Is mammography screening history a predictor of future breast cancer risk?

Inspired by the model by Walter and Day for risk of cervical cancer following negative screens, one might hypothesize that women in a mammography screening programme with a certain number of negative screens had a lower remaining breast cancer risk than that of women in general. We studied whether number of negative screens was a predictor for a low remaining breast cancer risk in women participating in the mammography screening programmes in Stockholm, Copenhagen and Funen. Data were collected from the mammography screening programmes in Stockholm, Sweden (1989-2012), Copenhagen, Denmark (1991-2009) and Funen, Denmark (1993-2009), and linked to the respective cancer registries. We calculated cumulative hazard rates for breast cancer in women in cohorts defined by age at entry and number of negative screens for the maximum follow-up period in each screening centre. For all centres and cohorts, the cumulative hazard were parallel for all number of negative screens, from after the time, when the women were scheduled to be invited for the next screen. This means that the remaining breast cancer risk is similar no matter how many negative screens a woman have had. Number of negative screens was not a predictor of a low remaining breast cancer risk in women participating in the mammography screening programmes in Stockholm, Sweden, Copenhagen and Funen, Denmark. The history of previous negative screens is therefore not suitable for personalisation of mammography screening.

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 ICR=intervalcancersintervalcancers+screendetectedcancers. 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.

Participation behaviour following a false positive test in the Copenhagen mammography screening programme.

INTRODUCTION: There is an ongoing debate concerning possible disadvantages of mammography screening, one being the consequence of receiving a false positive test-result. It is argued that receiving a false positive answer may have short- and/or long-term adverse psychological effects on women, but results from different studies are conflicting. We tested if there was a difference in continued participation behaviour between the group of women who have been subject to a false positive result and those who have not. MATERIAL AND METHODS: The study used the registers from the first six invitation rounds of the mammography screening programme in Copenhagen (1991-2003). We estimated the relative risk of not participating in the subsequent screening round for women with a false positive test using women with a negative test as baseline. As outcome measure odds ratios (OR) with 95% confidence intervals (CI) were used. RESULTS: There was no significant difference in participation in the subsequent round between women with a false positive test and women with a negative test. The proportion of screens resulting in false positive answers, both after assessment and after surgery, decreased from 5.54% in Round 1 to 1.79% in Round 5. Participation in the subsequent screening round was well above 80% in all five screening rounds. DISCUSSION: Our results showed that women experiencing a false positive test at mammography screening participated in the subsequent screening round to the same extent as did women experiencing a negative screening test, regardless of whether the false positive statement was given following assessment or following surgery. The benign to malignant biopsy ratio, comparing the type B false positives to the true positives, was by the fifth round well below the desirable level of

Measuring the burden of interval cancers in long-standing screening mammography programmes.

OBJECTIVES: Mammography screening programme sensitivity is evaluated by comparing the interval cancer rate (ICR) with the expected breast cancer incidence without screening, ie. the proportional interval cancer rate (PICR). The PICR is usually found by extrapolating pre-screening incidence rates, whereas ICR is calculated from data available in the screening programmes. As there is no consensus regarding estimation of background incidence, we seek to validate the ICR measure against the PICR. METHODS: Screening data from the three mammography screening programmes of Stockholm, Copenhagen, and Funen in the period 1989-2011 provided data to calculate the ICR. The most commonly described methods of extrapolating pre-screening incidence rates to calculate the PICR were illustrated and PICRs were calculated by year and programme using these different methods and compared with the ICRs. RESULTS: PICRs varied greatly, reaching a difference of 32-34% in Stockholm, 79% in Copenhagen, and 100-106% in Funen between the highest and the lowest value, depending on which method was applied. PICRs exhibited large variations yearly and from programme to programme. ICRs did not vary to the same extent, ranging on average from 0.100 to 0.136 in the first 12-months and between 0.201 and 0.225 in the last 12-months of the two-year period after a negative screen across the three programmes. CONCLUSION: The value of the PICR is hugely influenced by which method is applied, whereas the ICR is calculated purely on data available within programmes. We find that the PICR, the establishing indicator for sensitivity, could preferably be replaced by the ICR.