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

Anal cancer Chemoradiotherapy Distributed learning Federated learning Freedom from distant metastasis Locoregional control Overall survival Squamous cell carcinoma outcome modelling

Journal

Diagnostic and prognostic research
ISSN: 2397-7523
Titre abrégé: Diagn Progn Res
Pays: England
ID NLM: 101718985

Informations de publication

Date de publication:
04 Aug 2022
Historique:
received: 11 03 2022
accepted: 09 06 2022
entrez: 3 8 2022
pubmed: 4 8 2022
medline: 4 8 2022
Statut: epublish

Résumé

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.

Sections du résumé

BACKGROUND 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 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 CONCLUSIONS
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.

Identifiants

pubmed: 35922837
doi: 10.1186/s41512-022-00128-8
pii: 10.1186/s41512-022-00128-8
pmc: PMC9351222
doi:

Types de publication

Journal Article

Langues

eng

Pagination

14

Subventions

Organisme : Cancer Research UK
ID : C19942/A28832
Pays : United Kingdom
Organisme : Yorkshire Cancer Research
ID : L389AA
Pays : United Kingdom
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : BIONIC (grant no. 629.002.204), TRAIN (grant no. 629.002.212), CARRIER (grant no. 628.011.212), and STRaTegy (grant no. 14930)
Organisme : Hanarth Foundation
ID : N/A

Investigateurs

Richard Adams (R)
Muhammad Amin (M)
Nikola Dino Capocchiano (ND)
Peter Colley (P)
Andrea Damiani (A)
Viola De Luca (V)
Charlotte Deijen (C)
Antri Demetriou (A)
Michael J Eble (MJ)
Matthew Field (M)
Loukia Georgiou (L)
Ann Henry (A)
Joanna Lau (J)
Mark Lee (M)
John Lilley (J)
Patricia Lopes (P)
Christina Maria Lutz (CM)
Stefania Manfrida (S)
Jenny Marsden (J)
Carlotta Masciocchi (C)
Joseph Mercer (J)
Lars Nyvang (L)
Elisavet Papageorgiou (E)
Gareth Price (G)
Thomas Rackley (T)
Mariachiara Savino (M)
Joep Stroom (J)
Ioannis Stylianou (I)
Nilesh Tambe (N)
David Thwaites (D)
Maciej Trojanowski (M)
Vincenzo Valentini (V)
Sandra Vieira (S)

Informations de copyright

© 2022. The Author(s).

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Auteurs

Stelios Theophanous (S)

Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK. umsth@leeds.ac.uk.

Per-Ivar Lønne (PI)

Department of Medical Physics, Oslo University Hospital, Oslo, Norway.

Ananya Choudhury (A)

MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands.

Maaike Berbee (M)

MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands.

Andre Dekker (A)

MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands.

Kristopher Dennis (K)

The Ottawa Hospital and the University of Ottawa, Ottawa, Canada.

Alice Dewdney (A)

Weston Park Hospital, Sheffield, UK.

Maria Antonietta Gambacorta (MA)

Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica S.Cuore, Rome, Italy.

Alexandra Gilbert (A)

Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.

Marianne Grønlie Guren (MG)

Department of Oncology, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

Lois Holloway (L)

Ingham Research Institute and Liverpool Hospital, Liverpool, New South Wales, Australia.

Rashmi Jadon (R)

Addenbrooke's Hospital, Cambridge, UK.

Rohit Kochhar (R)

The Christie NHS Foundation Trust, Manchester, UK.

Ahmed Allam Mohamed (AA)

RWTH Aachen University Medical Centre, Aachen, Germany.

Rebecca Muirhead (R)

Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

Oriol Parés (O)

Champalimaud Foundation, Lisbon, Portugal.

Lukasz Raszewski (L)

Greater Poland Cancer Centre, Poznań, Poland.

Rajarshi Roy (R)

Hull University Teaching Hospitals NHS Trust, Hull, UK.

Andrew Scarsbrook (A)

Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
Leeds Teaching Hospitals NHS Trust, Leeds, UK.

David Sebag-Montefiore (D)

Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.

Emiliano Spezi (E)

Cardiff University, Cardiff, UK.

Karen-Lise Garm Spindler (KG)

Aarhus University Hospital, Aarhus, Denmark.

Baukelien van Triest (B)

The Netherlands Cancer Institute-Antoni van Leeuwenhoek (NKI-AVL), Amsterdam, The Netherlands.

Vassilios Vassiliou (V)

Bank of Cyprus Oncology Centre, Nicosia, Cyprus.

Eirik Malinen (E)

Department of Medical Physics, Oslo University Hospital, Oslo, Norway.

Leonard Wee (L)

MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands.

Ane L Appelt (AL)

Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
Leeds Teaching Hospitals NHS Trust, Leeds, UK.

Classifications MeSH