High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models—penalized Cox model, random forest, gradient boosted model and support vector machine—to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months of follow-up, respectively. We developed models with five-fold cross-validation, selected the best-performing model for each outcome based on the concordance index (C-statistic) and the integrated Brier score (IBS) and validated them in an independent cohort of 102 patients. The penalized Cox model demonstrated better performance for ACM (C-statistic = 0.70, IBS = 0.12) and DM (C-statistic = 0.70, IBS = 0.08) while the random forest model displayed better performance for LR (C-statistic = 0.76, IBS = 0.07). We conclude that the ML-based prognostic model can aid clinicians in quantifying prognosis and determining effective treatment strategies, thereby improving favorable outcomes in HNSCC patients.
源自治疗前正电子发射断层扫描（PET）图像的高维影像组学特征可为头颈部鳞状细胞癌（HNSCC）患者提供预后信息。本研究基于232例患者的124个PET影像组学特征及临床变量（年龄、性别、癌症分期、原发部位），通过五折交叉验证构建了惩罚Cox模型、随机森林、梯度提升模型和支持向量机四种生存模型，分别预测36个月全因死亡率（ACM）、24个月局部区域复发/残留病灶（LR）及24个月远处转移（DM）概率。根据一致性指数（C统计量）和综合Brier评分（IBS）筛选各结局的最优模型，并在102例独立队列中进行验证。惩罚Cox模型在ACM（C统计量=0.70，IBS=0.12）和DM（C统计量=0.70，IBS=0.08）预测中表现更优，而随机森林模型在LR预测中表现更佳（C统计量=0.76，IBS=0.07）。研究表明基于机器学习的预后模型可辅助临床医生量化预后并制定有效治疗策略，从而改善HNSCC患者的临床结局。
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