Background/Objectives:The duration of the response to radiotherapy-related treatment is a critical prognostic indicator for patients with nasopharyngeal carcinoma (NPC). Persistent tumor status, including residual tumor presence and early recurrence, is associated with poorer survival outcomes. To address this, we developed a prediction model to identify patients at a high risk of persistent tumor status prior to initiating treatment.Methods:This retrospective study included 104 patients with NPC receiving radiotherapy-related treatment who had completed a 3-year follow-up period; 29 were classified into the persistent tumor status group and 75 into the disease-free group. Radiomic features were extracted from pretreatment positron emission tomography (PET) images and used to construct a prediction model by employing machine learning algorithms. The model’s diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC), whereas SHapley Additive exPlanations (SHAP) analysis was conducted to determine the contribution of individual features to the model.Results:The prediction model developed using the AdaBoost algorithm and validated through five-fold cross-validation achieved the highest AUC of 0.934. Its sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 89.66%, 86.67%, 72.22%, 95.59%, and 87.5%, respectively. SHAP analysis revealed that the feature of high dependence low metabolic uptake emphasis50had the greatest impact on model predictions. Furthermore, patients classified as disease-free exhibited markedly higher overall survival rates compared with those with persistent tumor status.Conclusions:In conclusion, the proposed prediction model efficiently identified patients with NPC at a high risk of persistent tumor status by using radiomic features extracted from pretreatment PET images.
背景/目的：放疗相关治疗的反应持续时间是鼻咽癌患者的关键预后指标。持续性肿瘤状态，包括残留肿瘤存在和早期复发，与较差的生存结局相关。为此，我们开发了一个预测模型，旨在治疗开始前识别持续性肿瘤状态高风险患者。 方法：这项回顾性研究纳入了104例接受放疗相关治疗并完成3年随访的鼻咽癌患者；其中29例被归入持续性肿瘤状态组，75例归入无病组。从治疗前正电子发射断层扫描（PET）图像中提取影像组学特征，并运用机器学习算法构建预测模型。采用受试者工作特征曲线下面积（AUC）评估模型的诊断性能，同时进行SHapley加性解释（SHAP）分析以确定各特征对模型的贡献度。 结果：使用AdaBoost算法开发并通过五折交叉验证的预测模型取得了最高的AUC值0.934。其敏感性、特异性、阳性预测值、阴性预测值和准确率分别为89.66%、86.67%、72.22%、95.59%和87.5%。SHAP分析显示，高依赖性低代谢摄取强调50这一特征对模型预测的影响最大。此外，与持续性肿瘤状态患者相比，被归类为无病的患者总生存率显著更高。 结论：综上所述，所提出的预测模型通过利用从治疗前PET图像中提取的影像组学特征，能有效识别持续性肿瘤状态高风险的鼻咽癌患者。
肿瘤(癌症)患者之家