Theophanous Stelios, Lønne Per-Ivar, Choudhury Ananya, Berbee Maaike, Dekker Andre, Dennis Kristopher, Dewdney Alice, Gambacorta Maria Antonietta, Gilbert Alexandra, Guren Marianne Grønlie, Holloway Lois, Jadon Rashmi, Kochhar Rohit, Mohamed Ahmed Allam, Muirhead Rebecca, Parés Oriol, Raszewski Lukasz, Roy Rajarshi, Scarsbrook Andrew, Sebag-Montefiore David, Spezi Emiliano, Spindler Karen-Lise Garm, van Triest Baukelien, Vassiliou Vassilios, Malinen Eirik, Wee Leonard, Appelt Ane L
Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
Department of Medical Physics, Oslo University Hospital, Oslo, Norway.
Diagn Progn Res. 2022 Aug 4;6(1):14. doi: 10.1186/s41512-022-00128-8.
Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy.
This is a retrospective multi-centre cohort study, investigating overall survival, locoregional control and freedom from distant metastasis after primary chemoradiotherapy for anal squamous cell carcinoma. Patient data will be extracted and organised at each participating radiotherapy centre (n = 18). Candidate prognostic factors have been identified through literature review and expert opinion. Summary statistics will be calculated and exchanged between centres prior to modelling. The primary analysis will involve developing and validating Cox proportional hazards models across centres for each outcome through distributed learning. Outcomes at specific timepoints of interest and factor effect estimates will be reported, allowing for outcome prediction for future patients.
The atomCAT2 study will analyse one of the largest available cross-institutional cohorts of patients with anal cancer treated with chemoradiotherapy. The analysis aims to provide information on current international clinical practice outcomes and may aid the personalisation and design of future anal cancer clinical trials through contributing to a better understanding of patient risk stratification.
肛管癌是一种发病率呈上升趋势的罕见癌症。尽管先进的放化疗带来了相对较好的治疗效果，但进一步改善疾病控制和降低毒性已被证明具有挑战性。利用常规收集的数据开发和验证预后模型可能为治疗方案的制定和选择提供新的见解。然而，由于该癌症的罕见性，很难获得足够的数据，尤其是来自单一中心的数据，来开发和验证可靠的模型。此外，多中心模型开发受到伦理障碍和数据保护法规的阻碍，这些法规常常限制对患者数据的获取。分布式（或联邦式）学习允许使用来自多个中心的数据开发模型，而无需任何个体层面的患者数据离开原始中心，从而保护患者数据隐私。这项工作基于概念验证的三中心atomCAT1研究，并描述了多中心atomCAT2研究的方案，该研究旨在为肛管癌放化疗后的三个临床重要结局开发和验证可靠的预后模型。
这是一项回顾性多中心队列研究，调查肛管鳞状细胞癌初次放化疗后的总生存期、局部区域控制和无远处转移情况。患者数据将在每个参与的放疗中心（n = 18）提取和整理。通过文献综述和专家意见确定候选预后因素。在建模之前，将计算汇总统计数据并在各中心之间交换。主要分析将涉及通过分布式学习在各中心为每个结局开发和验证Cox比例风险模型。将报告感兴趣的特定时间点的结局和因素效应估计值，以便对未来患者进行结局预测。
atomCAT2研究将分析接受放化疗的肛管癌患者中最大的可用跨机构队列之一。该分析旨在提供有关当前国际临床实践结局的信息，并可能通过有助于更好地理解患者风险分层来辅助未来肛管癌临床试验的个性化设计和方案制定。
