肛门鳞状细胞癌（ASCC）的发病率在全球范围内不断增加，接受治疗的患者中很大一部分出现复发。准确预测无进展生存期（PFS）和总生存期（OS）的能力将有助于制定个性化治疗策略。该研究的目的是训练和外部测试放射组学/临床特征衍生的事件发生时间预测模型。纳入了在两个大型三级转诊中心使用基线 FDG PET-CT 进行治疗性治疗的连续 ASCC 患者。使用 LIFEx 软件在治疗前 PET-CT 上进行放射组学特征提取。每个中心都训练和调整了两种不同的 PFS 和 OS 预测模型，其中表现最好的模型在其他中心的患者队列中进行了外部测试。中心 1 总共纳入了 187 名患者（平均年龄 61.6±11.5 岁） ，中位随访 30 个月，PFS 事件 = 57/187，OS 事件 = 46/187）和来自中心 2 的 257 名患者（平均年龄 62.6 ± 12.3 岁，中位随访 35 个月，PFS 事件 = 70/257，操作系统事件 = 54/257)。使用基于年龄和代谢肿瘤体积 (MTV) 的 Cox 回归模型实现了 PFS 和 OS 的最佳性能模型，训练 c 指数为 0.7，外部测试 c 指数为 0.7（标准误差 = 0.4）。使用外部验证已证明患者年龄和 MTV 的组合有可能预测 ASCC 患者的 OS 和 PFS。使用患者年龄和代谢肿瘤体积的 Cox 回归模型在外部测试中显示出良好的无进展生存预测潜力。我们小组之前发布的放射组学模型的优点无法在外部测试中得到证实。• 基于患者年龄和代谢肿瘤体积的预测模型显示出预测总生存期和无进展生存期的潜力，并在外部测试队列中得到了验证。 • 用于根据年龄和代谢肿瘤体积创建预测模型的方法可使用外部队列数据重复。 • 当考虑代谢肿瘤体积的影响时，正电子发射断层扫描-计算机断层扫描衍生的放射组学特征的预测能力减弱。© 2023。作者。

The incidence of anal squamous cell carcinoma (ASCC) is increasing worldwide, with a significant proportion of patients treated with curative intent having recurrence. The ability to accurately predict progression-free survival (PFS) and overall survival (OS) would allow for development of personalised treatment strategies. The aim of the study was to train and external test radiomic/clinical feature derived time-to-event prediction models.Consecutive patients with ASCC treated with curative intent at two large tertiary referral centres with baseline FDG PET-CT were included. Radiomic feature extraction was performed using LIFEx software on the pre-treatment PET-CT. Two distinct predictive models for PFS and OS were trained and tuned at each of the centres, with the best performing models externally tested on the other centres' patient cohort.A total of 187 patients were included from centre 1 (mean age 61.6 ± 11.5 years, median follow up 30 months, PFS events = 57/187, OS events = 46/187) and 257 patients were included from centre 2 (mean age 62.6 ± 12.3 years, median follow up 35 months, PFS events = 70/257, OS events = 54/257). The best performing model for PFS and OS was achieved using a Cox regression model based on age and metabolic tumour volume (MTV) with a training c-index of 0.7 and an external testing c-index of 0.7 (standard error = 0.4).A combination of patient age and MTV has been demonstrated using external validation to have the potential to predict OS and PFS in ASCC patients.A Cox regression model using patients' age and metabolic tumour volume showed good predictive potential for progression-free survival in external testing. The benefits of a previous radiomics model published by our group could not be confirmed on external testing.• A predictive model based on patient age and metabolic tumour volume showed potential to predict overall survival and progression-free survival and was validated on an external test cohort. • The methodology used to create a predictive model from age and metabolic tumour volume was repeatable using external cohort data. • The predictive ability of positron emission tomography-computed tomography-derived radiomic features diminished when the influence of metabolic tumour volume was accounted for.© 2023. The Author(s).