ArticleBMJ open2017
Categorising cancers to enable tailored care planning through a secondary analysis of cancer registration data in the UK.
Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Finding my tribe: a qualitative interview study of how people living with metastatic breast cancer perceive support groups.Journal of cancer survivorship : research and practice · 2026Article
- Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Improving personalised care and support planning for people living with treatable-but-not-curable cancer.BMJ open quality · 2023Article
- Clinicians' and Patients' Perceptions and Use of the Word "Cured" in Cancer Care: An Italian Survey.Current oncology (Toronto, Ont.) · 2023Article
- Article
- Current developments in delivering customized care: a scoping review.BMC health services research · 2021Article
- Treatable but not curable cancer in England: a retrospective cohort study using cancer registry data and linked data sets.BMJ open · 2021Article
- "This Graft-vs.-Host Disease Determines My Life. That's It."-A Qualitative Analysis of the Experiences and Needs of Allogenic Hematopoietic Stem Cells Transplantation Survivors in Germany.Frontiers in public health · 2021Article
- Personalized Cancer Follow-Up Care Pathways: A Delphi Consensus of Research Priorities.Journal of the National Cancer Institute · 2020Article
- Implementing personalized pathways for cancer follow-up care in the United States: Proceedings from an American Cancer Society-American Society of Clinical Oncology summit.CA: a cancer journal for clinicians · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study is to categorise cancers into broad groups based on clusters of common treatment aims, experiences and outcomes to provide a numerical framework for understanding the services required to meet the needs of people with different cancers. This framework will enable a high-level overview of care and support requirements for the whole cancer population. SETTING AND
participantsPeople in the UK with 1 of 20 common cancers; an estimated 309 000 diagnoses in 2014, 1 679 000 people diagnosed in a 20-year period and still living in 2010 and 135 000 cancer deaths in 2014. PRIMARY AND SECONDARY OUTCOME MEASURES: Survival and stage at diagnosis data were reviewed alongside clinically led assumptions to identify commonalities and cluster cancer types into three groups. The three cancer groups were then described using incidence, prevalence and mortality data collected and reported by UK cancer registries. This was then reviewed, validated and refined following consultation.
resultsGroup 1 includes cancers with the highest survival; 5-year survival is over 80%. Group 3 cancers have shorter term survival. Five-year survival is not >20% for any cancer in this group and many do not survive over a year. Group 2 includes cancers where people typically live more than a year but are less likely to live >5 years. We estimate that the majority (64%) of people living with cancer (20 year prevalence) have a cancer type in group 1 'longer term survival', but significant minorities of people have cancers in group 2 'intermediate survival' (19%) and group 3 'shorter term survival' (10%).
conclusionsEvery person with cancer has unique needs shaped by a multitude of factors including comorbidities, treatment regimens, patient preferences, needs, attitudes and behaviours. However, to deliver personalised care, there needs to be a high-level view of potential care requirements to support service planning.
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