Abstract
BACKGROUND: Individualised breast cancer risk prediction models may be key for planning risk-based screening approaches. Our aim was to conduct a systematic review and quality assessment of these models addressed to women in the general population.
METHODS: We followed the Cochrane Collaboration methods searching in Medline, EMBASE and The Cochrane Library databases up to February 2018. We included studies reporting a model to estimate the individualised risk of breast cancer in women in the general population. Study quality was assessed by two independent reviewers. Results are narratively summarised.
RESULTS: We included 24 studies out of the 2976 citations initially retrieved. Twenty studies were based on four models, the Breast Cancer Risk Assessment Tool (BCRAT), the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, and the International Breast Cancer Intervention Study (IBIS), whereas four studies addressed other original models. Four of the studies included genetic information. The quality of the studies was moderate with some limitations in the discriminative power and data inputs. A maximum AUROC value of 0.71 was reported in the study conducted in a screening context.
CONCLUSION: Individualised risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity.
| Original language | English |
|---|---|
| Pages (from-to) | 76-85 |
| Number of pages | 10 |
| Journal | British Journal of Cancer |
| Volume | 121 |
| Issue number | 1 |
| Early online date | 22 May 2019 |
| DOIs | |
| Publication status | Published - 2 Jul 2019 |
Keywords
- breast neoplasms/etiology
- female
- humans
- quality assurance, health care
- ROC curve
- risk
- risk assessment
ASJC Scopus subject areas
- Oncology
- Cancer Research
Documents and Links
- Louro, J. et al (2019) A systematic review and quality assessment of individualised breast cancer risk prediction models
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