medwireNews: Using a risk-based approach could help to prioritize mammogram evaluations during periods of decreased capacity, suggests US research published in JAMA Network Open.
“In this population-based cohort study, clinical indication and individual risk factors were associated with cancer detection,” report the researchers, who propose that triaging mammography based on these factors “could result in detecting the most cancers while performing the fewest examinations compared with a non–risk-based approach.”
They collated data on 1,878,924 mammograms – from 898,415 individuals – conducted between 2014 and 2019 at one of 92 facilities participating in the US Breast Cancer Surveillance Consortium, and used classification and regression trees to identify combinations of variables that grouped mammograms into risk groups based on cancer detection rates.
Diana Miglioretti (University of California Davis School of Medicine) and co-workers established five risk groups, ranging from very high to very low risk based on a cancer detection rate per 1000 mammograms of more than 50 to less than 5, respectively.
Patient indications classified as very high or high risk included additional evaluations for an abnormal mammogram or lump regardless of age or personal history of breast cancer and diagnostic work-up of symptoms other than a lump in those with a history of breast cancer. By contrast, among individuals with no breast cancer history, an annual screening mammogram in those aged 50–69 years and any screening mammogram in those younger than 50 years were considered very low risk.
The investigators found that the 12.0% of mammograms in the very high- and high-risk groups accounted for 55.0% of all the detected cancers, while the 44.2% of mammograms in the very low-risk category accounted for 13.1% of detected cancers.
“Radiology facilities could use this risk stratification model to triage individuals for mammography during periods of reduced capacity, such as that which occurred during the start of the COVID-19 pandemic,” suggests the team.
The authors of an accompanying commentary say that “[t]hese data are particularly interesting because all patients were risk stratified instead of the traditional binary assignment of patients into screening and diagnostic categories.”
But they note that this method requires a lot of specific information, such as the personal history of breast cancer and years since diagnosis, and the commentators highlight the impracticalities of asking clinicians or nonclinical scheduling staff to parse such data, especially in a crisis scenario.
Sarah Friedewald and Dipti Gupta (both from Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA) therefore conclude: “[T]hese data should be used with caution as the only barometer for whether a patient merits cancer screening during a period of rationing.
“Finally, the practicality of this process during the COVID-19 pandemic and extrapolation to other emergent settings are less obvious.”
medwireNews is an independent medical news service provided by Springer Healthcare Ltd. © 2021 Springer Healthcare Ltd, part of the Springer Nature Group
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JAMA Netw Open 2021; 4: e211974
JAMA Netw Open 2021; 4: e212546