Impact of Risk Factors on Different Interval Cancer Subtypes in a Population-Based Breast Cancer Screening Programme

Abstract

BackgroundInterval cancers are primary breast cancers diagnosed in women after a negative screening test and before the next screening invitation. Our aim was to evaluate risk factors for interval cancer and their subtypes and to compare the risk factors identified with those associated with incident screen-detected cancers.MethodsWe analyzed data from 645,764 women participating in the Spanish breast cancer screening program from 2000–2006 and followed-up until 2009. A total of 5,309 screen-detected and 1,653 interval cancers were diagnosed. Among the latter, 1,012 could be classified on the basis of findings in screening and diagnostic mammograms, consisting of 489 true interval cancers (48.2%), 235 false-negatives (23.2%), 172 minimal-signs (17.2%) and 114 occult tumors (11.3%). Information on the screening protocol and women's characteristics were obtained from the screening program registry. Cause-specific Cox regression models were used to estimate the hazard ratios (HR) of risks factors for interval cancer and incident screen-detected cancer. A multinomial regression model, using screen-detected tumors as a reference group, was used to assess the effect of breast density and other factors on the occurrence of interval cancer subtypes.ResultsA previous false-positive was the main risk factor for interval cancer (HR = 2.71, 95%CI: 2.28–3.23); this risk was higher for false-negatives (HR = 8.79, 95%CI: 6.24–12.40) than for true interval cancer (HR = 2.26, 95%CI: 1.59–3.21). A family history of breast cancer was associated with true intervals (HR = 2.11, 95%CI: 1.60–2.78), previous benign biopsy with a false-negatives (HR = 1.83, 95%CI: 1.23–2.71). High breast density was mainly associated with occult tumors (RRR = 4.92, 95%CI: 2.58–9.38), followed by true intervals (RRR = 1.67, 95%CI: 1.18–2.36) and false-negatives (RRR = 1.58, 95%CI: 1.00–2.49).ConclusionThe role of women's characteristics differs among interval cancer subtypes. This information could be useful to improve effectiveness of breast cancer screening programmes and to better classify subgroups of women with different risks of developing cancer.

Five-Year Overall Survival of Interval Breast Cancers is Better than Non- Interval Cancers from Korean Breast Cancer Registry

“…The distribution of tumor characteristics between ICs and non-ICs overall was not in full agreement with other literature, with ICs being larger at diagnosis (Komenaka et al, 2004;Brekelmans et al, 2001), of higher grade (Domingo et al, 2010;Kirsh et al, 2011;Meshkat et al, 2015), and displaying more lymph node involvement (Musolino et al, 2012;Blanch et al, 2014) and more often being ER/PR-negative (Damiani et al, 2015;Musolino et al, 2018), HER2-positive (Martín-López et al, 2012;Musolino et al, 2018), or triple negative (Gilliland et al, 2000). As results, these studies have reported that IC tumors had, on average, a poorer prognosis of ICs than those of screening-detected breast cancers (Andersson et al, 1998;Collett et al, 2005;Sihto et al, 2008).…”

“…In concordance with this, Holm et al, (2015) reported a two-fold increase in the odds for ICs among BRCA mutation carriers, in line with our results. Other studies of BRCA mutations have reported a lowered sensitivity of mammography screening for carriers (Brekelmans et al, 2001;Komenaka et al, 2004) Musolino et al, 2012;Blanch et al, 2014) on family history and ICs reports conflicting results, using varying definitions of family history and low patient numbers.…”

The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening

“…10, 11, 14, 39, 45, 51 The detailed findings, summarised in our results, are complex but reveal some common findings. Mammographic density was consistently associated with occult BCs (followed by true interval BCs) rather than missed interval cases, 10, 14 highlighting a likely masking effect.…”

Breast cancer risk is increased in the years following false-positive breast cancer screening

“…Third, to classify BC after FPR as either FNDA or new cancer, we used an algorithm that mainly used location data, which are routinely collected in both the BCR and the screening program. Compared with radiological review of all files, such an algorithm has several important advantages, including avoiding radiologist subjectivity, a much lower workload, and more complete data ( Blanch et al , 2014 ). We cannot exclude that some cases were categorized as FNDA, although these BC were in reality unrelated to the lesion seen at screening, meaning we would overestimate the FNDA rate.…”