Cost-Effectiveness of AI for Risk-Stratified Breast Cancer Screening.

Importance: Previous research has shown good discrimination of short-term risk using an artificial intelligence (AI) risk prediction model (Mirai). However, no studies have been undertaken to evaluate whether this might translate into economic gains. Objective: To assess the cost-effectiveness of incorporating risk-stratified screening using a breast cancer AI model into the United Kingdom (UK) National Breast Cancer Screening Program. Design, Setting, and Participants: This study, conducted from January 1, 2023, to January 31, 2024, involved the development of a decision analytical model to estimate health-related quality of life, cancer survival rates, and costs over the lifetime of the female population eligible for screening. The analysis took a UK payer perspective, and the simulated cohort consisted of women aged 50 to 70 years at screening. Exposures: Mammography screening at 1 to 6 yearly screening intervals based on breast cancer risk and standard care (screening every 3 years). Main Outcomes and Measures: Incremental net monetary benefit based on quality-adjusted life-years (QALYs) and National Health Service (NHS) costs (given in pounds sterling; to convert to US dollars, multiply by 1.28). Results: Artificial intelligence-based risk-stratified programs were estimated to be cost-saving and increase QALYs compared with the current screening program. A screening schedule of every 6 years for lowest-risk individuals, biannually and triennially for those below and above average risk, respectively, and annually for those at highest risk was estimated to give yearly net monetary benefits within the NHS of approximately £60.4 (US $77.3) million and £85.3 (US $109.2) million, with QALY values set at £20 000 (US $25 600) and £30 000 (US $38 400), respectively. Even in scenarios where decision-makers hesitate to allocate additional NHS resources toward screening, implementing the proposed strategies at a QALY value of £1 (US $1.28) was estimated to generate a yearly monetary benefit of approximately £10.6 (US $13.6) million. Conclusions and Relevance: In this decision analytical model study of integrating risk-stratified screening with a breast cancer AI model into the UK National Breast Cancer Screening Program, risk-stratified screening was likely to be cost-effective, yielding added health benefits at reduced costs. These results are particularly relevant for health care settings where resources are under pressure. New studies to prospectively evaluate AI-guided screening appear warranted.

Role of Supplemental Breast MRI in Screening Women with Mammographically Dense Breasts: A Systematic Review and Meta-analysis.

BACKGROUND: High mammographic density increases breast cancer risk and reduces mammographic sensitivity. We reviewed evidence on accuracy of supplemental MRI for women with dense breasts at average or increased risk. METHODS: PubMed and Embase were searched 1995-2022. Articles were included if women received breast MRI following 2D or tomosynthesis mammography. Risk of bias was assessed using QUADAS-2. Analysis used independent studies from the articles. Fixed-effect meta-analytic summaries were estimated for predefined groups (PROSPERO: 230277). RESULTS: Eighteen primary research articles (24 studies) were identified in women aged 19-87 years. Breast density was heterogeneously or extremely dense (BI-RADS C/D) in 15/18 articles and extremely dense (BI-RADS D) in 3/18 articles. Twelve of 18 articles reported on increased-risk populations. Following 21 440 negative mammographic examinations, 288/320 cancers were detected by MRI. Substantial variation was observed between studies in MRI cancer detection rate, partly associated with prevalent vs incident MRI exams (prevalent: 16.6/1000 exams, 12 studies; incident: 6.8/1000 exams, 7 studies). MRI had high sensitivity for mammographically occult cancer (20 studies with at least 1-year follow-up). In 5/18 articles with sufficient data to estimate relative MRI detection rate, approximately 2 in 3 cancers were detected by MRI (66.3%, 95% CI, 56.3%-75.5%) but not mammography. Positive predictive value was higher for more recent studies. Risk of bias was low in most studies. CONCLUSION: Supplemental breast MRI following negative mammography in women with dense breasts has breast cancer detection rates of ~16.6/1000 at prevalent and ~6.8/1000 at incident MRI exams, considering both high and average risk settings.

Predictable changes in the accuracy of human papillomavirus tests after vaccination: review with implications for performance monitoring in cervical screening.

Vaccination against human papillomavirus (HPV) is changing the performance of cytology as a cervical screening test, but its effect on HPV testing is unclear. We review the effect of HPV16/18 vaccination on the epidemiology and the detection of HPV infections and high-grade cervical lesions (CIN2+) to evaluate the likely direction of changes in HPV test accuracy. The reduction in HPV16/18 infections and cross-protection against certain non-16/18 high-risk genotypes, most notably 31, 33, and/or 45, will likely increase the test's specificity but decrease its positive predictive value (PPV) for CIN2+. Post-vaccination viral unmasking of non-16/18 genotypes due to fewer HPV16 co-infections might reduce the specificity and the PPV for CIN2+. Post-vaccination clinical unmasking exposing a higher frequency of CIN2+ related to non-16/18 high-risk genotypes is likely to increase the specificity and the PPV of HPV tests. The effect of HPV16/18 vaccination on HPV test sensitivity is difficult to predict based on these changes alone. Programmes relying on HPV detection for primary screening should monitor the frequency of false-positive and false-negative tests in vaccinated (younger) vs. unvaccinated (older) cohorts, to assess the outcomes and performance of their service.

Long-term prediction by DNA methylation of high-grade cervical intraepithelial neoplasia: Results of the ARTISTIC cohort.

Methylation markers have shown potential for triaging high-risk HPV-positive (hrHPV+) women to identify those at increased risk of invasive cervical cancer (ICC). Our aim was to assess the performance of the S5 DNA methylation classifier for predicting incident high-grade cervical intraepithelial neoplasia (CIN) and ICC among hrHPV+ women in the ARTISTIC screening trial cohort. The S5 classifier, comprising target regions of tumour suppressor gene EPB41L3 and L1 and L2 regions of HPV16, HPV18, HPV31, and HPV33, was assayed by pyrosequencing in archived hrHPV+ liquid-based samples from 343 women with high-grade disease (139 CIN2, 186 CIN3, and 18 ICC) compared to 800 hrHPV+ controls. S5 DNA methylation correlated directly with increasing severity of disease and inversely with lead time to diagnosis. S5 could discriminate between hrHPV+ women who developed CIN3 or ICC and hrHPV+ controls (p <.0001) using samples taken on average 5 years before diagnosis. This relationship was independent of cytology at baseline. The S5 test showed much higher sensitivity than HPV16/18 genotyping for identifying prevalent CIN3 (93% vs. 61%, p = .01) but lower specificity (50% vs. 66%, p <.0001). The S5 classifier identified most women at high risk of developing precancer and missed very few prevalent advanced lesions thus appearing to be an objective test for triage of hrHPV+ women. The combination of methylation of host and HPV genes enables S5 to combine the predictive power of methylation with HPV genotyping to identify hrHPV-positive women who are at highest risk of developing CIN3 and ICC in the future.

Body composition changes during breast cancer preventive treatment with anastrozole: Findings from the IBIS-II trial.

Background: Uptake to anastrozole for breast cancer prevention is low, partly due to women's concerns about side effects including gains in weight and specifically gains in body fat. Previous evidence does not link anastrozole with gains in weight, but there is a lack of data on any effects on body composition i.e. changes in fat and fat free mass. Here we assess association of anastrozole with body composition changes in a prospective sub-study from the second international breast intervention trial (IBIS-II). Methods: Participants had DXA scans at baseline and for five years of anastrozole/placebo and beyond (between March 2004 and September 2017. Primary outcomes were changes in body weight, body fat and fat free mass at 9-18 months. A linear model was used to estimate the size of a differential effect in these outcomes by randomised treatment allocation adjusted for baseline value and time since last scan, age, 10-year breast cancer risk, smoking and HRT status. Results: 203 postmenopausal women were recruited (n = 95 anastrozole, n = 108 placebo), mean age 58 years (SD = 5.4), BMI 28.0 kg/m2 (SD = 5.5). There was no evidence of a strong association between anastrozole or placebo and endpoints at 9-18 months; effect size (95 %CI) for anastrozole minus placebo for body weight (per/kg) -0.11 (-1.29-1.08); body fat 0.11 (-0.75-0.96) and fat free mass -0.30 (-0.79-0.19). Conclusions: There is unlikely to be a clinically significant change to body composition with anastrozole for breast cancer prevention.

Cost-Effectiveness of Gene-Specific Prevention Strategies for Ovarian and Breast Cancer.

Importance: Pathogenic variants (PVs) in BRCA1, BRCA2, PALB2, RAD51C, RAD51D, and BRIP1 cancer susceptibility genes (CSGs) confer an increased ovarian cancer (OC) risk, with BRCA1, BRCA2, PALB2, RAD51C, and RAD51D PVs also conferring an elevated breast cancer (BC) risk. Risk-reducing surgery, medical prevention, and BC surveillance offer the opportunity to prevent cancers and deaths, but their cost-effectiveness for individual CSGs remains poorly addressed. Objective: To estimate the cost-effectiveness of prevention strategies for OC and BC among individuals carrying PVs in the previously listed CSGs. Design, Setting, and Participants: In this economic evaluation, a decision-analytic Markov model evaluated the cost-effectiveness of risk-reducing salpingo-oophorectomy (RRSO) and, where relevant, risk-reducing mastectomy (RRM) compared with nonsurgical interventions (including BC surveillance and medical prevention for increased BC risk) from December 1, 2022, to August 31, 2023. The analysis took a UK payer perspective with a lifetime horizon. The simulated cohort consisted of women aged 30 years who carried BRCA1, BRCA2, PALB2, RAD51C, RAD51D, or BRIP1 PVs. Appropriate sensitivity and scenario analyses were performed. Exposures: CSG-specific interventions, including RRSO at age 35 to 50 years with or without BC surveillance and medical prevention (ie, tamoxifen or anastrozole) from age 30 or 40 years, RRM at age 30 to 40 years, both RRSO and RRM, BC surveillance and medical prevention, or no intervention. Main Outcomes and Measures: The incremental cost-effectiveness ratio (ICER) was calculated as incremental cost per quality-adjusted life-year (QALY) gained. OC and BC cases and deaths were estimated. Results: In the simulated cohort of women aged 30 years with no cancer, undergoing both RRSO and RRM was most cost-effective for individuals carrying BRCA1 (RRM at age 30 years; RRSO at age 35 years), BRCA2 (RRM at age 35 years; RRSO at age 40 years), and PALB2 (RRM at age 40 years; RRSO at age 45 years) PVs. The corresponding ICERs were -£1942/QALY (-$2680/QALY), -£89/QALY (-$123/QALY), and £2381/QALY ($3286/QALY), respectively. RRSO at age 45 years was cost-effective for RAD51C, RAD51D, and BRIP1 PV carriers compared with nonsurgical strategies. The corresponding ICERs were £962/QALY ($1328/QALY), £771/QALY ($1064/QALY), and £2355/QALY ($3250/QALY), respectively. The most cost-effective preventive strategy per 1000 PV carriers could prevent 923 OC and BC cases and 302 deaths among those carrying BRCA1; 686 OC and BC cases and 170 deaths for BRCA2; 464 OC and BC cases and 130 deaths for PALB2; 102 OC cases and 64 deaths for RAD51C; 118 OC cases and 76 deaths for RAD51D; and 55 OC cases and 37 deaths for BRIP1. Probabilistic sensitivity analysis indicated both RRSO and RRM were most cost-effective in 96.5%, 89.2%, and 84.8% of simulations for BRCA1, BRCA2, and PALB2 PVs, respectively, while RRSO was cost-effective in approximately 100% of simulations for RAD51C, RAD51D, and BRIP1 PVs. Conclusions and Relevance: In this cost-effectiveness study, RRSO with or without RRM at varying optimal ages was cost-effective compared with nonsurgical strategies for individuals who carried BRCA1, BRCA2, PALB2, RAD51C, RAD51D, or BRIP1 PVs. These findings support personalizing risk-reducing surgery and guideline recommendations for individual CSG-specific OC and BC risk management.

Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case-control study.

BACKGROUND: There is increasing evidence that artificial intelligence (AI) breast cancer risk evaluation tools using digital mammograms are highly informative for 1-6 years following a negative screening examination. We hypothesized that algorithms that have previously been shown to work well for cancer detection will also work well for risk assessment and that performance of algorithms for detection and risk assessment is correlated. METHODS: To evaluate our hypothesis, we designed a case-control study using paired mammograms at diagnosis and at the previous screening visit. The study included n = 3386 women from the OPTIMAM registry, that includes mammograms from women diagnosed with breast cancer in the English breast screening program 2010-2019. Cases were diagnosed with invasive breast cancer or ductal carcinoma in situ at screening and were selected if they had a mammogram available at the screening examination that led to detection, and a paired mammogram at their previous screening visit 3y prior to detection when no cancer was detected. Controls without cancer were matched 1:1 to cases based on age (year), screening site, and mammography machine type. Risk assessment was conducted using a deep-learning model designed for breast cancer risk assessment (Mirai), and three open-source deep-learning algorithms designed for breast cancer detection. Discrimination was assessed using a matched area under the curve (AUC) statistic. RESULTS: Overall performance using the paired mammograms followed the same order by algorithm for risk assessment (AUC range 0.59-0.67) and detection (AUC 0.81-0.89), with Mirai performing best for both. There was also a correlation in performance for risk and detection within algorithms by cancer size, with much greater accuracy for large cancers (30 mm+, detection AUC: 0.88-0.92; risk AUC: 0.64-0.74) than smaller cancers (0 to < 10 mm, detection AUC: 0.73-0.86, risk AUC: 0.54-0.64). Mirai was relatively strong for risk assessment of smaller cancers (0 to < 10 mm, risk, Mirai AUC: 0.64 (95% CI 0.57 to 0.70); other algorithms AUC 0.54-0.56). CONCLUSIONS: Improvements in risk assessment could stem from enhancing cancer detection capabilities of smaller cancers. Other state-of-the-art AI detection algorithms with high performance for smaller cancers might achieve relatively high performance for risk assessment.

Effect of baseline oestradiol serum concentration on the efficacy of anastrozole for preventing breast cancer in postmenopausal women at high risk: a case-control study of the IBIS-II prevention trial.

BACKGROUND: An increased risk of breast cancer is associated with high serum concentrations of oestradiol and testosterone in postmenopausal women, but little is known about how these hormones affect response to endocrine therapy for breast cancer prevention or treatment. We aimed to assess the effects of serum oestradiol and testosterone concentrations on the efficacy of the aromatase inhibitor anastrozole for the prevention of breast cancer in postmenopausal women at high risk. METHODS: In this case-control study we used data from the IBIS-II prevention trial, a randomised, controlled, double-blind trial in postmenopausal women aged 40-70 years at high risk of breast cancer, conducted in 153 breast cancer treatment centres across 18 countries. In the trial, women were randomly assigned (1:1) to receive anastrozole (1 mg/day, orally) or placebo daily for 5 years. In this pre-planned case-control study, the primary analysis was the effect of the baseline oestradiol to sex hormone binding globulin (SHBG) ratio (oestradiol-SHBG ratio) on the development of all breast cancers, including ductal carcinoma in situ (the primary endpoint in the trial). Cases were participants in whom breast cancer was reported after trial entry and until the cutoff on Oct 22, 2019, and who had valid blood samples and no use of hormone replacement therapy within 3 months of trial entry or during the trial. For each case, two controls without breast cancer were selected at random, matched on treatment group, age (within 2 years), and follow-up time (at least that of the matching case). For each treatment group, we applied a multinominal logistic regression likelihood-ratio trend test to assess what change in the proportion of cases was associated with a one-quartile change in hormone ratio. Controls were used only to determine quartile cutoffs. Profile likelihood 95% CIs were used to indicate the precision of estimates. A secondary analysis also investigated the effect of the baseline testosterone-SHBG ratio on breast cancer development. We also assessed relative benefit of anastrozole versus placebo (calculated as 1 - the ratio of breast cancer cases in the anastrozole group to cases in the placebo group). The trial was registered with ISRCTN (number ISRCTN31488319) and completed recruitment on Jan 31, 2012, but long-term follow-up is ongoing. FINDINGS: 3864 women were recruited into the trial between Feb 2, 2003, and Jan 31, 2012, and randomly assigned to receive anastrozole (n=1920) or placebo (n=1944). Median follow-up time was 131 months (IQR 106-156), during which 85 (4·4%) cases of breast cancer in the anastrozole group and 165 (8·5%) in the placebo group were identified. No data on gender, race, or ethnicity were collected. After exclusions, the case-control study included 212 participants from the anastrozole group (72 cases, 140 controls) and 416 from the placebo group (142 cases, 274 controls). A trend of increasing breast cancer risk with increasing oestradiol-SHBG ratio was found in the placebo group (trend per quartile 1·25 [95% CI 1·08 to 1·45], p=0·0033), but not in the anastrozole group (1·06 [0·86 to 1·30], p=0·60). A weaker effect was seen for the testosterone-SHBG ratio in the placebo group (trend 1·21 [1·05 to 1·41], p=0·011), but again not in the anastrozole group (trend 1·18 [0·96 to 1·46], p=0·11). A relative benefit of anastrozole was seen in quartile 2 (0·55 [95% CI 0·13 to 0·78]), quartile 3 (0·54 [0·22 to 0·74], and quartile 4 (0·56 [0·23 to 0·76]) of oestradiol-SHBG ratio, but not in quartile 1 (0·18 [-0·60 to 0·59]). INTERPRETATION: These results suggest that serum hormones should be measured more routinely and integrated into risk management decisions. Measuring serum hormone concentrations is inexpensive and might help clinicians differentiate which women will benefit most from an aromatase inhibitor. FUNDING: Cancer Research UK, National Health and Medical Research Council (Australia), Breast Cancer Research Foundation, and DaCosta Fund.

Staged design recommendations for validating relative sensitivity of self-sample human papillomavirus tests for cervical screening.

OBJECTIVES: To ensure that the emerging methods for human papillomavirus (HPV) testing on self-collected samples in cervical screening are evaluated robustly. STUDY DESIGN AND SETTING: We assess paired study designs for relative sensitivity of self-collected vs. traditional clinician-collected samples in detection of high-grade cervical intraepithelial neoplasia. RESULTS: Designs considered are (D1) both samples at screening, with clinical actions triggered by HPV positivity; (D2) offering a self-sample test to clinician-collected HPV-positive women; (D3) as D2 but using a repeat clinician-sample as comparator; (D4) offering a choice of self- vs. clinician-sampling, and the alternative test in HPV-positive women; (D5) paired samples at referral appointment. D1 is simple to analyze but requires the largest sample size and referral of self-sample positive, clinician-sample negative women. D2 requires a much smaller sample size, and no change to clinical practice, and could be used to rule-in a test because estimates are conservative (against self-sampling). D3 mitigates this bias but requires a second clinician sample. D4 is only manageable where self-sampling already occurs. The liberal D5 might be used to rule-out a self-sampling test. CONCLUSION: A universal recommendation for an optimal study design is challenging. Staged validation might be useful with D5 as a gatekeeper for D1-D4.

Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study.

BACKGROUND: Women with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman's entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability. METHODS: In total, 132,439 women, aged 40-73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR-χ2) and (3) concordance indices. RESULTS: In total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR-χ2 = 379.6 (degrees of freedom (df) = 2) vs. 307.7 (df = 3)), which increased to 35% (ΔLR-χ2 = 251.2 vs. 186.7) for follow-up after three mammograms (n = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7-8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4-5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003-0.012). CONCLUSIONS: Estimating mammographic density using a woman's history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening.

An optimization framework to guide the choice of thresholds for risk-based cancer screening.

It is uncommon for risk groups defined by statistical or artificial intelligence (AI) models to be chosen by jointly considering model performance and potential interventions available. We develop a framework to rapidly guide choice of risk groups in this manner, and apply it to guide breast cancer screening intervals using an AI model. Linear programming is used to define risk groups that minimize expected advanced cancer incidence subject to resource constraints. In the application risk stratification performance is estimated from a case-control study (2044 cases, 1:1 matching), and other parameters are taken from screening trials and the screening programme in England. Under the model, re-screening in 1 year for the highest 4% AI model risk, in 3 years for the middle 64%, and in 4 years for 32% of the population at lowest risk, was expected to reduce the number of advanced cancers diagnosed by approximately 18 advanced cancers per 1000 diagnosed with triennial screening, for the same average number of screens in the population as triennial screening for all. Sensitivity analyses found the choice of thresholds was robust to model parameters, but the estimated reduction in advanced cancers was not precise and requires further evaluation. Our framework helps define thresholds with the greatest chance of success for reducing the population health burden of cancer when used in risk-adapted screening, which should be further evaluated such as in health-economic modelling based on computer simulation models, and real-world evaluations.

Safety and efficacy of artesunate treatment in severely injured patients with traumatic hemorrhage. The TOP-ART randomized clinical trial.

PURPOSE: This study aimed at determining whether intravenous artesunate is safe and effective in reducing multiple organ dysfunction syndrome in trauma patients with major hemorrhage. METHODS: TOP-ART, a randomized, blinded, placebo-controlled, phase IIa trial, was conducted at a London major trauma center in adult trauma patients who activated the major hemorrhage protocol. Participants received artesunate or placebo (2:1 randomization ratio) as an intravenous bolus dose (2.4 mg/kg or 4.8 mg/kg) within 4 h of injury. The safety outcome was the 28-day serious adverse event (SAE) rate. The primary efficacy outcome was the 48 h sequential organ failure assessment (SOFA) score. The per-protocol recruitment target was 105 patients. RESULTS: The trial was terminated after enrolment of 90 patients because of safety concerns. Eighty-three participants received artesunate (n = 54) or placebo (n = 29) and formed the safety population and 75 met per-protocol criteria (48 artesunate, 27 placebo). Admission characteristics were similar between groups (overall 88% male, median age 29 years, median injury severity score 22), except participants who received artesunate were more shocked (median base deficit 9 vs. 4.7, p = 0.042). SAEs occurred in 17 artesunate participants (31%) vs. 5 who received placebo (17%). Venous thromboembolic events (VTE) occurred in 9 artesunate participants (17%) vs. 1 who received placebo (3%). Superiority of artesunate was not supported by the 48 h SOFA score (median 5.5 artesunate vs. 4 placebo, p = 0.303) or any of the trial's secondary endpoints. CONCLUSION: Among critically ill trauma patients, artesunate is unlikely to improve organ dysfunction and might be associated with a higher VTE rate.

Evaluation of an AI Model to Assess Future Breast Cancer Risk.

Background Accurate breast cancer risk assessment after a negative screening result could enable better strategies for early detection. Purpose To evaluate a deep learning algorithm for risk assessment based on digital mammograms. Materials and Methods A retrospective observational matched case-control study was designed using the OPTIMAM Mammography Image Database from the National Health Service Breast Screening Programme in the United Kingdom from February 2010 to September 2019. Patients with breast cancer (cases) were diagnosed following a mammographic screening or between two triannual screening rounds. Controls were matched based on mammography device, screening site, and age. The artificial intelligence (AI) model only used mammograms at screening before diagnosis. The primary objective was to assess model performance, with a secondary objective to assess heterogeneity and calibration slope. The area under the receiver operating characteristic curve (AUC) was estimated for 3-year risk. Heterogeneity according to cancer subtype was assessed using a likelihood ratio interaction test. Statistical significance was set at P < .05. Results Analysis included patients with screen-detected (median age, 60 years [IQR, 55-65 years]; 2044 female, including 1528 with invasive cancer and 503 with ductal carcinoma in situ [DCIS]) or interval (median age, 59 years [IQR, 53-65 years]; 696 female, including 636 with invasive cancer and 54 with DCIS) breast cancer and 1:1 matched controls, each with a complete set of mammograms at the screening preceding diagnosis. The AI model had an overall AUC of 0.68 (95% CI: 0.66, 0.70), with no evidence of a significant difference between interval and screen-detected (AUC, 0.69 vs 0.67; P = .085) cancer. The calibration slope was 1.13 (95% CI: 1.01, 1.26). There was similar performance for the detection of invasive cancer versus DCIS (AUC, 0.68 vs 0.66; P = .057). The model had higher performance for advanced cancer risk (AUC, 0.72 ≥stage II vs 0.66

Dynamic data-enabled stratified sampling for trial invitations with application in NHS-Galleri.

BACKGROUND: Participants of health research studies such as cancer screening trials usually have better health than the target population. Data-enabled recruitment strategies might be used to help minimise healthy volunteer effects on study power and improve equity. METHODS: A computer algorithm was developed to help target trial invitations. It assumes participants are recruited from distinct sites (such as different physical locations or periods in time) that are served by clusters (such as general practitioners in England, or geographical areas), and the population may be split into defined groups (such as age and sex bands). The problem is to decide the number of people to invite from each group, such that all recruitment slots are filled, healthy volunteer effects are accounted for, and equity is achieved through representation in sufficient numbers of all major societal and ethnic groups. A linear programme was formulated for this problem. RESULTS: The optimisation problem was solved dynamically for invitations to the NHS-Galleri trial (ISRCTN91431511). This multi-cancer screening trial aimed to recruit 140,000 participants from areas in England over 10 months. Public data sources were used for objective function weights, and constraints. Invitations were sent by sampling according to lists generated by the algorithm. To help achieve equity the algorithm tilts the invitation sampling distribution towards groups that are less likely to join. To mitigate healthy volunteer effects, it requires a minimum expected event rate of the primary outcome in the trial. CONCLUSION: Our invitation algorithm is a novel data-enabled approach to recruitment that is designed to address healthy volunteer effects and inequity in health research studies. It could be adapted for use in other trials or research studies.

Quantifying the effects of risk-stratified breast cancer screening when delivered in real time as routine practice versus usual screening: the BC-Predict non-randomised controlled study (NCT04359420).

BACKGROUND: Risk stratification as a routine part of the NHS Breast Screening Programme (NHSBSP) could provide a better balance of benefits and harms. We developed BC-Predict, to offer women when invited to the NHSBSP, which collects standard risk factor information; mammographic density; and in a sub-sample, a Polygenic Risk Score (PRS). METHODS: Risk prediction was estimated primarily from self-reported questionnaires and mammographic density using the Tyrer-Cuzick risk model. Women eligible for NHSBSP were recruited. BC-Predict produced risk feedback letters, inviting women at high risk (≥8% 10-year) or moderate risk (≥5-<8% 10-year) to have appointments to discuss prevention and additional screening. RESULTS: Overall uptake of BC-Predict in screening attendees was 16.9% with 2472 consenting to the study; 76.8% of those received risk feedback within the 8-week timeframe. Recruitment was 63.2% with an onsite recruiter and paper questionnaire compared to <10% with BC-Predict only (P < 0.0001). Risk appointment attendance was highest for those at high risk (40.6%); 77.5% of those opted for preventive medication. DISCUSSION: We have shown that a real-time offer of breast cancer risk information (including both mammographic density and PRS) is feasible and can be delivered in reasonable time, although uptake requires personal contact. Preventive medication uptake in women newly identified at high risk is high and could improve the cost-effectiveness of risk stratification. TRIAL REGISTRATION: Retrospectively registered with clinicaltrials.gov (NCT04359420).

Breast cancer polygenic risk scores derived in White European populations are not calibrated for women of Ashkenazi Jewish descent.

PURPOSE: Polygenic risk scores (PRSs) are a major component of accurate breast cancer (BC) risk prediction but require ethnicity-specific calibration. Ashkenazi Jewish (AJ) population is assumed to be of White European (WE) origin in some commercially available PRSs despite differing effect allele frequencies (EAFs). We conducted a case-control study of WE and AJ women from the Predicting Risk of Cancer at Screening Study. The Breast Cancer in Northern Israel Study provided a separate AJ population-based case-control validation series. METHODS: All women underwent Illumina OncoArray single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]) analysis. Two PRSs were assessed, SNV142 and SNV78. A total of 221 of 2243 WE women (discovery: cases = 111; controls = 110; validation: cases = 651; controls = 1772) and 221 AJ women (cases = 121; controls = 110) were included from the UK study; the Israeli series consisted of 2045 AJ women (cases = 1331; controls = 714). EAFs were obtained from the Genome Aggregation Database. RESULTS: In the UK study, the mean SNV142 PRS demonstrated good calibration and discrimination in WE population, with mean PRS of 1.33 (95% CI 1.18-1.48) in cases and 1.01 (95% CI 0.89-1.13) in controls. In AJ women from Manchester, the mean PRS of 1.54 (1.38-1.70) in cases and 1.20 (1.08-1.32) in controls demonstrated good discrimination but overestimation of BC relative risk. After adjusting for EAFs for the AJ population, mean risk was corrected (mean SNV142 PRS cases = 1.30 [95% CI 1.16-1.44] and controls = 1.02 [95% CI 0.92-1.12]). This was recapitulated in the larger Israeli data set with good discrimination (area under the curve = 0.632 [95% CI 0.607-0.657] for SNV142). CONCLUSION: AJ women should not be given BC relative risk predictions based on PRSs calibrated to EAFs from the WE population. PRSs need to be recalibrated using AJ-derived EAFs. A simple recalibration using the mean PRS adjustment ratio likely performs well.

Quality of life after risk-reducing surgery for breast and ovarian cancer prevention: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to assess the impact of risk-reducing surgery for breast cancer and ovarian cancer prevention on quality of life. We considered risk-reducing mastectomy, risk-reducing salpingo-oophorectomy, and risk-reducing early salpingectomy and delayed oophorectomy. DATA SOURCES: We followed a prospective protocol (International Prospective Register of Systematic Reviews: CRD42022319782) and searched MEDLINE, Embase, PubMed, and Cochrane Library from inception to February 2023. STUDY ELIGIBILITY CRITERIA: We followed a PICOS (population, intervention, comparison, outcome, and study design) framework. The population included women at increased risk of breast cancer or ovarian cancer. We focused on studies reporting quality of life outcomes (health-related quality of life, sexual function, menopause symptoms, body image, cancer-related distress or worry, anxiety, or depression) after risk-reducing surgery, including risk-reducing mastectomy for breast cancer and risk-reducing salpingo-oophorectomy or risk-reducing early salpingectomy and delayed oophorectomy for ovarian cancer. METHODS: We used the Methodological Index for Non-Randomized Studies (MINORS) for study appraisal. Qualitative synthesis and fixed-effects meta-analysis were performed. RESULTS: A total of 34 studies were included (risk-reducing mastectomy: 16 studies; risk-reducing salpingo-oophorectomy: 19 studies; risk-reducing early salpingectomy and delayed oophorectomy: 2 studies). Health-related quality of life was unchanged or improved in 13 of 15 studies after risk-reducing mastectomy (N=986) and 10 of 16 studies after risk-reducing salpingo-oophorectomy (N=1617), despite short-term deficits (N=96 after risk-reducing mastectomy and N=459 after risk-reducing salpingo-oophorectomy). Sexual function (using the Sexual Activity Questionnaire) was affected in 13 of 16 studies (N=1400) after risk-reducing salpingo-oophorectomy in terms of decreased sexual pleasure (-1.21 [-1.53 to -0.89]; N=3070) and increased sexual discomfort (1.12 [0.93-1.31]; N=1400). Hormone replacement therapy after premenopausal risk-reducing salpingo-oophorectomy was associated with an increase (1.16 [0.17-2.15]; N=291) in sexual pleasure and a decrease (-1.20 [-1.75 to -0.65]; N=157) in sexual discomfort. Sexual function was affected in 4 of 13 studies (N=147) after risk-reducing mastectomy, but stable in 9 of 13 studies (N=799). Body image was unaffected in 7 of 13 studies (N=605) after risk-reducing mastectomy, whereas 6 of 13 studies (N=391) reported worsening. Increased menopause symptoms were reported in 12 of 13 studies (N=1759) after risk-reducing salpingo-oophorectomy with a reduction (-1.96 [-2.81 to -1.10]; N=1745) in the Functional Assessment of Cancer Therapy - Endocrine Symptoms. Cancer-related distress was unchanged or decreased in 5 of 5 studies after risk-reducing mastectomy (N=365) and 8 of 10 studies after risk-reducing salpingo-oophorectomy (N=1223). Risk-reducing early salpingectomy and delayed oophorectomy (2 studies, N=413) led to better sexual function and menopause-specific quality of life. CONCLUSION: Risk-reducing surgery may be associated with quality of life outcomes. Risk-reducing mastectomy and risk-reducing salpingo-oophorectomy reduce cancer-related distress, and do not affect health-related quality of life. Women and clinicians should be aware of body image problems after risk-reducing mastectomy, and of sexual dysfunction and menopause symptoms after risk-reducing salpingo-oophorectomy. Risk-reducing early salpingectomy and delayed oophorectomy may be a promising alternative to mitigate quality of life-related risks of risk-reducing salpingo-oophorectomy.

Impact of Multiple COVID-19 Waves on Gynaecological Cancer Services in the UK.

BACKGROUND: This study aimed to assess the impact of multiple COVID-19 waves on UK gynaecological-oncology services. METHODS: An online survey was distributed to all UK-British-Gynaecological-Cancer-Society members during three COVID-19 waves from 2020 to2022. RESULTS: In total, 51 hospitals (including 32 cancer centres) responded to Survey 1, 42 hospitals (29 centres) to Survey 2, and 39 hospitals (30 centres) to Survey 3. During the first wave, urgent referrals reportedly fell by a median of 50% (IQR = 25-70%). In total, 49% hospitals reported reduced staffing, and the greatest was noted for trainee doctors, by a median of 40%. Theatre capacity was reduced by a median of 40%. A median of 30% of planned operations was postponed. Multidisciplinary meetings were completely virtual in 39% and mixed in 65% of the total. A median of 75% of outpatient consultations were remote. By the second wave, fewer hospitals reported staffing reductions, and there was a return to pre-pandemic urgent referrals and multidisciplinary workloads. Theatre capacity was reduced by a median of 10%, with 5% of operations postponed. The third wave demonstrated worsening staff reductions similar to Wave 1, primarily from sickness. Pre-pandemic levels of urgent referrals/workload continued, with little reduction in surgical capacity. CONCLUSION: COVID-19 led to a significant disruption of gynaecological-cancer care across the UK, including reduced staffing, urgent referrals, theatre capacity, and working practice changes. Whilst disruption eased and referrals/workloads returned to normal, significant staff shortages remained in 2022, highlighting persistent capacity constraints.