Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure

Breast density and risk of breast cancer: masking and detection bias

Breast density is associated with risk of breast cancer (BC) diagnosis, affecting risk prediction tools and patient notification policies. Density affects mammography sensitivity and may influence screening intensity. Therefore, the observed association between density and BC diagnosis may not reflect the relationship between density and disease risk. We investigated the association between breast density and BC risk using data sourced from 33 542 women in the Breast Cancer Surveillance Consortium, 2000-2018. We estimated mammogram sensitivity and rates of screening mammography among dense (Breast Imaging Reporting and Data System [BI-RADS] breast density categories C and D) and nondense (BI-RADS categories A and B) breasts. We used Kaplan-Meier estimates to summarize the relative risks of BC diagnosis (RRdx) by density and fit a natural history model to estimate the relative risks of BC onset (RRonset) given density-specific sensitivities. The RRdx for dense vs nondense breasts was 1.80 (95% CI, 1.46-2.57). Based on estimated screening sensitivities of 0.88 and 0.78 for nondense and dense breasts, respectively, RRonset was 1.73 (95% CI, 1.43-2.25). Sensitivity analyses suggested higher breast density is robustly associated with increased risk of BC onset, similar in magnitude to the increased risk of BC diagnosis. These findings support laws requiring notifications to women with dense breasts of their increased BC risk.

Random effects models of tumour growth for investigating interval breast cancer

In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.

Revisiting the standard blueprint for biomarker development to address emerging cancer early detection technologies

Novel liquid biopsy technologies are creating a watershed moment in cancer early detection. Evidence supporting population screening is nascent, but a rush to market the new tests is prompting cancer early detection researchers to revisit the standard blueprint that the Early Detection Research Network established to evaluate novel screening biomarkers. In this commentary, we review the Early Detection Research Network's Phases of Biomarker Development (PBD) for rigorous evaluation of novel early detection biomarkers and discuss both hazards and opportunities involved in expedited evaluation. According to the PBD, for a biomarker-based test to be considered for population screening, 1) test sensitivity in a prospective screening setting must be adequate, 2) the shift to early curable stages must be meaningful, and 3) any stage shift must translate into clinically significant mortality benefit. In the past, determining mortality benefit has required lengthy randomized screening trials, but interest is growing in expedited trial designs with shorter-term endpoints. Whether and how best to use such endpoints in a manner that retains the rigor of the PBD remains to be determined. We discuss how computational disease modeling can be harnessed to learn about screening impact and meet the needs of the moment.