Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, United States of America.
Department of Oral and Maxillofacial Surgery, University of Port Harcourt, Choba, Nigeria.
PLoS One. 2024 Aug 26;19(8):e0309214. doi: 10.1371/journal.pone.0309214. eCollection 2024.
Oral Squamous Cell Carcinoma is the most prevalent malignancies affecting the oral cavity. Despite progress in studies and treatment options its outlook remains grim with survival prospects greatly affected by demographic and clinical factors. Precisely predicting survival rates and prognosis plays a role in making treatment choices for the best achievable overall health outcomes.
To develop and validate an accelerated failure time model as a predictive model for cause-specific survival and prognosis of Oral Squamous Cell Carcinoma patients and compare its results to the traditional Cox proportional hazard model.
We screened Oral cancer patients diagnosed with Squamous Cell Carcinoma from the Surveillance Epidemiology and End Results (SEER) database between 2010 and 2020. An accelerated failure time model using the Type I generalized half logistic distribution was used to determine independent prognostic factors affecting the survival time of patients with oral squamous carcinoma. In addition, accelerated factors were estimated to assess how some variables influence the survival times of the patients. We used the Akaike Information Criterion, Bayesian Information Criterion to evaluate the model fit, the area under the curve for discriminability, Concordance Index (C-index) and Root Mean Square Error and calibration curve for predictability, to compare the type I generalized half logistic survival model to other common classical survival models. All tests are conducted at a 0.05 level of significance.
The accelerated failure time models demonstrated superior effectiveness in modeling (fit and predictive accuracy) the cause-specific survival (CSS) of oral squamous cell carcinoma compared to the Cox model. Among the accelerated failure time models considered, the Type I generalized half logistic distribution exhibited the most robust model fit, as evidenced by the lowest Akaike Information Criterion (AIC = 27370) and Bayesian Information Criterion (BIC = 27415) values. This outperformed other parametric models and the Cox Model (AIC = 47019, BIC = 47177). The TIGHLD displayed an AUC of 0.642 for discrimination, surpassing the Cox model (AUC = 0.544). In terms of predictive accuracy, the model achieved the highest concordance index (C-index = 0.780) and the lowest root mean square error (RMSE = 1.209), a notable performance over the Cox model (C-index = 0.336, RMSE = 6.482). All variables under consideration in this study demonstrated significance at the 0.05 level for CSS, except for race and the time span from diagnosis to treatment, in the TIGHLD AFT model. However, differences emerged regarding the significant variations in survival times among subgroups. Finally, the results derived from the model revealed that all significant variables except chemotherapy, all TNM stages and patients with Grade II and III tumor presentations contributed to the deceleration of time to cause-specific deaths.
The accelerated failure time model provides a relatively accurate method to predict the prognosis of oral squamous cell carcinoma patients and is recommended over the Cox PH model for its superior predictive capabilities. This study also underscores the importance of using advanced statistical models to improve survival predictions and outcomes for cancer patients.
口腔鳞状细胞癌是最常见的影响口腔的恶性肿瘤。尽管在研究和治疗方案方面取得了进展，但由于人口统计学和临床因素的影响，其预后仍然严峻，生存前景受到极大影响。准确预测生存率和预后对于为实现最佳整体健康结果选择治疗方案至关重要。
开发并验证加速失效时间模型作为口腔鳞状细胞癌患者的特定原因生存和预后的预测模型，并将其结果与传统的 Cox 比例风险模型进行比较。
我们从 2010 年至 2020 年期间的监测、流行病学和最终结果（SEER）数据库中筛选出诊断为鳞状细胞癌的口腔癌患者。使用 I 型广义半对数分布的加速失效时间模型来确定影响口腔鳞状细胞癌患者生存时间的独立预后因素。此外，还估计了加速因素，以评估某些变量如何影响患者的生存时间。我们使用赤池信息量准则、贝叶斯信息量准则来评估模型拟合度、区分曲线下面积、一致性指数（C 指数）和均方根误差和校准曲线来预测，以比较 I 型广义半对数生存模型与其他常见的经典生存模型。所有检验均在 0.05 水平上进行。
与 Cox 模型相比，加速失效时间模型在建模（拟合和预测准确性）口腔鳞状细胞癌的特定原因生存（CSS）方面表现出更好的效果。在所考虑的加速失效时间模型中，I 型广义半对数分布表现出最稳健的模型拟合，证据是最低的赤池信息量准则（AIC = 27370）和贝叶斯信息量准则（BIC = 27415）值。这优于其他参数模型和 Cox 模型（AIC = 47019，BIC = 47177）。TIGHLD 的区分度 AUC 为 0.642，超过了 Cox 模型（AUC = 0.544）。在预测准确性方面，该模型的一致性指数（C 指数）最高（C 指数 = 0.780），均方根误差（RMSE）最低（RMSE = 1.209），明显优于 Cox 模型（C 指数 = 0.336，RMSE = 6.482）。在 TIGHLD AFT 模型中，除种族和诊断到治疗的时间间隔外，所有纳入本研究的变量在 CSS 方面均具有统计学意义（p 值均 < 0.05）。然而，在亚组间生存时间的显著变化方面存在差异。最后，模型得出的结果表明，除化疗、所有 TNM 分期和 II 级和 III 级肿瘤表现的患者外，所有有意义的变量都有助于特定原因死亡时间的减速。
加速失效时间模型为口腔鳞状细胞癌患者的预后提供了一种相对准确的预测方法，并且因其预测能力更强，建议优先选择 Cox PH 模型。本研究还强调了使用先进的统计模型来提高癌症患者生存预测和结果的重要性。
