Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study

Abstract

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.

Metadata

Item Type:	Article
Authors/Creators:	Theophanous, S https://orcid.org/0000-0002-4148-3905 Lønne, P-I Choudhury, A Berbee, M Dekker, A Dennis, K Dewdney, A Gambacorta, MA Gilbert, A Guren, MG Holloway, L Jadon, R Kochhar, R Mohamed, AA Muirhead, R Parés, O Raszewski, L Roy, R Scarsbrook, A https://orcid.org/0000-0002-4243-032X Sebag-Montefiore, D https://orcid.org/0000-0002-5978-9259 Spezi, E Spindler, K-LG van Triest, B Vassiliou, V Malinen, E Wee, L Appelt, AL https://orcid.org/0000-0003-2792-9218 on behalf of the atomCAT consortium
Copyright, Publisher and Additional Information:	© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Anal cancer, Squamous cell carcinoma, Chemoradiotherapy, Distributed learning, Federated learning, outcome modelling, Overall survival, Locoregional control, Freedom from distant metastasis
Dates:	Published: December 2022 Published (online): 4 August 2022 Accepted: 9 June 2022
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Inst of Biomed & Clin Sciences (LIBACS) (Leeds)
Funding Information:	Funder Grant number Yorkshire Cancer Research Account Ref: 2UOLEEDS Not Known Cancer Research UK Supplier No: 138573 A28832
Depositing User:	Symplectic Publications
Date Deposited:	09 Aug 2022 13:51
Last Modified:	11 Jan 2023 11:20
Status:	Published
Publisher:	BMC
Identification Number:	10.1186/s41512-022-00128-8
Related URLs:	PubMed URL
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:189817

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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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