The effect of variable labels on deep learning models trained to predict breast density

Purpose: High breast density is associated with reduced efficacy of mammographic screening and increased risk of developing breast cancer. Accurate and reliable automated density estimates can be used for direct risk prediction and passing density related information to further predictive models. Expert reader assessments of density show a strong relationship to cancer risk but also inter-reader variation. The effect of label variability on model performance is important when considering how to utilise automated methods for both research and clinical purposes. Methods: We utilise subsets of images with density labels to train a deep transfer learning model which is used to assess how label variability affects the mapping from representation to prediction. We then create two end-to-end deep learning models which allow us to investigate the effect of label variability on the model representation formed. Results: We show that the trained mappings from representations to labels are altered considerably by the variability of reader scores. Training on labels with distribution variation removed causes the Spearman rank correlation coefficients to rise from $0.751\pm0.002$ to either $0.815\pm0.006$ when averaging across readers or $0.844\pm0.002$ when averaging across images. However, when we train different models to investigate the representation effect we see little difference, with Spearman rank correlation coefficients of $0.846\pm0.006$ and $0.850\pm0.006$ showing no statistically significant difference in the quality of the model representation with regard to density prediction. Conclusions: We show that the mapping between representation and mammographic density prediction is significantly affected by label variability. However, the effect of the label variability on the model representation is limited

Is Breast Cancer Risk Associated with Menopausal Hormone Therapy Modified by Current or Early Adulthood BMI or Age of First Pregnancy?

Menopausal hormone therapy (MHT) has an attenuated effect on breast cancer (BC) risk amongst heavier women, but there are few data on a potential interaction with early adulthood body mass index (at age 20 years) and age of first pregnancy. We studied 56,489 women recruited to the PROCAS (Predicting Risk of Cancer at Screening) study in Manchester UK, 2009-15. Cox regression models estimated the effect of reported MHT use at entry on breast cancer (BC) risk, and potential interactions with a. self-reported current body mass index (BMI), b. BMI aged 20 and c. First pregnancy >30 years or nulliparity compared with first pregnancy <30 years. Analysis was adjusted for age, height, family history, age of menarche and menopause, menopausal status, oophorectomy, ethnicity, self-reported exercise and alcohol. With median follow up of 8 years, 1663 breast cancers occurred. BC risk was elevated amongst current users of combined MHT compared to never users (Hazard ratioHR 1.64, 95% CI 1.32-2.03), risk was higher than for oestrogen only users (HR 1.03, 95% CI 0.79-1.34). Risk of current MHT was attenuated by current BMI (interaction HR 0.80, 95% CI 0.65-0.99) per 5 unit increase in BMI. There was little evidence of an interaction between MHT use, breast cancer risk and early and current BMI or with age of first pregnancy

Penetrance estimates for BRCA1, BRCA2 (also applied to Lynch syndrome) based on presymptomatic testing: a new unbiased method to assess risk?

PURPOSE: The identification of BRCA1, BRCA2 or mismatch repair (MMR) pathogenic gene variants in familial breast/ovarian/colorectal cancer families facilitates predictive genetic testing of at-risk relatives. However, controversy still exists regarding overall lifetime risks of cancer in individuals testing positive. METHODS: We assessed the penetrance of BRCA1, BRCA2, MLH1 and MSH2 mutations in men and women using Bayesian calculations based on ratios of positive to negative presymptomatic testing by 10-year age cohorts. Mutation position was also assessed for BRCA1/BRCA2. RESULTS: Using results from 2264 presymptomatic tests in first-degree relatives (FDRs) of mutation carriers in BRCA1 and BRCA2 and 646 FDRs of patients with MMR mutations, we assessed overall associated cancer penetrance to age of 68 years as 73% (95% CI 61% to 82%) for BRCA1, 60% (95% CI 49% to 71%) for BRCA2, 95% (95% CI 76% to 99%) for MLH1% and 61% (95% CI 49% to 76%) for MSH2. There was no evidence for significant penetrance for males in BRCA1 or BRCA2 families and males had equivalent penetrance to females with Lynch syndrome. Mutation position and degree of family history influenced penetrance in BRCA2 but not BRCA1. CONCLUSION: We describe a new method for assessing penetrance in cancer-prone syndromes. Results are in keeping with published prospective series and present modern-day estimates for overall disease penetrance that bypasses retrospective series biases

A novel and fully automated mammographic texture analysis for risk prediction : results from two case-control studies

BACKGROUND: The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw full field digital mammograms (FFDM). METHOD: A case-control study nested within a screening cohort (age 46-73 years) from Manchester UK was used to develop a texture feature risk score (264 cases diagnosed at the same time as mammogram of the contralateral breast, 787 controls) using the least absolute shrinkage and selection operator (LASSO) method for 112 features, and validated in a second case-control study from the same cohort but with cases diagnosed after the index mammogram (317 cases, 931 controls). Predictive ability was assessed using deviance and matched concordance index (mC). The ability to improve risk estimation beyond percent volumetric density (Volpara) was evaluated using conditional logistic regression. RESULTS: The strongest features identified in the training set were "sum average" based on the grey-level co-occurrence matrix at low image resolutions (original resolution 10.628 pixels per mm; downsized by factors of 16, 32 and 64), which had a better deviance and mC than volumetric PD. In the validation study, the risk score combining the three sum average features achieved a better deviance than volumetric PD (Deltachi2 = 10.55 or 6.95 if logarithm PD) and a similar mC to volumetric PD (0.58 and 0.57, respectively). The risk score added independent information to volumetric PD (Deltachi2 = 14.38, p = 0.0008). CONCLUSION: Textural features based on digital mammograms improve risk assessment beyond volumetric percentage density. The features and risk score developed need further investigation in other settings

Is Breast Cancer Risk Associated with Menopausal Hormone Therapy Modified by Current or Early Adulthood BMI or Age of First Pregnancy?

Menopausal hormone therapy (MHT) has an attenuated effect on breast cancer (BC) risk amongst heavier women, but there are few data on a potential interaction with early adulthood body mass index (at age 20 years) and age of first pregnancy. We studied 56,489 women recruited to the PROCAS (Predicting Risk of Cancer at Screening) study in Manchester UK, 2009-15. Cox regression models estimated the effect of reported MHT use at entry on breast cancer (BC) risk, and potential interactions with a. self-reported current body mass index (BMI), b. BMI aged 20 and c. First pregnancy >30 years or nulliparity compared with first pregnancy 30 years. Analysis was adjusted for age, height, family history, age of menarche and menopause, menopausal status, oophorectomy, ethnicity, self-reported exercise and alcohol. With median follow up of 8 years, 1663 breast cancers occurred. BC risk was elevated amongst current users of combined MHT compared to never users (Hazard ratioHR 1.64, 95% CI 1.32–2.03), risk was higher than for oestrogen only users (HR 1.03, 95% CI 0.79–1.34). Risk of current MHT was attenuated by current BMI (interaction HR 0.80, 95% CI 0.65–0.99) per 5 unit increase in BMI. There was little evidence of an interaction between MHT use, breast cancer risk and early and current BMI or with age of first pregnancy

Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds

Background The percentage of mammographic dense tissue (PD) defined by pixel value threshold is a well-established risk factor for breast cancer. Recently there has been some evidence to suggest that an increased threshold based on visual assessment could improve risk prediction. It is unknown, however, whether this also applies to volumetric density using digital raw mammograms. Method Two case-control studies nested within a screening cohort (ages of participants 46–73 years) from Manchester UK were used. In the first study (317 cases and 947 controls) cases were detected at the first screen; whereas in the second study (318 cases and 935 controls), cases were diagnosed after the initial mammogram. Volpara software was used to estimate dense tissue height at each pixel point, and from these, volumetric and area-based PD were computed at a range of thresholds. Volumetric and area-based PDs were evaluated using conditional logistic regression, and their predictive ability was assessed using the Akaike information criterion (AIC) and matched concordance index (mC). Results The best performing volumetric PD was based on a threshold of 5 mm of dense tissue height (which we refer to as VPD5), and the best areal PD was at a threshold level of 6 mm (which we refer to as APD6), using pooled data and in both studies separately. VPD5 showed a modest improvement in prediction performance compared to the original volumetric PD by Volpara with ΔAIC = 5.90 for the pooled data. APD6, on the other hand, shows much stronger evidence for better prediction performance, with ΔAIC = 14.52 for the pooled data, and mC increased slightly from 0.567 to 0.577. Conclusion These results suggest that imposing a 5 mm threshold on dense tissue height for volumetric PD could result in better prediction of cancer risk. There is stronger evidence that area-based density with a 6 mm threshold gives better prediction than the original volumetric density metric

Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk.

We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo-DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists' visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48-55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5-4.4) and a 3.6 (95% CI 1.4-9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists' visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580-0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623-0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists' visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity

Mammographic density change in a cohort of premenopausal women receiving tamoxifen for breast cancer prevention over 5 years.

BACKGROUND: A decrease in breast density due to tamoxifen preventive therapy might indicate greater benefit from the drug. It is not known whether mammographic density continues to decline after 1 year of therapy, or whether measures of breast density change are sufficiently stable for personalised recommendations. METHODS: Mammographic density was measured annually over up to 5 years in premenopausal women with no previous diagnosis of breast cancer but at increased risk of breast cancer attending a family-history clinic in Manchester, UK (baseline 2010-2013). Tamoxifen (20 mg/day) for prevention was prescribed for up to 5 years in one group; the other group did not receive tamoxifen and were matched by age. Fully automatic methods were used on mammograms over the 5-year follow-up: three area-based measures (NN-VAS, Stratus, Densitas) and one volumetric (Volpara). Additionally, percentage breast density at baseline and first follow-up mammograms was measured visually. The size of density declines at the first follow-up mammogram and thereafter was estimated using a linear mixed model adjusted for age and body mass index. The stability of density change at 1 year was assessed by evaluating mean squared error loss from predictions based on individual or mean density change at 1 year. RESULTS: Analysis used mammograms from 126 healthy premenopausal women before and as they received tamoxifen for prevention (median age 42 years) and 172 matched controls (median age 41 years), with median 3 years follow-up. There was a strong correlation between percentage density measures used on the same mammogram in both the tamoxifen and no tamoxifen groups (all correlation coeficients > 0.8). Tamoxifen reduced mean breast density in year 1 by approximately 17-25% of the inter-quartile range of four automated percentage density measures at baseline, and from year 2, it decreased further by approximately 2-7% per year. Predicting change at 2 years using individual change at 1 year was approximately 60-300% worse than using mean change at 1year. CONCLUSIONS: All measures showed a consistent and large average tamoxifen-induced change in density over the first year, and a continued decline thereafter. However, these measures of density change at 1 year were not stable on an individual basis

What are the benefits and harms of risk stratified screening as part of the NHS breast screening Programme? Study protocol for a multi-site non-randomised comparison of BC-predict versus usual screening (NCT04359420)

From Springer Nature via Jisc Publications RouterHistory: received 2020-05-01, accepted 2020-06-09, registration 2020-06-09, pub-electronic 2020-06-18, online 2020-06-18, collection 2020-12Publication status: PublishedFunder: Programme Grants for Applied Research; doi: http://dx.doi.org/10.13039/501100007602; Grant(s): RP-PG-1214-20016Funder: Manchester Biomedical Research Centre; doi: http://dx.doi.org/10.13039/100014653; Grant(s): IS-BRC-1215-20007Funder: Genesis Research Trust; doi: http://dx.doi.org/10.13039/100012156; Grant(s): GA15-003Funder: Prevent Breast Cancer; Grant(s): GA18-001Funder: Breast Cancer Now; Grant(s): 2018RP005Abstract: Background: In principle, risk-stratification as a routine part of the NHS Breast Screening Programme (NHSBSP) should produce a better balance of benefits and harms. The main benefit is the offer of NICE-approved more frequent screening and/ or chemoprevention for women who are at increased risk, but are unaware of this. We have developed BC-Predict, to be offered to women when invited to NHSBSP which collects information on risk factors (self-reported information on family history and hormone-related factors via questionnaire; mammographic density; and in a sub-sample, Single Nucleotide Polymorphisms). BC-Predict produces risk feedback letters, inviting women at high risk (≥8% 10-year) or moderate risk (≥5 to < 8% 10-year) to have discussion of prevention and early detection options at Family History, Risk and Prevention Clinics. Despite the promise of systems such as BC-Predict, there are still too many uncertainties for a fully-powered definitive trial to be appropriate or ethical. The present research aims to identify these key uncertainties regarding the feasibility of integrating BC-Predict into the NHSBSP. Key objectives of the present research are to quantify important potential benefits and harms, and identify key drivers of the relative cost-effectiveness of embedding BC-Predict into NHSBSP. Methods: A non-randomised fully counterbalanced study design will be used, to include approximately equal numbers of women offered NHSBSP (n = 18,700) and BC-Predict (n = 18,700) from selected screening sites (n = 7). In the initial 8-month time period, women eligible for NHSBSP will be offered BC-Predict in four screening sites. Three screening sites will offer women usual NHSBSP. In the following 8-months the study sites offering usual NHSBSP switch to BC-Predict and vice versa. Key potential benefits including uptake of risk consultations, chemoprevention and additional screening will be obtained for both groups. Key potential harms such as increased anxiety will be obtained via self-report questionnaires, with embedded qualitative process analysis. A decision-analytic model-based cost-effectiveness analysis will identify the key uncertainties underpinning the relative cost-effectiveness of embedding BC-Predict into NHSBSP. Discussion: We will assess the feasibility of integrating BC-Predict into the NHSBSP, and identify the main uncertainties for a definitive evaluation of the clinical and cost-effectiveness of BC-Predict. Trial registration: Retrospectively registered with clinicaltrials.gov (NCT04359420)