Contrast-enhanced mammography for surveillance in women with a personal history of breast cancer.

PURPOSE: Women with a personal history of breast cancer have an increased risk of subsequent breast malignancy and may benefit from more sensitive surveillance than conventional mammography (MG). We previously reported outcomes for first surveillance episode using contrast-enhanced mammography (CEM), demonstrating higher sensitivity and comparable specificity to MG. We now report CEM performance for subsequent surveillance. METHODS: A retrospective study of 1,190 women in an Australian hospital setting undergoing annual surveillance following initial surveillance CEM between June 2016 and December 2022. Outcome measures were recall rate, cancer detection rate, contribution of contrast to recalls, false positive rate, interval cancer rate and characteristics of surveillance detected and interval cancers. RESULTS: 2,592 incident surveillance episodes were analysed, of which 93% involved contrast-based imaging. Of 116 (4.5%) recall episodes, 40/116 (34%) recalls were malignant (27 invasive; 13 ductal carcinoma in situ), totalling 15.4 cancers per 1000 surveillance episodes. 55/116 (47%) recalls were contrast-directed including 17/40 (43%) true positive recalls. Tumour features were similar for contrast-directed recalls and other diagnoses. 8/9 (89%) of contrast-directed invasive recalls were Grade 2-3, and 5/9 (56%) were triple negative breast cancers. There were two symptomatic interval cancers (0.8 per 1000 surveillance episodes, program sensitivity 96%). CONCLUSION: Routine use of CEM in surveillance of women with PHBC led to an increase in the detection of clinically significant malignant lesions, with a low interval cancer rate compared to previous published series. Compared to mammographic surveillance, contrast-enhanced mammography increases the sensitivity of surveillance programs for women with PHBC.

Contrast-Enhanced Mammography in Local Staging of Screen-Detected Breast Cancer.

BACKGROUND: BreastScreen Australia, the population mammographic screening program for breast cancer, uses two-view digital screening mammography ± ultrasound followed by percutaneous biopsy to detect breast cancer. Secondary breast imaging for further local staging, not performed at BreastScreen, may identify additional clinically significant breast lesions. Staging options include further mammography, bilateral ultrasound, and/or contrast-based imaging (CBI) [magnetic resonance imaging (MRI) or contrast-enhanced mammography (CEM)]. CBI for local staging of screen-detected cancer was introduced at an academic hospital breast service in Melbourne, VIC, Australia. We report findings for otherwise occult disease and resulting treatment changes. MATERIAL AND METHODS: Patients staged using CEM between November 2018 and April 2022 were identified from hospital records. Data were extracted from radiology, pathology, and breast unit databases. CEM-detected abnormalities were documented as true positive (TP) for invasive cancer or ductal carcinoma in situ (DCIS), or otherwise false positive (FP). The impact on surgical decisions was assessed. RESULTS: Of 202 patients aged 44-84 years, 60 (30%) had 74 additional findings [34 (46%) TP, 40 (54%) FP]. These were malignant in 29/202 (14%) patients (79% invasive cancers, 21% DCIS). CEM resulted in surgical changes in 43/202 (21%) patients: wider resection (24/43), conversion to mastectomy (6/43), contralateral breast surgery (6/43), additional ipsilateral excision (5/43), and bracketing (2/43). Additional findings were more common for patients with larger index lesions and for invasive cancer, but there was no significant variation by age, breast density, or index lesion grade. CONCLUSIONS: CEM for local staging of screen-detected breast cancers identified occult malignancy in 14% of patients. CEM improves local staging and may facilitate appropriate management of screen-detected breast cancers.

Contrast enhanced mammography in breast cancer surveillance.

PURPOSE: Mammography (MG) is the standard imaging in surveillance of women with a personal history of breast cancer or DCIS (PHBC), supplemented with ultrasound. Contrast Enhanced Mammography (CEM) has higher sensitivity than MG and US. We report the performance of CEM compared with MG ± US. METHODS: A retrospective study of patients undergoing their first surveillance CEM in an Australian hospital setting between June 2006 and October 2020. Cases where a patient was recalled for assessment were identified, recording radiology, pathology and treatment details. Blinded re-reading of recalled cases was performed to determine the contribution of contrast. Use of surveillance US across the board was assessed for the period. RESULTS: 73/1191 (6.1%) patients were recalled. 35 (48%) were true positives (TP), with 26 invasive cancers and 9 cases of DCIS, while 38 (52%) were false positive (FP) with a positive predictive value (PPV) 47.9%. 32/73 were recalled due to MG findings, while 41/73 were only recalled due to Contrast. 14/73 had 'minimal signs' with a lesion identifiable on MG with knowledge of the contrast finding, while 27/73 were visible only with contrast. 41% (17/41) recalled due to contrast were TP. Contrast-only TPs were found with low and high mammographic density (MD). Screening breast US reduced by 55% in the year after CEM was implemented. CONCLUSION: Compared to MG, CEM as a single surveillance modality for those with PHBC has higher sensitivity and comparable specificity, identifying additional malignant lesions that are clinically significant. Investigation of interval cancer and subsequent round outcomes is warranted.

Breast cancer screening and overdiagnosis.

Overdiagnosis is a harmful consequence of screening which is particularly challenging to estimate. An unbiased setting to measure overdiagnosis in breast cancer screening requires comparative data from a screened and an unscreened cohort for at least 30 years. Such randomised data will not become available, leaving us with observational data over shorter time periods and outcomes of modelling. This collaborative effort of the International Cancer Screening Network quantified the variation in estimated breast cancer overdiagnosis in organised programmes with evaluation of both observed and simulated data, and presented examples of how modelling can provide additional insights. Reliable observational data, analysed with study design accounting for methodological pitfalls, and modelling studies with different approaches, indicate that overdiagnosis accounts for less than 10% of invasive breast cancer cases in a screening target population of women aged 50 to 69. Estimates above this level are likely to derive from inaccuracies in study design. The widely discrepant estimates of overdiagnosis reported from observational data could substantially be reduced by use of a cohort study design with at least 10 years of follow-up after screening stops. In contexts where concomitant opportunistic screening or gradual implementation of screening occurs, and data on valid comparison groups are not readily available, modelling of screening intervention becomes an advantageous option to obtain reliable estimates of breast cancer overdiagnosis.

The impact of the Covid-19 pandemic on breast cancer early detection and screening.

The COVID-19 pandemic affects mortality and morbidity, with disruptions expected to continue for some time, with access to timely cancer-related services a concern. For breast cancer, early detection and treatment is key to improved survival and longer-term quality of life. Health services generally have been strained and in many settings with population breast mammography screening, efforts to diagnose and treat breast cancers earlier have been paused or have had reduced capacity. The resulting delays to diagnosis and treatment may lead to more intensive treatment requirements and, potentially, increased mortality. Modelled evaluations can support responses to the pandemic by estimating short- and long-term outcomes for various scenarios. Multiple calibrated and validated models exist for breast cancer screening, and some have been applied in 2020 to estimate the impact of breast screening disruptions and compare options for recovery, in a range of international settings. On behalf of the Covid and Cancer Modelling Consortium (CCGMC) Working Group 2 (Breast Cancer), we summarize and provide examples of such in a range of settings internationally, and propose priorities for future modelling exercises. International expert collaborations from the CCGMC Working Group 2 (Breast Cancer) will conduct analyses and modelling studies needed to inform key stakeholders recovery efforts in order to mitigate the impact of the pandemic on early diagnosis and treatment of breast cancer.

Molecular comparison of interval and screen-detected breast cancers.

Breast cancer (BC) diagnosed after a negative mammogram but prior to the next screening episode is termed an 'interval BC' (IBC). Understanding the molecular differences between IBC and screen-detected BCs (SDBC) could improve mammographic screening and management options. Therefore, we assessed both germline and somatic genomic aberrations in a prospective cohort. Utilising the Lifepool cohort of >54 000 women attending mammographic screening programs, 930 BC cases with screening status were identified (726 SDBC and 204 IBC). Clinico-pathological and family history information were recorded. Germline and tumour DNA were collected where available and sequenced for BC predisposition and driver gene mutations. Compared to SDBC, IBCs were significantly associated with a younger age at diagnosis and tumour characteristics associated with worse prognosis. Germline DNA assessment of BC cases that developed post-enrolment (276 SDBCs and 77 IBCs) for pathogenic mutations in 12 hereditary BC predisposition genes identified 8 carriers (2.27%). The germline mutation frequency was higher in IBC versus SDBC, although not statistically significant (3.90% versus 1.81%, p = 0.174). Comparing somatic genetic features of IBC and SDBC matched for grade, histological subtype and hormone receptor revealed no significant differences, with the exception of higher homologous recombination deficiency scores in IBC, and copy number changes on chromosome Xq in triple negative SDBCs. Our data demonstrates that while IBCs are clinically more aggressive than SDBC, when matched for confounding clinico-pathological features they do not represent a unique molecular class of invasive BC, but could be a consequence of timing of tumour initiation and mammographic screening. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women.

BACKGROUND: There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. METHODS: We used data from 40,158 women aged 50-69 years (via the lifepool cohort) participating in Australia's BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. RESULTS: Over a median of 4.3 years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O) = 1.09, 95% confidence interval (CI) 1.00-1.18). There was good agreement across decile groups of Gail scores (χ2 = 7.1, p = 0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O = 1.65, 95% CI 1.33-2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73-3.02, p < 0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06-1.70, p = 0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44-0.79, p < 0.0001). Similar patterns were observed separately for women aged 50-59 and 60-69 years. The model's overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56-0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. CONCLUSIONS: This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50-69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.

Treatment Intensity Differences After Early-Stage Breast Cancer (ESBC) Diagnosis Depending on Participation in a Screening Program.

BACKGROUND: While population mammographic screening identifies early-stage breast cancers (ESBCs; ductal carcinoma in situ [DCIS] and invasive disease stages 1-3A), commentaries suggest that harms from overdiagnosis and overtreatment may outweigh the benefits. Apparent benefits to patients with screen-detected cancers may be due to selection bias from exclusion of interval cancers (ICs). Treatment intensity is rarely discussed, with an assumption that all ESBCs are treated similarly. We hypothesized that women diagnosed while in a screening program would receive less-intense treatment than those never or not recently screened (NRS). METHODS: This was a retrospective analysis of all women aged 50-69 years managed for ESBC (invasive or DCIS) during the period 2007-2013 within a single service, comparing treatment according to screening status. Data on demographics, detection, pathology, and treatment were derived from hospital, cancer registry, and screening service records. RESULTS: Overall, 622 patients were active screeners (AS) at diagnosis (569 screen-detected and 53 ICs) and 169 patients were NRS. AS cancers were smaller (17 mm vs. 26 mm, p < 0.0001), less likely to involve nodes (26% vs. 48%, p < 0.0001), and lower grade. For invasive cancer, NRS patients were more likely to be recommended for mastectomies [35% vs. 16%; risk ratio(RR) 2.2, p < 0.0001], axillary dissection (43% vs. 19%; RR 2.3, p < 0.0001), adjuvant chemotherapy (65% vs. 37%; RR 1.7, p < 0.0001), and postmastectomy radiotherapy (58% vs. 39%; RR 1.5, p = 0.04). CONCLUSION: Participants in population screening diagnosed with ESBC receive substantially less-intense treatment than non-participants. Differences persist when potential overdiagnosis is taken into account; these differences should be factored into debates around mammographic screening.

An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance.

The aim of this work is to investigate how radiologist expertise and image appearance may have an impact on inter-reader variability of mammographic density (MD) identification. Seventeen radiologists, divided into three expertise groups, were asked to manually segment the areas they consider to be MD in 40 clinical images. The variation in identification of MD for each image was quantified by finding the range of segmentation areas. The impact of radiologist expertise and image appearance on this variation was explored. The range of areas chosen by participating radiologists varied from 7 to 73% across the 40 images, with a mean range of 35 ± 13%. Participants with high expertise were more likely to choose similar areas to one another, compared to participants with medium and low expertise levels (mean range were 19 ± 10%, 29 ± 13% and 25 ± 14 %, respectively, p < 0.0001). There was a significantly higher average grey level for the area segmented by all radiologists as MD compared to the area of variation, with mean grey level value for 8-bit images being 146 ± 19 vs. 99 ± 14, respectively. MD segmentation borders were consistent in areas where there was a sharp intensity change within a short distance. In conclusion, radiologists with high expertise tend to have a higher agreement when identifying MD. Tissues which have a lower contrast and a less visually sharp gradient change at the interface between high density tissue and adipose background lead to inter-reader variation in choosing mammographic density.

Breast cancer screening of women aged 70-74 years: results from a natural experiment across Australia.

There is a lack of evidence regarding the optimal age at which to cease mammographic screening for breast cancer. This ecological study compared Australian state and territory level screening participation rates and cancer outcomes from 1996 to 2005 to identify the extent to which screening women aged 70-74 results in smaller, earlier stage breast cancers. With each 10 % absolute increase in screening participation, there was no significant difference in cancer incidence, but the incidence of large cancers was 8 % lower (IRR = 0.92, 95 % CI 0.90-0.94, p < 0.001); there was some evidence of reduced nodal involvement at diagnosis (IRR 0.97, 95 % CI 0.95-0.99, p = 0.004) but this estimate was sensitive to assumptions regarding missing data. Increased mammographic screening of women aged 70-74 years reduces the incidence of large (>15 mm) cancers-and possibly cancers with nodal involvement-without a concomitant increase in overall cancer incidence.

Factors Influencing Time From Diagnosis to Treatment of Breast Cancer and the Impact of Longer Waiting Time on Survival in Kathmandu Valley, Nepal: A Population-Based Study.

PURPOSE: Longer time between breast cancer (BC) diagnosis and treatment initiation is associated with poorer survival, and this may be a factor behind disparities in global survival rates. We assessed time to BC treatment in the Kathmandu Valley, Nepal, including factors associated with longer waiting times and their impact on survival. METHODS: We conducted a retrospective population-based study of BC cases recorded in the Kathmandu Valley Population-Based Cancer Registry between 2018 and 2019. Fieldwork survey through telephone was undertaken to collect additional sociodemographic and clinical information. Logistic regression was performed to identify factors associated with longer time to treatment, and Kaplan-Meier and Cox proportional hazard regression was used to examine survival time and evaluate the association between longer time to treatment and survival. RESULTS: Among the 385 patients with BC, one third waited >4 weeks from diagnosis to initial treatment. Lower education was associated with longer time to treatment (adjusted odds ratio, 1.63 [95% CI, 1.03 to 2.60]). The overall 3-year survival rate was 88.6% and survival was not associated with time to treatment (P = .50). However, advanced stage at diagnosis was associated with poorer survival (adjusted hazard ratio, 4.09 [95% CI, 1.27 to 13.23]). There was some indication that longer time to treatment was associated with poorer survival for advanced-stage patients, but data quality limited that analysis. CONCLUSION: In the Kathmandu Valley, Nepal, women with a lower education tend to wait longer from BC diagnosis to treatment. Patients with advanced-stage BC had poorer survival, and longer waiting time may be associated with poorer survival for women diagnosed with advanced-stage disease.

Factors influencing the time to diagnosis and treatment of breast cancer among women in low- and middle-income countries: A systematic review.

PURPOSE: Shorter time from symptoms recognition to diagnosis and timely treatment would be expected to improve the survival of patients with breast cancer (BC). This review identifies and summarizes evidence on time to diagnosis and treatment, and associated factors to inform an improved BC care pathways in Low- and Middle-Income Countries (LMICs). METHODS: A systematic search was conducted in electronic databases including Medline, Embase, PsycINFO and Global Health, covering publications between January 1, 2010, and November 6, 2023. Inclusion criteria encompassed studies published in English from LMICs that reported on time from symptoms recognition to diagnosis and/or from diagnosis to treatment, as well as factors influencing these timelines. Study quality was assessed independently by two reviewers using a standard checklist. Pre-contact, post-contact and treatment intervals and delays in these intervals are presented. Barriers and facilitators for shorter time to diagnosis and treatment found by individual studies after adjusting with covariates are summarized. RESULTS: The review identified 21 studies across 14 countries and found that BC cases took a longer time to diagnosis than to treatment. However, time to treatment also exceeded the World Health Organization (WHO) recommended period for optimal survival. There was inconsistency in terminology and benchmarks for defining delays in time intervals. Low socioeconomic status and place of residence emerged as frequent barriers, while initial contact with a private health facility or specialist was commonly reported as a facilitator for shorter time to diagnosis and treatment. CONCLUSIONS: Guidelines or consensus recommendations are essential for defining the optimal time intervals to BC diagnosis and treatment. Our review supported WHO's Global Breast Cancer Initiative recommendations. Increasing public awareness, strengthening of healthcare professional's capacities, partial decentralization of diagnostic services and implementation of effective referral mechanisms are recommended to achieve a shorter time to diagnosis and treatment of BC in LMICs.

AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes.

INTRODUCTION: While Cumulus - a semi-automated method for measuring breast density - is utilised extensively in research, it is labour-intensive and unsuitable for screening programmes that require an efficient and valid measure on which to base screening recommendations. We develop an automated method to measure breast density (AutoDensity) and compare it to Cumulus in terms of association with breast cancer risk and breast cancer screening outcomes. METHODS: AutoDensity automatically identifies the breast area in the mammogram and classifies breast density in a similar way to Cumulus, through a fast, stand-alone Windows or Linux program. Our sample comprised 985 women with screen-detected cancers, 367 women with interval cancers and 4,975 controls (women who did not have cancer), sampled from first and subsequent screening rounds of a film mammography screening programme. To test the validity of AutoDensity, we compared the effect estimates using AutoDensity with those using Cumulus from logistic regression models that tested the association between breast density and breast cancer risk, risk of small and large screen-detected cancers and interval cancers, and screening programme sensitivity (the proportion of cancers that are screen-detected). As a secondary analysis, we report on correlation between AutoDensity and Cumulus measures. RESULTS: AutoDensity performed similarly to Cumulus in all associations tested. For example, using AutoDensity, the odds ratios for women in the highest decile of breast density compared to women in the lowest quintile for invasive breast cancer, interval cancers, large and small screen-detected cancers were 3.2 (95% CI 2.5 to 4.1), 4.7 (95% CI 3.0 to 7.4), 6.4 (95% CI 3.7 to 11.1) and 2.2 (95% CI 1.6 to 3.0) respectively. For Cumulus the corresponding odds ratios were: 2.4 (95% CI 1.9 to 3.1), 4.1 (95% CI 2.6 to 6.3), 6.6 (95% CI 3.7 to 11.7) and 1.3 (95% CI 0.9 to 1.8). Correlation between Cumulus and AutoDensity measures was 0.63 (P < 0.001). CONCLUSIONS: Based on the similarity of the effect estimates for AutoDensity and Cumulus inmodels of breast density and breast cancer risk and screening outcomes, we conclude that AutoDensity is a valid automated method for measuring breast density from digitised film mammograms.

Benefits and harms of breast cancer screening: Cohort study of breast cancer mortality and overdiagnosis.

BACKGROUND: Quantifying the benefits and harms of breast cancer screening accurately is important for planning and evaluating screening programs and for enabling women to make informed decisions about participation. However, few cohort studies have attempted to estimate benefit and harm simultaneously. AIMS: We aimed to quantify the impact of mammographic screening on breast cancer mortality and overdiagnosis using a cohort of women invited to attend Australia's national screening program, BreastScreen. METHODS: In a cohort of 41,330 women without prior breast cancer diagnosis, screening, or diagnostic procedures invited to attend BreastScreen Western Australia in 1994-1995, we estimated the cumulative risk of breast cancer mortality and breast cancer incidence (invasive and ductal carcinoma in situ) from age 50 to 85 years for attenders and non-attenders. Data were obtained by linking population-based state and national health registries. Breast cancer mortality risks were estimated from a survival analysis that accounted for competing risk of death from other causes. Breast cancer risk for unscreened women was estimated by survival analysis, while accounting for competing causes of death. For screened women, breast cancer risk was the sum of risk of being diagnosed at first screen, estimated using logistic regression, and risk of diagnosis following a negative first screen estimated from a survival analysis. RESULTS: For every 1,000 women 50 years old at first invitation to attend BreastScreen, there were 20 (95% CI 12-30) fewer breast cancer deaths and 25 (95% CI 15-35) more breast cancers diagnosed for women who attended than for non-attendees by age 85. Of the breast cancers diagnosed in screened women, 21% (95% CI 13%-27%) could be attributed to screening. DISCUSSION: The estimated ratio of benefit to harm was consistent with, but slightly less favourable to screening than most other estimates from cohort studies. CONCLUSION: Women who participate in organised screening for breast cancer in Australia have substantially lower breast cancer mortality, while some screen-detected cancers may be overdiagnosed.

Breast Cancer Risk Assessment Tools for Stratifying Women into Risk Groups: A Systematic Review.

BACKGROUND: The benefits and harms of breast screening may be better balanced through a risk-stratified approach. We conducted a systematic review assessing the accuracy of questionnaire-based risk assessment tools for this purpose. METHODS: Population: asymptomatic women aged ≥40 years; Intervention: questionnaire-based risk assessment tool (incorporating breast density and polygenic risk where available); Comparison: different tool applied to the same population; Primary outcome: breast cancer incidence; Scope: external validation studies identified from databases including Medline and Embase (period 1 January 2008-20 July 2021). We assessed calibration (goodness-of-fit) between expected and observed cancers and compared observed cancer rates by risk group. Risk of bias was assessed with PROBAST. RESULTS: Of 5124 records, 13 were included examining 11 tools across 15 cohorts. The Gail tool was most represented (n = 11), followed by Tyrer-Cuzick (n = 5), BRCAPRO and iCARE-Lit (n = 3). No tool was consistently well-calibrated across multiple studies and breast density or polygenic risk scores did not improve calibration. Most tools identified a risk group with higher rates of observed cancers, but few tools identified lower-risk groups across different settings. All tools demonstrated a high risk of bias. CONCLUSION: Some risk tools can identify groups of women at higher or lower breast cancer risk, but this is highly dependent on the setting and population.

A modelled evaluation of the impact of COVID-19 on breast, bowel, and cervical cancer screening programmes in Australia.

Australia introduced COVID-19 infection prevention and control measures in early 2020. To help prepare health services, the Australian Government Department of Health commissioned a modelled evaluation of the impact of disruptions to population breast, bowel, and cervical cancer screening programmes on cancer outcomes and cancer services. We used the Policy1 modelling platforms to predict outcomes for potential disruptions to cancer screening participation, covering periods of 3, 6, 9, and 12 mo. We estimated missed screens, clinical outcomes (cancer incidence, tumour staging), and various diagnostic service impacts. We found that a 12-mo screening disruption would reduce breast cancer diagnoses (9.3% population-level reduction over 2020-2021) and colorectal cancer (up to 12.1% reduction over 2020-21), and increase cervical cancer diagnoses (up to 3.6% over 2020-2022), with upstaging expected for these cancer types (2, 1.4, and 6.8% for breast, cervical, and colorectal cancers, respectively). Findings for 6-12-mo disruption scenarios illustrate that maintaining screening participation is critical to preventing an increase in the burden of cancer at a population level. We provide programme-specific insights into which outcomes are expected to change, when changes are likely to become apparent, and likely downstream impacts. This evaluation provided evidence to guide decision-making for screening programmes and emphasises the ongoing benefits of maintaining screening in the face of potential future disruptions.