into a machine learning model identified three risk-groups that significantly stratified overall survival (p< 0.0001). These findings were validated in the external validation cohort (p= 0.0019). Progression-free survival was also significantly different for the three groups in the validation cohort (p= 0.0025).Conclusions and Relevance:An integrated machine learning model using SIRI, performance status, and smoking history was successfully developed and externally validated in oropharyngeal head and neck cancer patients.
重要性：接受根治性放疗或术后放疗的口咽部头颈癌（HNC-OROP）患者，其全身炎症反应指数（SIRI）具有独立的预后评估价值。我们假设SIRI的应用可构建HNC-OROP预后的简约预测模型。 目的：探讨全身炎症反应指数（SIRI）对接受放疗的口咽部头颈癌患者的预后评估价值。 设计、场所与研究对象：本回顾性队列研究采用随机生存森林（RSF）机器学习方法，对568例口咽癌患者的生存数据建模。SIRI通过治疗前血液检测计算获得，模型在包含421例口咽癌患者的外部队列中进行验证。 暴露因素：暴露因素为针对口咽部头颈癌（HNC-OROP）的根治性放疗或术后放疗。 结果：本研究为回顾性分析，包含罗斯威尔公园癌症研究所队列（568例）和外部俄亥俄州立大学队列（421例）。我们评估了完整版与简化版RSF模型及稳健决策树模型，其C指数分别为0.758（完整RSF）、0.725（简化RSF）和0.702（决策树）。将SIRI（联合体能状态与吸烟史）纳入机器学习模型后，成功识别出三个具有显著总生存差异的风险分层（p<0.0001）。该发现在外部验证队列中得到确认（p=0.0019），且三组患者的无进展生存期在验证队列中也存在显著差异（p=0.0025）。 结论与意义：本研究成功构建并外部验证了整合SIRI、体能状态和吸烟史的机器学习模型，该模型适用于口咽部头颈癌患者的预后分层。
肿瘤(癌症)患者之家