Evaluation of Prognostic and Predictive Models in the Oncology Clinic.

Life cycle Model Oncology Personalised Prediction Radiotherapy

Journal

Clinical oncology (Royal College of Radiologists (Great Britain))
ISSN: 1433-2981
Titre abrégé: Clin Oncol (R Coll Radiol)
Pays: England
ID NLM: 9002902

Informations de publication

Date de publication:
02 2022
Historique:
received: 03 09 2021
revised: 19 11 2021
accepted: 25 11 2021
pubmed: 20 12 2021
medline: 28 1 2022
entrez: 19 12 2021
Statut: ppublish

Résumé

Predictive and prognostic models hold great potential to support clinical decision making in oncology and could ultimately facilitate a paradigm shift to a more personalised form of treatment. While a large number of models relevant to the field of oncology have been developed, few have been translated into clinical use and assessment of clinical utility is not currently considered a routine part of model development. In this narrative review of the clinical evaluation of prediction models in oncology, we propose a high-level process diagram for the life cycle of a clinical model, encompassing model commissioning, clinical implementation and ongoing quality assurance, which aims to bridge the gap between model development and clinical implementation.

Identifiants

pubmed: 34922799
pii: S0936-6555(21)00441-6
doi: 10.1016/j.clon.2021.11.022
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

102-113

Subventions

Organisme : Cancer Research UK
ID : C147/A25254
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C1994/A28701
Pays : United Kingdom

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auteurs

M Craddock (M)

University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK. Electronic address: matthew.craddock@postgrad.manchester.ac.uk.

C Crockett (C)

Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK.

A McWilliam (A)

University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK.

G Price (G)

University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK.

M Sperrin (M)

Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.

S N van der Veer (SN)

Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.

C Faivre-Finn (C)

University of Manchester, Radiotherapy Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Manchester, UK; Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK.

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Classifications MeSH