Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study
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Published:2022-08-04
Issue:1
Volume:6
Page:
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ISSN:2397-7523
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Container-title:Diagnostic and Prognostic Research
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language:en
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Short-container-title:Diagn Progn Res
Author:
Theophanous SteliosORCID, 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., Adams Richard, Amin Muhammad, Capocchiano Nikola Dino, Colley Peter, Damiani Andrea, De Luca Viola, Deijen Charlotte, Demetriou Antri, Eble Michael J, Field Matthew, Georgiou Loukia, Henry Ann, Lau Joanna, Lee Mark, Lilley John, Lopes Patricia, Lutz Christina Maria, Manfrida Stefania, Marsden Jenny, Masciocchi Carlotta, Mercer Joseph, Nyvang Lars, Papageorgiou Elisavet, Price Gareth, Rackley Thomas, Savino Mariachiara, Stroom Joep, Stylianou Ioannis, Tambe Nilesh, Thwaites David, Trojanowski Maciej, Valentini Vincenzo, Vieira Sandra,
Abstract
Abstract
Background
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.
Methods
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.
Discussion
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.
Funder
Cancer Research UK Yorkshire Cancer Research Nederlandse Organisatie voor Wetenschappelijk Onderzoek Hanarth Foundation
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,General Mathematics
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