The impact of height and weight on rescreening rates within a population-based breast screening program.

INTRODUCTION: Women with obesity are at increased risk of post-menopausal breast cancer and less likely to participate in breast screening. This study investigates the impact of asking women their height and weight within a population-based screening program, and the association of BMI with rescreening status. METHODS: Data regarding 666,130 screening events from 318,198 women aged 50-74 attending BreastScreen Western Australia between 2016 and 2021 were used to compare crude and age-standardised rescreening rates over time. Mixed effects logistic regression was used to investigate associations of BMI with rescreening status. RESULTS: Rescreening rates for women screened since 2016 were within 1.8% points from the previous reporting period, stratified by screening round. Increasing BMI was associated with decreased likelihood of returning to breast screening (OR = 0.993, 95% CI: 0.988-0.998; OR = 0.989, 95% CI: 0.984-0.994; OR = 0.985, 95% CI: 0.982-0.987 for women screening for the first, second and third+ time, respectively). CONCLUSIONS: This large, prospective study supports implementation of routine height and weight collection within breast screening programs. It shows that asking women their height and weight does not deter them from returning to screening and that women with increased BMI are less likely to rescreen, highlighting a need for targeted interventions to improve screening barriers for women living with obesity.

Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection.

BACKGROUND: Artificial intelligence (AI) has been proposed to reduce false-positive screens, increase cancer detection rates (CDRs), and address resourcing challenges faced by breast screening programs. We compared the accuracy of AI versus radiologists in real-world population breast cancer screening, and estimated potential impacts on CDR, recall and workload for simulated AI-radiologist reading. METHODS: External validation of a commercially-available AI algorithm in a retrospective cohort of 108,970 consecutive mammograms from a population-based screening program, with ascertained outcomes (including interval cancers by registry linkage). Area under the ROC curve (AUC), sensitivity and specificity for AI were compared with radiologists who interpreted the screens in practice. CDR and recall were estimated for simulated AI-radiologist reading (with arbitration) and compared with program metrics. FINDINGS: The AUC for AI was 0.83 compared with 0.93 for radiologists. At a prospective threshold, sensitivity for AI (0.67; 95% CI: 0.64-0.70) was comparable to radiologists (0.68; 95% CI: 0.66-0.71) with lower specificity (0.81 [95% CI: 0.81-0.81] versus 0.97 [95% CI: 0.97-0.97]). Recall rate for AI-radiologist reading (3.14%) was significantly lower than for the BSWA program (3.38%) (-0.25%; 95% CI: -0.31 to -0.18; P < 0.001). CDR was also lower (6.37 versus 6.97 per 1000) (-0.61; 95% CI: -0.77 to -0.44; P < 0.001); however, AI detected interval cancers that were not found by radiologists (0.72 per 1000; 95% CI: 0.57-0.90). AI-radiologist reading increased arbitration but decreased overall screen-reading volume by 41.4% (95% CI: 41.2-41.6). INTERPRETATION: Replacement of one radiologist by AI (with arbitration) resulted in lower recall and overall screen-reading volume. There was a small reduction in CDR for AI-radiologist reading. AI detected interval cases that were not identified by radiologists, suggesting potentially higher CDR if radiologists were unblinded to AI findings. These results indicate AI's potential role as a screen-reader of mammograms, but prospective trials are required to determine whether CDR could improve if AI detection was actioned in double-reading with arbitration. FUNDING: National Breast Cancer Foundation (NBCF), National Health and Medical Research Council (NHMRC).

Reflections from Women with an Interval Breast Cancer Diagnosis: A Qualitative Analysis of Open Disclosure in the BreastScreen Western Australia Program.

BACKGROUND: 'Interval breast cancer' describes a malignancy that is diagnosed after a negative screening mammogram. Open disclosure is a process of addressing a negative health outcome that includes an apology and an opportunity for the client to discuss concerns. BreastScreen Western Australia has implemented a policy of open disclosure. The purpose of this study was to gain an understanding of clients' experience with interval cancer and their attitude towards the screening programme by conducting a thematic analysis of written responses from women participating in the open disclosure process. METHODS: Women experiencing an interval cancer diagnosis between 2011 and 2020 were sent a questionnaire by mail. It included two broad questions with free-text responses. A qualitative analysis of the responses was conducted using an inductive approach. Responses were de-identified and data were thematically analysed and presented using verbatim quotations. RESULTS: Five themes emerged in response to "what could we have done better?": 'nothing,' 'broaden scope,' 'service delivery,' 'breast density education' and 'more education' generally. Six themes emerged in response to "what did we do well?": 'staffing,' 'overall satisfaction,' 'reminders,' 'follow-up after interval cancer,' 'efficiency' and 'information and education provision.' An additional theme of 'storytelling' emerged from both questions: an opportunity for the woman to share her experience of cancer. CONCLUSION: Most women expressed positive attitudes towards the service and appreciated giving feedback in the open disclosure process. Several themes supporting the role of BreastScreen in education were identified, including providing information about breast density, breast health, and limitations of screening.

Evidence from a BreastScreen cohort does not support a longer inter-screen interval in women who have no conventional risk factors for breast cancer.

OBJECTIVES: To determine screening outcomes in women who have no recorded risk factors for breast cancer. METHODS: A retrospective population-based cohort study included all 1,026,137 mammography screening episodes in 323,082 women attending the BreastScreen Western Australia (part of national biennial screening) program between July 2007 and June 2017. Cancer detection rates (CDR) and interval cancer rates (ICR) were calculated in screening episodes with no recorded risk factors for breast cancer versus at least one risk factor stratified by age. CDR was further stratified by timeliness of screening (<27 versus ≥27 months); ICR was stratified by breast density. RESULTS: Amongst 566,948 screens (55.3%) that had no recorded risk factors, 2347 (40.9%) screen-detected cancers were observed. In screens with no risk factors, CDR was 50 (95%CI 48-52) per 10,000 screens and ICR was 7.9 (95%CI 7.4-8.4) per 10,000 women-years, estimates that were lower than screens with at least one risk factor (CDR 83 (95%CI 80-86) per 10,000 screens, ICR 12.2 (95%CI 11.5-13.0) per 10,000 women-years). Compared to timely screens with risk factors, delayed screens with no risk factors had similar CDR across all age groups and a higher proportion of node positive cancers (26.1% vs 20.7%). ICR was lowest in screens that had no risk factors nor dense breasts in all age groups. CONCLUSIONS: Majority of screens had no recorded breast cancer risk factors, hence a substantial proportion of screen-detected cancers occur in these screening episodes. Our findings may not justify less frequent screening in women with no risk factors.

The impact of breast density notification on rescreening rates within a population-based mammographic screening program.

BACKGROUND: High participation in mammographic screening is essential for its effectiveness to detect breast cancers early and thereby, improve breast cancer outcomes. Breast density is a strong predictor of breast cancer risk and significantly reduces the sensitivity of mammography to detect the disease. There are increasing mandates for routine breast density notification within mammographic screening programs. It is unknown if breast density notification impacts the likelihood of women returning to screening when next due (i.e. rescreening rates). This study investigates the association between breast density notification and rescreening rates using individual-level data from BreastScreen Western Australia (WA), a population-based mammographic screening program. METHODS: We examined 981,705 screening events from 311,656 women aged 40+ who attended BreastScreen WA between 2008 and 2017. Mixed effect logistic regression was used to investigate the association between rescreening and breast density notification status. RESULTS: Results were stratified by age (younger, targeted, older) and screening round (first, second, third+). Targeted women screening for the first time were more likely to return to screening if notified as having dense breasts (Percentunadjusted notified vs. not-notified: 57.8% vs. 56.1%; Padjusted = 0.016). Younger women were less likely to rescreen if notified, regardless of screening round (all P < 0.001). There was no association between notification and rescreening in older women (all P > 0.72). CONCLUSIONS: Breast density notification does not deter women in the targeted age range from rescreening but could potentially deter younger women from rescreening. These results suggest that all breast density notification messaging should include information regarding the importance of regular mammographic screening to manage breast cancer risk, particularly for younger women. These results will directly inform BreastScreen programs in Australia as well as other population-based screening providers outside Australia who notify women about breast density or are considering implementing breast density notification.

Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection.

INTRODUCTION: Artificial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval cancers, without contributing substantially to overdiagnosis. Studies suggesting that AI has comparable or greater accuracy than radiologists commonly employ 'enriched' datasets in which cancer prevalence is higher than in population screening. Routine screening outcome metrics (cancer detection and recall rates) cannot be estimated from these datasets, and accuracy estimates may be subject to spectrum bias which limits generalisabilty to real-world screening. We aim to address these limitations by comparing the accuracy of AI and radiologists in a cohort of consecutive of women attending a real-world population breast cancer screening programme. METHODS AND ANALYSIS: A retrospective, consecutive cohort of digital mammography screens from 109 000 distinct women was assembled from BreastScreen WA (BSWA), Western Australia's biennial population screening programme, from November 2016 to December 2017. The cohort includes 761 screen-detected and 235 interval cancers. Descriptive characteristics and results of radiologist double-reading will be extracted from BSWA outcomes data collection. Mammograms will be reinterpreted by a commercial AI algorithm (DeepHealth). AI accuracy will be compared with that of radiologist single-reading based on the difference in the area under the receiver operating characteristic curve. Cancer detection and recall rates for combined AI-radiologist reading will be estimated by pairing the first radiologist read per screen with the AI algorithm, and compared with estimates for radiologist double-reading. ETHICS AND DISSEMINATION: This study has ethical approval from the Women and Newborn Health Service Ethics Committee (EC00350) and the Curtin University Human Research Ethics Committee (HRE2020-0316). Findings will be published in peer-reviewed journals and presented at national and international conferences. Results will also be disseminated to stakeholders in Australian breast cancer screening programmes and policy makers in population screening.

Screening outcomes by risk factor and age: evidence from BreastScreen WA for discussions of risk-stratified population screening.

OBJECTIVES: To estimate rates of screen-detected and interval breast cancers, stratified by risk factor, to inform discussions of risk-stratified population screening. DESIGN: Retrospective population-based cohort study; analysis of routinely collected BreastScreen WA program clinical and administrative data. SETTING, PARTICIPANTS: All BreastScreen WA mammography screening episodes for women aged 40 years or more during 1 July 2007 - 30 June 2017. MAIN OUTCOME MEASURES: Cancer detection rate (CDR) and interval cancer rate (ICR), by risk factor. RESULTS: A total of 323 082 women were screened in 1 026 137 screening episodes (mean age, 58.5 years; SD, 8.6 years). The overall CDR was 68 (95% CI, 67-70) cancers per 10 000 screens, and the overall ICR was 9.7 (95% CI, 9.2-10.1) cancers per 10 000 women-years. Interactions between the effects on CDR of age group and five risk factors were statistically significant: personal history of breast cancer (P = 0.039), family history of breast cancer (P = 0.005), risk-relevant benign conditions (P = 0.012), hormone-replacement therapy (P = 0.002), and self-reported symptoms (P < 0.001). The influence of these risk factors (except personal history) increased with age. For ICR, only the interaction between age and hormone-replacement therapy was significant (P < 0.001), although weak interactions between age and family history of breast cancer or having dense breasts were noted (each P = 0.07). The influence of family history on ICR was significant only for women aged 40-49 years. CONCLUSIONS: Screening CDR and (for some risk factors) ICR were higher for women in some age groups with personal histories of breast cancer or risk-relevant benign breast conditions or first degree family history of breast cancer, women with dense breasts or self-reported breast-related symptoms, and women using hormone-replacement therapy. Our findings could inform the evaluation of risk-based screening.

Impact of the age expansion of breast screening on screening uptake and screening outcomes among older women in BreastScreen western.

OBJECTIVES: To assess the impact of age expansion of screening (EOS) of the target age group from 50 to 69 to 50-74 in Australia, which began mid-2013, by examining screening uptake and outcomes of older women, and by identifying factors associated with continuing screening after reaching the age of 75 years. METHODS: Retrospective study using data from women aged 65+ who attended BreastScreen Western Australia between 2010 and 2017 for free mammograms. Screening uptake and screening outcomes were calculated for the periods before (2010-2012) and after (2015-2017) the age EOS to women aged 70-74. Logistic regression was used to identify variables associated with continuing screening after reaching age 75 years, while controlling for possible confounding variables. RESULTS: Age EOS increased screening uptake amongst women aged 70-74 b y 36% and amongst women ≥75 years by 3% while screening uptake in women aged 65-69 decreased by 3%. Rate of invasive screened-detected cancers significantly decreased among women aged 70-74 from 11.4/1000 screens before to 8.1/1000 screens after age EOS. Likelihood of continuing screening into age ≥75 years was higher in women who had a personal history or a family history of breast cancer, or used hormone replacement therapy within six months of screening. Women who were born outside Australia were less likely to continue screening after reaching age 75 years. CONCLUSIONS: Our study found that age EOS to women aged 70-74 was effective in increasing screening uptake in this age-group but was accompanied by a moderate increase in screening uptake amongst women ≥75 years via self-referral for whom potential benefit of screening may be limited.

The distribution and determinants of mammographic density measures in Western Australian aboriginal women.

BACKGROUND: Mammographic density (MD) is an established risk factor for breast cancer. There are significant ethnic differences in MD measures which are consistent with those for corresponding breast cancer risk. This is the first study investigating the distribution and determinants of MD measures within Aboriginal women of Western Australia (WA). METHODS: Epidemiological data and mammographic images were obtained from 628 Aboriginal women and 624 age-, year of screen-, and screening location-matched non-Aboriginal women randomly selected from the BreastScreen Western Australia database. Women were cancer free at the time of their mammogram between 1989 and 2014. MD was measured using the Cumulus software. Kolmogorov-Smirnov tests were used to compare distributions of absolute dense area (DA), precent dense area (PDA), non-dense area (NDA) and total breast area between Aboriginal and non-Aboriginal women. General linear regression was used to estimate the determinants of MD, adjusting for age, NDA, hormone therapy use, family history, measures of socio-economic status and remoteness of residence for Aboriginal and non-Aboriginal women separately. RESULTS: Aboriginal women were found to have lower DA and PDA and higher NDA than non-Aboriginal women. Age (p <  0.001) was negatively associated and several socio-economic indices (p <  0.001) were positively associated with DA and PDA in Aboriginal and non-Aboriginal women. Remoteness of residence was associated with both mammographic measures but for non-Aboriginal women only. CONCLUSIONS: Aboriginal women have, on average, less MD than non-Aboriginal women but the factors associated with MD are similar for both sample populations. Since reduced MD is associated with improved sensitivity of mammography, this study suggests that mammographic screening is a particularly good test for Australian Indigenous women, a population that suffers from high breast cancer mortality.

Initial diagnosis of chronic myelogenous leukemia based on quantification of M-BCR status using droplet digital PCR.

Formed from a reciprocal translocation t(9:22)(q34;q11) of genetic material between the long arms of human chromosomes 9 and 22, the constitutively active breakpoint cluster region (BCR) Abelson 1 (ABL) tyrosine kinase BCR-ABL is known to be causative of chronic myelogenous leukemia (CML). In 98% of CML patients harboring the t(9:22)(q34;q11) translocation, known as the Philadelphia chromosome, the chimeric BCR-ABL oncogene is created through cleavage of the BCR gene within its major breakpoint region (M-BCR) and breakage of the ABL gene within a 100-kbp region downstream of exon 2a. Clinical detection of the fused BCR-ABL oncogene currently relies on direct visualization by fluorescence in situ hybridization (FISH), a relatively tedious assay that typically offers a detection limit of ca. 2%. Here, we describe a novel assay that uses droplet digital PCR (ddPCR) technology to reliably measure M-BCR status and the presence of BCR-ABL. When applied to cell-line models of CML, the assay accurately quantifies BCR-ABL frequency to a detection limit of 0.25%. It therefore offers improved specificity relative to FISH, and may allow identification of variant translocation patterns, including derivative chromosome 9 deletions.