Huang Chao, He Jialin, Ding Zichuan, Li Hao, Zhou Zongke, Shi Xiaojun
Department of Orthopedics, West China Hospital of Sichuan University, Chengdu, China.
Department of Orthopedics, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Front Genet. 2022 May 30;13:865418. doi: 10.3389/fgene.2022.865418. eCollection 2022.
Bone metastasis (BM) is one of the typical metastatic types of head and neck cancer (HNC). The occurrence of BM prevents the HNC patients from obtaining a long survival period. Early assessment of the possibility of BM could bring more therapy options for HNC patients, as well as a longer overall survival time. This study aims to identify independent BM risk factors and develop a diagnostic nomogram to predict BM risk in HNC patients. Patients diagnosed with HNC between 2010 and 2015 were retrospectively evaluated in the Surveillance, Epidemiology, and End Results (SEER) database, and then eligible patients were enrolled in our study. First, those patients were randomly assigned to training and validation sets in a 7:3 ratio. Second, univariate and multivariate logistic regression analyses were used to determine the HNC patients' independent BM risk factors. Finally, the diagnostic nomogram's risk prediction capacity and clinical application value were assessed using calibration curves, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves. 39,561 HNC patients were enrolled in the study, and they were randomly divided into two sets: training ( = 27,693) and validation ( = 11,868). According to multivariate logistic regression analysis, race, primary site, tumor grade, T stage, N stage, and distant metastases (brain, liver, and lung) were all independent risk predictors of BM in HNC patients. The diagnostic nomogram was created using the above independent risk factors and had a high predictive capacity. The training and validation sets' area under the curves (AUC) were 0.893 and 0.850, respectively. The AUC values of independent risk predictors were all smaller than that of the constructed diagnostic nomogram. Meanwhile, the calibration curve and DCA also proved the reliability and accuracy of the diagnostic nomogram. The diagnostic nomogram can quickly assess the probability of BM in HNC patients, help doctors allocate medical resources more reasonably, and achieve personalized management, especially for HNC patients with a potentially high BM risk, thus acquiring better early education, early detection, and early diagnosis and treatment to maximize the benefits of patients.
骨转移（BM）是头颈癌（HNC）典型的转移类型之一。骨转移的发生使HNC患者难以获得较长生存期。早期评估骨转移可能性可为HNC患者带来更多治疗选择，并延长总生存时间。本研究旨在确定独立的骨转移风险因素，并开发一种诊断列线图以预测HNC患者的骨转移风险。对2010年至2015年间诊断为HNC的患者在监测、流行病学和最终结果（SEER）数据库中进行回顾性评估，然后将符合条件的患者纳入本研究。首先，将这些患者按7:3的比例随机分配到训练集和验证集。其次，采用单因素和多因素逻辑回归分析来确定HNC患者的独立骨转移风险因素。最后，使用校准曲线、受试者工作特征（ROC）曲线和决策曲线分析（DCA）曲线评估诊断列线图的风险预测能力和临床应用价值。39561例HNC患者纳入本研究，他们被随机分为两组：训练组（n = 27693）和验证组（n = 11868）。根据多因素逻辑回归分析，种族、原发部位、肿瘤分级、T分期、N分期和远处转移（脑、肝和肺）均为HNC患者骨转移的独立风险预测因素。利用上述独立风险因素创建了诊断列线图，其具有较高的预测能力。训练集和验证集的曲线下面积（AUC）分别为0.893和0.850。各独立风险预测因素的AUC值均小于构建的诊断列线图。同时，校准曲线和DCA曲线也证明了诊断列线图的可靠性和准确性。该诊断列线图可快速评估HNC患者发生骨转移的概率，有助于医生更合理地分配医疗资源，实现个性化管理，尤其是对于骨转移风险潜在较高的HNC患者，从而获得更好的早期教育、早期检测、早期诊断和治疗，使患者利益最大化。
