Harnessing population-wide health data to predict cancer risk
Authors: Mattias Johansson, Hilary A Robbins
Journal: The Lancet Digital Health (2024)

The use of statistical models to estimate a person's disease risk (ie, risk models) has important advantages in allowing a physician—or a patient themselves—to consider the joint influence of multiple factors on disease risk.1Murray CJ Ezzati M Lopez AD Rodgers A Vander Hoorn S Comparative quantification of health risks: conceptual framework and methodological issues.Popul Health Metr. 2003; 1: 1Crossref PubMed Scopus (304) Google Scholar Risk models are routinely used in certain areas of medicine, including in cardiology to guide prevention of atherosclerotic cardiovascular disease.2Tokgozoglu L Torp-Pedersen C Redefining cardiovascular risk prediction: is the crystal ball clearer now?.Eur Heart J. 2021; 42: 2468-2471Crossref PubMed Scopus (13) Google Scholar Risk models are, however, seldom used in clinical practice for cancer prevention. Risk models are frequently trained using risk factor information from volunteer research participants in cohort studies—a powerful approach, albeit often limited by small sample size and healthy-volunteer bias. In The Lancet Digital Health, Alexander W Jung and colleagues3Jung AW Holm PC Gaurav K et al.Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study.Lancet Digit Health. 2024; 6: e396-e406Google Scholar circumvented these limitations using real-world electronic health records (EHRs) from 6·7 million individuals in Denmark to develop risk models for 20 types of cancer. Models used risk indicators extracted from EHRs (eg, ICD codes), health factors from physician notes, family disease histories, and registry-based cancer diagnoses occurring during 1995–2014. The study is unique in that virtually everyone in the country was included, and it represents a massive effort to link and harmonise data to train the models, as well as to render them interpretable. The performance of the models was evaluated internally, using Danish cancer diagnoses from 2015 to 2018, and externally, in the UK Biobank cohort. Internal average model discrimination was good, with a concordance index of 0·81 (SD 0·08), which was reduced to 0·5–0·6 for most cancers when discounting the effect of age and sex. Among 350 000 volunteers aged 50–75 years from the UK Biobank, the average concordance index was 0·66 (SD 0·08), which was similar but slightly lower than in the Danish data when restricted to the same age bracket. Internal model calibration was good, whereas risk was slightly over-predicted in the UK Biobank cohort. These results highlight the central effect of age in predicting cancer risk, and the importance of comparing risk models within the same study population. Detailed information on behavioural risk factors was not available in the EHRs, and discrimination in the UK Biobank for cancers with strong behavioural risk factors seemed to be lower than in previously published models based on questionnaire data.4Kachuri L Graff RE Smith-Byrne K et al.Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction.Nat Commun. 2020; 116084Crossref Scopus (78) Google Scholar Over-prediction was notable for lung cancer, which probably reflects differences between the Danish and UK Biobank populations in the intensity and duration of smoking exposure. This highlights a limitation of the approach and suggests that better model performance might be achieved if EHR and behavioural risk data could be combined. A potential future direction is to apply methods that allow incorporation of risk factors whose effects were established in separate studies.5Pal Choudhury P Maas P Wilcox A et al.iCARE: an R package to build, validate and apply absolute risk models.PLoS One. 2020; 15e0228198Crossref PubMed Scopus (48) Google Scholar As for any prediction tool, a wealth of translational research would be needed before patients could benefit from this work. The authors' suggestion to use risk models to inform use of emerging blood-based multi-cancer early detection tests6Rubinstein WS Patriotis C Dickherber A et al.Cancer screening with multicancer detection tests: a translational science review.CA Cancer J Clin. 2024; (published online March 22.)https://doi.org/10.3322/caac.21833Crossref PubMed Scopus (1) Google Scholar is supported by a study that modelled the influence of total cancer risk on the positive and negative predictive values of multi-cancer early detection tests.7Kim ES Scharpf RB Garcia-Closas M Visvanathan K Velculescu VE Chatterjee N Potential utility of risk stratification for multicancer screening with liquid biopsy tests.NPJ Precis Oncol. 2023; 7: 39Crossref PubMed Scopus (0) Google Scholar A necessary additional step for this use case would be to extend the modelling framework to also provide total cancer risk estimates.7Kim ES Scharpf RB Garcia-Closas M Visvanathan K Velculescu VE Chatterjee N Potential utility of risk stratification for multicancer screening with liquid biopsy tests.NPJ Precis Oncol. 2023; 7: 39Crossref PubMed Scopus (0) Google Scholar Another application for risk models based on routine health data would be to automatically provide risk estimates in medical records. If coupled with carefully developed guidelines specifying recommended actions, this could be of use for the attending physician. One reason for caution is that the strongest risk indicator for individual cancer types in the models proposed by Jung and colleagues might often represent misdiagnosis of the cancer itself, one of several examples being "carcinoma in situ of breast" for breast cancer. Because risk estimates were updated throughout the follow-up, it is unclear how long before clinical diagnosis the models are able to signal elevated risk, which is an important consideration for risk stratified early detection strategies. The study from Jung and colleagues raises questions. Are the models transportable to other settings with different registry and health-care systems? Could the use of EHR-based risk models exacerbate inequalities,8Dent THS Wright CF Stephan BCM Brayne C Janssens ACJW Risk prediction models: a framework for assessment.Public Health Genomics. 2012; 15: 98-105Crossref PubMed Google Scholar considering that assignment of ICD codes requires a visit to a health provider, which might in turn trigger provision of additional health care? A central question is whether this type of large-scale health information can be handled with sufficient attention to ethical considerations, privacy, and data protection to ensure that patients are not harmed.9Institute of Medicine (US) Roundtable on Value & Science-Driven Health CareClinical data as the basic staple of health learning: creating and protecting a public good. Workshop Summary.https://www.ncbi.nlm.nih.gov/books/NBK54293/Date: 2010Date accessed: April 30, 2024Google Scholar In an era of big data and efficient computing, the development of complex risk models has become substantially easier than assessing whether, how, and in what context they are useful. The 1976 observation by George E P Box that "All models are wrong but some are useful"10Box GEPScience and statistics.J Am Stat Assoc. 1976; 71: 791-799Crossref Scopus (1368) Google Scholar remains equally true nearly 50 years later. Only time will tell whether the impressive advances since Box's observation in our ability to build risk models will ultimately prove useful for cancer prevention. We declare no competing interests. The authors alone are responsible for the views expressed in this Article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization. Multi-cancer risk stratification based on national health data: a retrospective modelling and validation studyData available in national electronic health databases can be used to approximate cancer risk factors and enable risk predictions in most cancer types. Model predictions generalise between the Danish and UK health-care systems. With the emergence of multi-cancer early detection tests, electronic health record-based risk models could supplement screening efforts. Full-Text PDF Open Access