A simple method to estimate the episode and programme sensitivity of breast cancer screening programmes

Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors

BACKGROUND

Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors.

METHODS

The authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states.

RESULTS

There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively.

CONCLUSION

In risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied.

Modeled residual current cancer risk after clinical investigation of a positive multicancer early detection test result

BACKGROUND

Positive results of a multi-cancer early detection (MCED) test require confirmatory diagnostic workup. Here, residual current cancer risk (RR) during the process of diagnostic resolution, including situations where the initial confirmatory test does not provide resolution, was modeled.

METHODS

A decision-tree framework was used to model conditional risk in a patient's journey through confirmatory diagnostic options and outcomes. The diagnostic journey assumed that cancer signal detection (a positive MCED test result) had already led to a transition from screening to diagnosis and began with an initial positive predictive value (PPV) from the positive result. Evaluation of a most probable (top) predicted cancer signal origin (CSO) and then a second-most probable predicted CSO followed. Under the assumption that the top- and second-predicted CSOs were each followed by a targeted confirmatory test, the RR was estimated for each subsequent scenario.

RESULTS

For an initial MCED test result with typical performance characteristics modeled (PPV, 40%; top-predicted CSO accuracy, 90%), after a negative initial confirmatory test (sensitivity, 70%, 90%, or 100%) the RR ranged from 6% to 20%. A second-predicted CSO (accuracy, 50%), after a negative second confirmatory test, still provided a significant RR (3%-18%) in comparison with the National Institute for Health and Care Excellence-recommended cancer risk threshold warranting investigation in symptomatic individuals (3%). With a 40% PPV for an MCED test and 90% specificity for a confirmatory test, the risk of incidental findings after one or two confirmatory tests was 6% and 12%, respectively.

CONCLUSIONS

These results may illustrate the impact of a positive MCED test on follow-up decision-making.

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

The true sensitivity of a cancer screening test, defined as the frequency with which the test returns a positive result if the cancer is present, is a key indicator of diagnostic performance. Given the challenges of directly assessing test sensitivity in a prospective screening program, proxy measures for true sensitivity are frequently reported. We call one such proxy empirical sensitivity, as it is given by the observed ratio of screen-detected cancers to the sum of screen-detected and interval cancers. In the setting of the canonical three-state Markov model for progression from preclinical onset to clinical diagnosis, we formulate a mathematical relationship for how empirical sensitivity varies with the screening interval and the mean preclinical sojourn time and identify conditions under which empirical sensitivity exceeds or falls short of true sensitivity. In particular, when the inter-screening interval is short relative to the mean sojourn time, empirical sensitivity tends to exceed true sensitivity, unless true sensitivity is high. The Breast Cancer Surveillance Consortium (BCSC) has reported an estimate of 0.87 for the empirical sensitivity of digital mammography. We show that this corresponds to a true sensitivity of 0.82 under a mean sojourn time of 3.6 years estimated based on breast cancer screening trials. However, the BCSC estimate of empirical sensitivity corresponds to even lower true sensitivity under more contemporary, longer estimates of mean sojourn time. Consistently applied nomenclature that distinguishes empirical sensitivity from true sensitivity is needed to ensure that published estimates of sensitivity from prospective screening studies are properly interpreted.

Estimating Cancer Screening Sensitivity and Specificity Using Healthcare Utilization Data: Defining the Accuracy Assessment Interval

The effectiveness and efficiency of cancer screening in real-world settings depend on many factors, including test sensitivity and specificity. Outside of select experimental studies, not everyone receives a gold standard test that can serve as a comparator in estimating screening test accuracy. Thus, many studies of screening test accuracy use the passage of time to infer whether or not cancer was present at the time of the screening test, particularly for patients with a negative screening test. We define the accuracy assessment interval as the period of time after a screening test that is used to estimate the test's accuracy. We describe how the length of this interval may bias sensitivity and specificity estimates. We call for future research to quantify bias and uncertainty in accuracy estimates and to provide guidance on setting accuracy assessment interval lengths for different cancers and screening modalities.

A simple way to measure the burden of interval cancers in breast cancer screening

Background

The sensitivity of a mammography program is normally evaluated by comparing the interval cancer rate to the expected breast cancer incidence without screening, i.e. the proportional interval cancer rate (PICR). The expected breast cancer incidence in absence of screening is, however, difficult to estimate when a program has been running for some time. As an alternative to the PICR we propose the interval cancer ratio . We validated this simple measure by comparing it with the traditionally used PICR.

Method

We undertook a systematic review and included studies: 1) covering a service screening program, 2) women aged 50-69 years, 3) observed data, 4) interval cancers, women screened, or interval cancer rate, screen detected cases, or screen detection rate, and 5) estimated breast cancer incidence rate of background population. This resulted in 5 papers describing 12 mammography screening programs.

Results

Covering initial screens only, the ICR varied from 0.10 to 0.28 while the PICR varied from 0.22 to 0.51. For subsequent screens only, the ICR varied from 0.22 to 0.37 and the PICR from 0.28 to 0.51. There was a strong positive correlation between the ICR and the PICR for initial screens (r = 0.81), but less so for subsequent screens (r = 0.65).

Conclusion

This alternate measure seems to capture the burden of interval cancers just as well as the traditional PICR, without need for the increasingly difficult estimation of background incidence, making it a more accessible tool when evaluating mammography screening program performance.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-782) contains supplementary material, which is available to authorized users.

Co-expression of Oct-4 and Nestin in human breast cancers

The aim is to investigate the clinical implications of the Oct-4 and Nestin protein in human breast cancers. A total of 346 cases including 26 fresh and 320 paraffin-embedded tumor tissues were selected for characterizing the frequency of CD44(+)CD24(-) tumor cells by flow cytometry and the differential expression of the stem cell-related genes between CD44(+)CD24(-) and non-CD44(+)CD24(-) tumor cells was analyzed by PCR Array and immunofluorescence. In comparison with the non-CD44(+)CD24(-) tumor cells, the CD44(+)CD24(-), particularly for those with high percentage of Oct-4(+) and Nestin(+), tumor cells had higher tumorigenicity by forming mammospheres in vitro. More importantly, 42 (13.125%) out of 320 tumor tissues were positive for Oct-4 and Nestin staining. Universal analysis and multivariate analysis revealed that the expression of Oct-4 and Nestin was associated significantly with younger age, pathogenic degrees, lymph node metastasis and triple-negative breast cancer independently (P < 0.05) as well as shorter survival (P = 0.001). Oct-4 and Nestin were important regulators of the development of breast cancer, and Oct-4 and Nestin may be used as predictors for the prognosis of breast cancers.