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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

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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351222/
https://www.ncbi.nlm.nih.gov/pubmed/35922837
http://dx.doi.org/10.1186/s41512-022-00128-8
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author 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.
author_facet 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.
author_sort Theophanous, Stelios
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-022-00128-8.
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spelling pubmed-93512222022-08-05 Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study 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. Diagn Progn Res Protocol 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-022-00128-8. BioMed Central 2022-08-04 /pmc/articles/PMC9351222/ /pubmed/35922837 http://dx.doi.org/10.1186/s41512-022-00128-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Protocol
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.
Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study
title Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study
title_full Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study
title_fullStr Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study
title_full_unstemmed Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study
title_short Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study
title_sort development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomcat2 study
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351222/
https://www.ncbi.nlm.nih.gov/pubmed/35922837
http://dx.doi.org/10.1186/s41512-022-00128-8
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