Hide metadata

dc.contributor.authorTheophanous, Stelios
dc.contributor.authorLønne, Per-Ivar
dc.contributor.authorChoudhury, Ananya
dc.contributor.authorBerbee, Maaike
dc.contributor.authorDekker, Andre
dc.contributor.authorDennis, Kristopher
dc.contributor.authorDewdney, Alice
dc.contributor.authorGambacorta, Maria A.
dc.contributor.authorGilbert, Alexandra
dc.contributor.authorGuren, Marianne G.
dc.contributor.authorHolloway, Lois
dc.contributor.authorJadon, Rashmi
dc.contributor.authorKochhar, Rohit
dc.contributor.authorMohamed, Ahmed A.
dc.contributor.authorMuirhead, Rebecca
dc.contributor.authorParés, Oriol
dc.contributor.authorRaszewski, Lukasz
dc.contributor.authorRoy, Rajarshi
dc.contributor.authorScarsbrook, Andrew
dc.contributor.authorSebag-Montefiore, David
dc.contributor.authorSpezi, Emiliano
dc.contributor.authorSpindler, Karen-Lise G.
dc.contributor.authorvan Triest, Baukelien
dc.contributor.authorVassiliou, Vassilios
dc.contributor.authorMalinen, Eirik
dc.contributor.authorWee, Leonard
dc.contributor.authorAppelt, Ane L.
dc.date.accessioned2022-08-09T05:03:23Z
dc.date.available2022-08-09T05:03:23Z
dc.date.issued2022
dc.identifier.citationDiagnostic and Prognostic Research. 2022 Aug 04;6(1):14
dc.identifier.urihttp://hdl.handle.net/10852/94876
dc.description.abstractBackground 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.
dc.language.isoeng
dc.rightsThe Author(s); licensee BioMed Central Ltd.
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDevelopment and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study
dc.typeJournal article
dc.date.updated2022-08-09T05:03:24Z
dc.creator.authorTheophanous, Stelios
dc.creator.authorLønne, Per-Ivar
dc.creator.authorChoudhury, Ananya
dc.creator.authorBerbee, Maaike
dc.creator.authorDekker, Andre
dc.creator.authorDennis, Kristopher
dc.creator.authorDewdney, Alice
dc.creator.authorGambacorta, Maria A.
dc.creator.authorGilbert, Alexandra
dc.creator.authorGuren, Marianne G.
dc.creator.authorHolloway, Lois
dc.creator.authorJadon, Rashmi
dc.creator.authorKochhar, Rohit
dc.creator.authorMohamed, Ahmed A.
dc.creator.authorMuirhead, Rebecca
dc.creator.authorParés, Oriol
dc.creator.authorRaszewski, Lukasz
dc.creator.authorRoy, Rajarshi
dc.creator.authorScarsbrook, Andrew
dc.creator.authorSebag-Montefiore, David
dc.creator.authorSpezi, Emiliano
dc.creator.authorSpindler, Karen-Lise G.
dc.creator.authorvan Triest, Baukelien
dc.creator.authorVassiliou, Vassilios
dc.creator.authorMalinen, Eirik
dc.creator.authorWee, Leonard
dc.creator.authorAppelt, Ane L.
dc.identifier.doihttps://doi.org/10.1186/s41512-022-00128-8
dc.identifier.urnURN:NBN:no-97403
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/94876/1/41512_2022_Article_128.pdf
dc.type.versionPublishedVersion
cristin.articleid14


Files in this item

Appears in the following Collection

Hide metadata

Attribution 4.0 International
This item's license is: Attribution 4.0 International