Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
Eur Radiol. 2024 May;34(5):3194-3204. doi: 10.1007/s00330-023-10340-9. Epub 2023 Nov 4.
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.
全球范围内肛门鳞状细胞癌（ASCC）的发病率正在上升，相当一部分接受根治性治疗的患者出现复发。能够准确预测无进展生存期（PFS）和总生存期（OS）将有助于制定个性化的治疗策略。本研究的目的是训练和外部测试基于放射组学和临床特征的时间相关预测模型。
纳入在两家大型三级转诊中心接受根治性治疗的连续 ASCC 患者，均有基线 FDG PET-CT 检查。使用 LIFEx 软件对基线 PET-CT 进行放射组学特征提取。在每个中心分别训练和调整用于预测 PFS 和 OS 的两个不同的预测模型，并在其他中心的患者队列中对最佳模型进行外部测试。
中心 1 共纳入 187 例患者（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），中心 2 共纳入 257 例患者（平均年龄 62.6±12.3 岁，中位随访 35 个月，PFS 事件=70/257，OS 事件=54/257）。基于年龄和代谢肿瘤体积（MTV）的 Cox 回归模型是预测 PFS 和 OS 的最佳模型，其训练 c 指数为 0.7，外部测试 c 指数为 0.7（标准误差=0.4）。
使用外部验证证明，年龄和 MTV 的组合具有预测 ASCC 患者 OS 和 PFS 的潜力。
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对无进展生存期的良好预测潜力。本研究无法验证之前由我们团队发表的放射组学模型的预测能力。
基于患者年龄和代谢肿瘤体积的预测模型具有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验证。
使用外部队列数据重复创建预测模型的方法是可行的。
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会降低。
