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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.
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.
Afiliação
  • Theophanous S; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK. umsth@leeds.ac.uk.
  • Lønne PI; Department of Medical Physics, Oslo University Hospital, Oslo, Norway.
  • 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.
  • 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.
  • 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.
  • Dennis K; The Ottawa Hospital and the University of Ottawa, Ottawa, Canada.
  • Dewdney A; Weston Park Hospital, Sheffield, UK.
  • Gambacorta MA; Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica S.Cuore, Rome, Italy.
  • Gilbert A; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Guren MG; Department of Oncology, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
  • Holloway L; Ingham Research Institute and Liverpool Hospital, Liverpool, New South Wales, Australia.
  • Jadon R; Addenbrooke's Hospital, Cambridge, UK.
  • Kochhar R; The Christie NHS Foundation Trust, Manchester, UK.
  • Mohamed AA; RWTH Aachen University Medical Centre, Aachen, Germany.
  • Muirhead R; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Parés O; Champalimaud Foundation, Lisbon, Portugal.
  • Raszewski L; Greater Poland Cancer Centre, Poznan, Poland.
  • Roy R; Hull University Teaching Hospitals NHS Trust, Hull, UK.
  • Scarsbrook A; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Sebag-Montefiore D; Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • Spezi E; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Spindler KG; Cardiff University, Cardiff, UK.
  • van Triest B; Aarhus University Hospital, Aarhus, Denmark.
  • Vassiliou V; The Netherlands Cancer Institute-Antoni van Leeuwenhoek (NKI-AVL), Amsterdam, The Netherlands.
  • Malinen E; Bank of Cyprus Oncology Centre, Nicosia, Cyprus.
  • Wee L; Department of Medical Physics, Oslo University Hospital, Oslo, Norway.
  • Appelt AL; MAASTRO (Dept of Radiotherapy), GROW School of Oncology and Developmental Biology, Maastricht University and Maastricht University Medical Centre+, P. Debyelaan 25, 6229, Maastricht, Netherlands.
Diagn Progn Res ; 6(1): 14, 2022 Aug 04.
Article em En | MEDLINE | ID: mdl-35922837
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagn Progn Res Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagn Progn Res Ano de publicação: 2022 Tipo de documento: Article