The benefits and harms of breast cancer screening: an independent review

Tumor Doubling Time and Screening Interval

The goal of screening is to detect breast cancers when still curable to decrease breast cancer-specific mortality. Breast cancer screening in the United States is routinely performed with digital mammography and digital breast tomosynthesis. This article reviews breast cancer doubling time by tumor subtype and examines the impact of doubling time on breast cancer screening intervals. By the article's end, the reader will be better equipped to have informed discussions with patients and medical professionals regarding the benefits and disadvantages of the currently recommended screening mammography intervals.

Nonsurgical Management of High-Risk Lesions

This article highlights the recent publications and changing trends in practice regarding management of high-risk lesions of the breast. Traditional management has always been a surgical operation but this is recognized as overtreatment. It is recognized that overdiagnosis is inevitable but what we can control is overtreatment. Vacuum-assisted excision is now established as an alternative technique to surgery for further sampling of these high-risk lesions in the United Kingdom. Guidelines from the United Kingdom and Europe now recognize this alternative pathway, and data are available showing that vacuum-assisted excision is a safe alternative to surgery.

Understanding cardiac events in breast cancer (UCARE): pilot cardio-oncology assessment and surveillance pathway for breast cancer patients

PURPOSE

In Ireland, over 3000 patients are diagnosed with breast cancer annually, and 1 in 9 Irish women will be diagnosed with breast cancer in their lifetime. There is evidence that female breast cancer survivors are more likely to die of cardiovascular disease than their age-matched counterparts. Specific services for cancer patients suffering from cancer therapy related cardiovascular toxicity have led to a higher incidence of safe anti-cancer treatment completion. Such services are not widely available in our jurisdiction, and the purpose of this trial is to remedy this situation.

METHODS

This protocol describes a prospective, single arm, pilot feasibility study implementing a dedicated Cardio-Oncology assessment and surveillance pathway for patients receiving multimodal breast cancer treatment. It incorporates novel biomarker and radiomic surveillance and monitoring approaches for cancer-therapy related cardiac dysfunction into routine care for breast cancer patients undergoing adjuvant systemic chemotherapy.

RESULTS

Declaration of results will via peer reviewed academic journals, and communicated directly to key knowledge users both nationally and internationally. This engagement will be critical to enable to healthcare services and policy sector make informed decisions or valuable changes to clinical practice, expenditure and/or systems development to support specialized Cardio-Oncology clinical pathways. All data is to be made available upon request.

CONCLUSION

Dedicated cardio-oncology services have been recommended in recent literature to improve patient outcomes. Our protocol describes a feasibility study into the provision of such services for breast cancer.

Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network

BACKGROUND

After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS.

METHODS

884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified. A U-Net-based segmentation network was trained with 922 images and validated with 394 images. An external test dataset consisting of 39 images was annotated by two radiologists with up to 7 years of experience in breast imaging. The network's performance was compared to that of human readers using accuracy and interrater agreement (Cohen's Kappa).

RESULTS

The overall classification accuracy on the validation set after 45 epochs ranged between 88.2% and 92.6%, indicating that the model's performance is comparable to the decisions of a human reader. In 17.4% of cases, calcifications have been misclassified as post-operative clips. The interrater reliability of the model compared to the radiologists showed substantial agreement (κreader1 = 0.72, κreader2 = 0.78) while the readers compared to each other revealed a Cohen's Kappa of 0.84, thus showing near-perfect agreement.

CONCLUSIONS

With this study, we show that surgery clips can adequately be identified by an AI technique. A potential application of the proposed technique is patient triage as well as the automatic exclusion of post-operative cases from PGMI (Perfect, Good, Moderate, Inadequate) evaluation, thus improving the quality management workflow.

USPSTF Recommendations and Overdiagnosis

Overdiagnosis is the concept that some cancers detected at screening would never have become clinically apparent during a woman's lifetime in the absence of screening. This could occur if a woman dies of a cause other than breast cancer in the interval between mammographic detection and clinical detection (obligate overdiagnosis) or if a mammographically detected breast cancer fails to progress to clinical presentation. Overdiagnosis cannot be measured directly. Indirect methods of estimating overdiagnosis include use of data from randomized controlled trials (RCTs) designed to evaluate breast cancer mortality, population-based screening studies, or modeling. In each case, estimates of overdiagnosis must consider lead time, breast cancer incidence trends in the absence of screening, and accurate and predictable rates of tumor progression. Failure to do so has led to widely varying estimates of overdiagnosis. The U.S. Preventive Services Task Force (USPSTF) considers overdiagnosis a major harm of mammography screening. Their 2024 report estimated overdiagnosis using summary evaluations of 3 RCTs that did not provide screening to their control groups at the end of the screening period, along with Cancer Intervention and Surveillance Network modeling. However, there are major flaws in their evidence sources and modeling estimates, limiting the USPSTF assessment. The most plausible estimates remain those based on observational studies that suggest overdiagnosis in breast cancer screening is 10% or less and can be attributed primarily to obligate overdiagnosis and nonprogressive ductal carcinoma in situ.

Test-set results can predict participants' development in breast-screen cancer detection: An observational cohort study

Background and Aim

Test-sets are standardized assessments used to evaluate reader performance in breast screening. Understanding how test-set results affect real-world performance can help refine their use as a quality improvement tool. The aim of this study is to explore if mammographic test-set results could identify breast-screening readers who improved their cancer detection in association with test-set training.

Methods

Test-set results of 41 participants were linked to their annual cancer detection rate change in two periods oriented around their first test-set participation year. Correlation tests and a multiple linear regression model investigated the relationship between each metric in the test-set results and the change in detection rates. Additionally, participants were divided based on their improvement status between the two periods, and Mann-Whitney U test was used to determine if the subgroups differed in their test-set metrics.

Results

Test-set records indicated multiple significant correlations with the change in breast cancer detection rate: a moderate positive correlation with sensitivity (0.688, p < 0.001), a moderate negative correlation with specificity (-0.528, p < 0.001), and a low to moderate positive correlation with lesion sensitivity (0.469, p = 0.002), and the number of years screen-reading mammograms (0.365, p = 0.02). In addition, the overall regression was statistically significant (F (2,38) = 18.456 p < 0.001), with an R² of 0.493 (adjusted R² = 0.466) based on sensitivity (F = 27.132, p < 0.001) and specificity (F = 9.78, p = 0.003). Subgrouping the cohort based on the change in cancer detection indicated that the improved group is significantly higher in sensitivity (p < 0.001) and lesion sensitivity (p = 0.02) but lower in specificity (p = 0.003).

Conclusion

Sensitivity and specificity are the strongest test-set performance measures to predict the change in breast cancer detection in real-world breast screening settings following test-set participation.

Joint ABS-UKCGG-CanGene-CanVar consensus regarding the use of CanRisk in clinical practice

BACKGROUND

The CanRisk tool, which operationalises the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is used by Clinical Geneticists, Genetic Counsellors, Breast Oncologists, Surgeons and Family History Nurses for breast cancer risk assessments both nationally and internationally. There are currently no guidelines with respect to the day-to-day clinical application of CanRisk and differing inputs to the model can result in different recommendations for practice.

METHODS

To address this gap, the UK Cancer Genetics Group in collaboration with the Association of Breast Surgery and the CanGene-CanVar programme held a workshop on 16th of May 2023, with the aim of establishing best practice guidelines.

RESULTS

Using a pre-workshop survey followed by structured discussion and in-meeting polling, we achieved consensus for UK best practice in use of CanRisk in making recommendations for breast cancer surveillance, eligibility for genetic testing and the input of available information to undertake an individualised risk assessment.

CONCLUSIONS

Whilst consensus recommendations were achieved, the meeting highlighted some of the barriers limiting the use of CanRisk in clinical practice and identified areas that require further work and collaboration with relevant national bodies and policy makers to incorporate wider use of CanRisk into routine breast cancer risk assessments.

Improved PAA algorithm for breast mass detection in mammograms

Mammography screening is instrumental in the early detection and diagnosis of breast cancer by identifying masses in mammograms. With the rapid development of deep learning, numerous deep learning-based object detection algorithms have been explored for mass detection studies. However, these methods often yield a high false positive rate per image (FPPI) while achieving a high true positive rate (TPR). To maintain a higher TPR while also ensuring lower FPPI, we improved the Probability Anchor Assignment (PAA) algorithm to enhance the detection capability for mammographic characteristics with our previous work. We considered three dimensions: the backbone network, feature fusion module, and dense detection heads. The final experiment showed the effectiveness of the proposed method, and the TPR/FPPI values of the final improved PAA algorithm were 0.96/0.56 on the INbreast datasets. Compared to other methods, our method stands distinguished with its effectiveness in addressing the imbalance between positive and negative classes in cases of single lesion detection.

Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer

Background Retrospective studies have suggested that using artificial intelligence (AI) may decrease the workload of radiologists while preserving mammography screening performance. Purpose To compare workload and screening performance for two cohorts of women who underwent screening before and after AI system implementation. Materials and Methods This retrospective study included 50-69-year-old women who underwent biennial mammography screening in the Capital Region of Denmark. Before AI system implementation (October 1, 2020, to November 17, 2021), all screenings involved double reading. For screenings conducted after AI system implementation (November 18, 2021, to October 17, 2022), likely normal screenings (AI examination score ≤5 before May 3, 2022, or ≤7 on or after May 3, 2022) were single read by one of 19 senior full-time breast radiologists. The remaining screenings were read by two radiologists with AI-assisted decision support. Biopsy and surgical outcomes were retrieved between October 1, 2020, and April 15, 2023, ensuring at least 180 days of follow-up. Screening metrics were compared using the χ2 test. Reading workload reduction was measured as saved screening reads. Results In total, 60 751 and 58 246 women were screened before and after AI system implementation, respectively (median age, 58 years [IQR, 54-64 years] for both cohorts), with a median screening interval before AI of 845 days (IQR, 820-878 days) and with AI of 993 days (IQR, 968-1013 days; P < .001). After AI system implementation, the recall rate decreased by 20.5% (3.09% before AI [1875 of 60 751] vs 2.46% with AI [1430 of 58 246]; P < .001), the cancer detection rate increased (0.70% [423 of 60 751] vs 0.82% [480 of 58 246]; P = .01), the false-positive rate decreased (2.39% [1452 of 60 751] vs 1.63% [950 of 58 246]; P < .001), the positive predictive value increased (22.6% [423 of 1875] vs 33.6% [480 of 1430]; P < .001), the rate of small cancers (≤1 cm) increased (36.6% [127 of 347] vs 44.9% [164 of 365]; P = .02), the rate of node-negative cancers was unchanged (76.7% [253 of 330] vs 77.8% [273 of 351]; P = .73), and the rate of invasive cancers decreased (84.9% [359 of 423] vs 79.6% [382 of 480]; P = .04). The reading workload was reduced by 33.5% (38 977 of 116 492 reads). Conclusion In a population-based mammography screening program, using AI reduced the overall workload of breast radiologists while improving screening performance. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Lee and Friedewald in this issue.

Patient preferences for breast cancer screening: a systematic review update to inform recommendations by the Canadian Task Force on Preventive Health Care

BACKGROUND

Different guideline panels, and individuals, may make different decisions based in part on their preferences. Preferences for or against an intervention are viewed as a consequence of the relative importance people place on the expected or experienced health outcomes it incurs. These findings can then be considered as patient input when balancing effect estimates on benefits and harms reported by empirical evidence on the clinical effectiveness of screening programs. This systematic review update examined the relative importance placed by patients on the potential benefits and harms of mammography-based breast cancer screening to inform an update to the 2018 Canadian Task Force on Preventive Health Care's guideline on screening.

METHODS

We screened all articles from our previous review (search December 2017) and updated our searches to June 19, 2023 in MEDLINE, PsycINFO, and CINAHL. We also screened grey literature, submissions by stakeholders, and reference lists. The target population was cisgender women and other adults assigned female at birth (including transgender men and nonbinary persons) aged ≥ 35 years and at average or moderately increased risk for breast cancer. Studies of patients with breast cancer were eligible for health-state utility data for relevant outcomes. We sought three types of data, directly through (i) disutilities of screening and curative treatment health states (measuring the impact of the outcome on one's health-related quality of life; utilities measured on a scale of 0 [death] to 1 [perfect health]), and (ii) other preference-based data, such as outcome trade-offs, and indirectly through (iii) the relative importance of benefits versus harms inferred from attitudes, intentions, and behaviors towards screening among patients provided with estimates of the magnitudes of benefit(s) and harms(s). For screening, we used machine learning as one of the reviewers after at least 50% of studies had been reviewed in duplicate by humans; full-text selection used independent review by two humans. Data extraction and risk of bias assessments used a single reviewer with verification. Our main analysis for utilities used data from utility-based health-related quality of life tools (e.g., EQ-5D) in patients; a disutility value of about 0.04 can be considered a minimally important value for the Canadian public. When suitable, we pooled utilities and explored heterogeneity. Disutilities were calculated for screening health states and between different treatment states. Non-utility data were grouped into categories, based on outcomes compared (e.g. for trade-off data), participant age, and our judgements of the net benefit of screening portrayed by the studies. Thereafter, we compared and contrasted findings while considering sample sizes, risk of bias, subgroup findings and data on knowledge scores, and created summary statements for each data set. Certainty assessments followed GRADE guidance for patient preferences and used consensus among at least two reviewers.

FINDINGS

Eighty-two studies (38 on utilities) were included. The estimated disutilities were 0.07 for a positive screening result (moderate certainty), 0.03-0.04 for a false positive (FP; "additional testing" resolved as negative for cancer) (low certainty), and 0.08 for untreated screen-detected cancer (moderate certainty) or (low certainty) an interval cancer. At ≤12 months, disutilities of mastectomy (vs. breast-conserving therapy), chemotherapy (vs. none) (low certainty), and radiation therapy (vs. none) (moderate certainty) were 0.02-0.03, 0.02-0.04, and little-to-none, respectively, though in each case findings were somewhat limited in their applicability. Over the longer term, there was moderate certainty for little-to-no disutility from mastectomy versus breast-conserving surgery/lumpectomy with radiation and from radiation. There was moderate certainty that a majority (>50%) and possibly a large majority (>75%) of women probably accept up to six cases of overdiagnosis to prevent one breast-cancer death; there was some uncertainty because of an indication that overdiagnosis was not fully understood by participants in some cases. Low certainty evidence suggested that a large majority may accept that screening may reduce breast-cancer but not all-cause mortality, at least when presented with relatively high rates of breast-cancer mortality reductions (n = 2; 2 and 5 fewer per 1000 screened), and at least a majority accept that to prevent one breast-cancer death at least a few hundred patients will receive a FP result and 10-15 will have a FP resolved through biopsy. An upper limit for an acceptable number of FPs was not evaluated. When using data from studies assessing attitudes, intentions, and screening behaviors, across all age groups but most evident for women in their 40s, preferences reduced as the net benefit presented by study authors decreased in magnitude. In a relatively low net-benefit scenario, a majority of patients in their 40s may not weigh the benefits as greater than the harms from screening whereas for women in their 50s a large majority may prefer screening (low certainty evidence for both ages). There was moderate certainty that a large majority of women 50 years of age and 50 to 69 years of age, who have usually experienced screening, weigh the benefits as greater than the harms from screening in a high net-benefit scenario. A large majority of patients aged 70-71 years who have recently screened probably think the benefits outweigh the harms of continuing to screen. A majority of women in their mid-70s to early 80s may prefer to continue screening.

CONCLUSIONS

Evidence across a range of data sources on how informed patients value the potential outcomes from breast-cancer screening will be useful during decision-making for recommendations. The evidence suggests that all of the outcomes examined have importance to women of any age, that there is at least some and possibly substantial (among those in their 40s) variability across and within age groups about the acceptable magnitude of effects across outcomes, and that provision of easily understandable information on the likelihood of the outcomes may be necessary to enable informed decision making. Although studies came from a wide range of countries, there were limited data from Canada and about whether findings applied well across an ethnographically and socioeconomically diverse population.

SYSTEMATIC REVIEW REGISTRATION

Protocol available at Open Science Framework https://osf.io/xngsu/ .

Breast cancer patients enrolled in the Swiss mammography screening program "donna" demonstrate prolonged survival

STUDY GOAL

We compared the survival rates of women with breast cancer (BC) detected within versus outside the mammography screening program (MSP) "donna".

METHODS

We merged data from the MSP with the data from corresponding cancer registries to categorize BC cases as within MSP (screen-detected and interval carcinomas) and outside the MSP. We analyzed the tumor stage distribution, tumor characteristics and the survival of the women. We further estimated hazard ratios using Cox-regressions to account for different characteristics between groups and corrected the survival rates for lead-time bias.

RESULTS

We identified 1057 invasive (ICD-10: C50) and in-situ (D05) BC cases within the MSP and 1501 outside the MSP between 2010 and 2019 in the Swiss cantons of St. Gallen and Grisons. BC within the MSP had a higher share of stage I carcinoma (46.5% vs. 33.0%; p < 0.01), a smaller (mean) tumor size (19.1 mm vs. 24.9 mm, p < 0.01), and fewer recurrences and metastases in the follow-up period (6.7% vs. 15.6%, p < 0.01). The 10-year survival rates were 91.4% for women within and 72.1% for women outside the MSP (p < 0.05). Survival difference persisted but decreased when women within the same tumor stage were compared. Lead-time corrected hazard ratios for the MSP accounted for age, tumor size and Ki-67 proliferation index were 0.550 (95% CI 0.389, 0.778; p < 0.01) for overall survival and 0.469 (95% CI 0.294, 0.749; p < 0.01) for BC related survival.

CONCLUSION

Women participating in the "donna" MSP had a significantly higher overall and BC related survival rate than women outside the program. Detection of BC at an earlier tumor stage only partially explains the observed differences.

Minimally invasive treatment of early, good prognosis breast cancer-is this feasible?

Breast cancer screening programmes frequently detect early, good prognosis breast cancers with significant treatment burden for patients, and associated health-cost implications. Emerging evidence suggests a role for minimally invasive techniques in the management of these patients enabling many women to avoid surgical intervention. Minimally invasive techniques include vacuum-assisted excision, cryoablation, and radiofrequency ablation. We review published evidence in relation to the risks and benefits of each technique and discuss ongoing trials. Data to date are promising, and we predict a trend towards minimally invasive treatment for early, good-prognosis breast cancer as technical skills, suitability criteria, and follow-up protocols are established.

ESR Essentials: screening for breast cancer - general recommendations by EUSOBI

Breast cancer is the most frequently diagnosed cancer in women accounting for about 30% of all new cancer cases and the incidence is constantly increasing. Implementation of mammographic screening has contributed to a reduction in breast cancer mortality of at least 20% over the last 30 years. Screening programs usually include all women irrespective of their risk of developing breast cancer and with age being the only determining factor. This approach has some recognized limitations, including underdiagnosis, false positive cases, and overdiagnosis. Indeed, breast cancer remains a major cause of cancer-related deaths in women undergoing cancer screening. Supplemental imaging modalities, including digital breast tomosynthesis, ultrasound, breast MRI, and, more recently, contrast-enhanced mammography, are available and have already shown potential to further increase the diagnostic performances. Use of breast MRI is recommended in high-risk women and women with extremely dense breasts. Artificial intelligence has also shown promising results to support risk categorization and interval cancer reduction. The implementation of a risk-stratified approach instead of a "one-size-fits-all" approach may help to improve the benefit-to-harm ratio as well as the cost-effectiveness of breast cancer screening. KEY POINTS: Regular mammography should still be considered the mainstay of the breast cancer screening. High-risk women and women with extremely dense breast tissue should use MRI for supplemental screening or US if MRI is not available. Women need to participate actively in the decision to undergo personalized screening. KEY RECOMMENDATIONS: Mammography is an effective imaging tool to diagnose breast cancer in an early stage and to reduce breast cancer mortality (evidence level I). Until more evidence is available to move to a personalized approach, regular mammography should be considered the mainstay of the breast cancer screening. High-risk women should start screening earlier; first with yearly breast MRI which can be supplemented by yearly or biennial mammography starting at 35-40 years old (evidence level I). Breast MRI screening should be also offered to women with extremely dense breasts (evidence level I). If MRI is not available, ultrasound can be performed as an alternative, although the added value of supplemental ultrasound regarding cancer detection remains limited. Individual screening recommendations should be made through a shared decision-making process between women and physicians.

Predictors of re-attendance at biennial screening mammography following a false positive referral: A study among women in the south of the Netherlands

AIM

A false positive (FP) referral after screening mammography may influence a woman's likelihood to re-attend the screening program. The impact of having a FP result in the first or subsequent screening round on re-attendance after a FP result was investigated. In addition, we aimed to study differences in re-attendance rates between women who underwent non-invasive and invasive additional examinations as part of the diagnostic work-up following a FP referral.

METHODS

A consecutive series of 13,597 women with a FP referral following biennial screening mammography in the south of the Netherlands between 2009 and 2019 was included.

RESULTS

The screening re-attendance rate was 81.2% after a FP referral, and 91.3% when also including women who had clinical mammographic follow-up. Women who received a FP referral in the first screening round were less likely to re-attend the screening programme in the following three years, compared to those with a FP test in any subsequent round (odds ratio (OR): 0.59, 95%-confidence interval (CI): 0.51-0.69). Women with a FP referral who underwent invasive examinations after referral were less likely to re-attend the screening programme than those who only received additional imaging (OR, 0.48; 95% CI 0.36-0.64).

CONCLUSION

Women with a FP referral are less likely to re-attend the screening programme if this referral occurs at their first screening round or when they undergo invasive diagnostic workup. Hospitals and screening organizations should prioritize informing women about the importance of re-attending the programme following a FP referral.

Healthcare professionals' experiences of caring for women with false-positive screening test results in the National Health Service Breast Screening Programme

BACKGROUND

Understanding healthcare professionals' (HCPs) experiences of caring for women with false-positive screening test results in the National Health Service Breast Screening Programme (NHSBSP) is important for reducing the impact of such results.

METHODS

Interviews were undertaken with 12 HCPs from a single NHSBSP unit, including advanced radiographer practitioners, breast radiographers, breast radiologists, clinical nurse specialists (CNSs), and a radiology healthcare assistant. Data were analysed thematically using Template Analysis.

RESULTS

Two themes were produced: (1) Gauging and navigating women's anxiety during screening assessment was an inevitable and necessary task for all participants. CNSs were perceived as particularly adept at this, while breast radiographers reported a lack of adequate formal training. (2) Controlling the delivery of information to women (including amount, type and timing of information). HCPs reported various communication strategies to facilitate women's information processing and retention during a distressing time.

CONCLUSIONS

Women's anxiety could be reduced through dedicated CNS support, but this should not replace support from other HCPs. Breast radiographers may benefit from more training to emotionally support recalled women. While HCPs emphasised taking a patient-centred communication approach, the use of other strategies (e.g., standardised scripts) and the constraints of the 'one-stop shop' model pose challenges to such an approach.

PATIENT AND PUBLIC CONTRIBUTION

During the study design, two Patient and Public Involvement members (women with false-positive-breast screening test results) were consulted to gain an understanding of patient perspectives and experiences of being recalled specifically in the NHSBSP. Their feedback informed the formulations of the research aim, objectives and the direction of the interview guide.

Emotion constructs and outcome measures following false positive breast screening test results: A systematic review of reporting clarity and selection rationale

OBJECTIVE

(i) To systematically identify constructs and outcome measures used to assess the emotional and mood impact of false positive breast screening test results; (ii) to appraise the reporting clarity and rationale for selecting constructs and outcome measures.

METHODS

Databases (MEDLINE, CINAHL, PsycINFO) were systematically searched from 1970. Studies using standardised and non-standardised outcome measures to evaluate the emotion or mood impact of false positive breast screening test results were eligible. A 15-item coding scheme was devised to appraise articles on clarity and rationale for selected constructs and measures.

RESULTS

Forty-seven articles were identified. The most investigated constructs were general anxiety and depression and disease-specific anxiety and worry. Twenty-two standardised general outcome questionnaire measures and three standardised disease-specific outcome questionnaire measures were identified. Twenty articles used non-standardised scales/items. Reporting of constructs and outcome measures was generally clear, but rationales for their selection were lacking. Anxiety was typically justified, but justification for depression was almost always absent. Practical and psychometric justification for selecting outcome measures was lacking, and theoretical rationale was absent.

CONCLUSIONS

Heterogeneity in constructs and measures, coupled with unclear rationale for these, impedes a thorough understanding of why there are emotional effects of false positive screening test results. This may explain the repeated practice of investigating less relevant outcomes such as depression. There is need to develop a consensual conceptual model of and standardised approach to measuring emotional impact from cancer screening test results, to address heterogeneity and other known issues of interpreting an inconsistent evidence base.

Examining the impact of COVID-19 disruptions on population-based breast cancer screening in Ireland

OBJECTIVE

Many population-based breast screening programmes temporarily suspended routine screening following the COVID-19 pandemic onset. This study aimed to describe screening mammography utilisation and the pattern of screen-detected breast cancer diagnoses following COVID-19-related screening disruptions in Ireland.

METHODS

Using anonymous aggregate data from women invited for routine screening, three time periods were examined: (1) January-December 2019, (2) January-December 2020, and (3) January-December 2021. Descriptive statistics were conducted and comparisons between groups were performed using chi-square tests.

RESULTS

In 2020, screening mammography capacity fell by 67.1% compared to 2019; recovering to 75% of mammograms performed in 2019, during 2021. Compared to 2019, for screen-detected invasive breast cancers, a reduction in Grade 1 (14.2% vs. 17.2%) and Grade 2 tumours (53.4% vs. 58.0%) and an increase in Grade 3 tumours (32.4% vs. 24.8%) was observed in 2020 (p = 0.03); whereas an increase in Grade 2 tumours (63.3% vs. 58.0%) and a reduction in Grade 3 tumours (19.6% vs. 24.8%) was found in 2021 (p = 0.02). No changes in oestrogen receptor-positive or nodal-positive diagnoses were observed; however the proportion of oestrogen/progesterone receptor-positive breast cancers significantly increased in 2020 (76.2%; p < 0.01) and 2021 (78.7%; p < 0.001) compared to 2019 (67.8%).

CONCLUSION

These findings demonstrate signs of a grade change for screen-detected invasive breast cancers early in the pandemic, with recovery evident in 2021, and without an increase in nodal positivity. Future studies are needed to determine the COVID-19 impact on long-term breast cancer outcomes including mortality.

AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net

Purpose

The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency.

Approach

The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing. The model performance was evaluated by calculating the true positive ratio (TPR), false positive ratio (FPR), F-score, intersection over union (IoU), and 95% Hausdorff distance (pixels) as they are appropriate for datasets with class imbalance.

Results

The model achieved 0.911, 0.003, 0.911, 0.900, 5.82 for TPR, FPR, F-score, IoU, and 95% Hausdorff distance, respectively.

Conclusions

The AMS-U-Net model demonstrated impressive visual and quantitative results, achieving high accuracy in mass segmentation without the need for human interaction. This capability has the potential to significantly increase clinical efficiency and workflow in DBT for breast cancer screening.

Including the method of detection for breast cancer in the Surveillance, Epidemiology, and End Results database is long overdue

Debates about breast cancer screening have continued in part because the Surveillance, Epidemiology, and End Results database, which began in 1974, has never included the method of detection so that it has been impossible to determine the role that early detection has played in the major decline in deaths from breast cancer that we have seen in the US since 1990. Method of detection should be added to the Surveillance, Epidemiology, and End Results database as soon as possible.

How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway

OBJECTIVES

To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative.

METHODS

In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative.

RESULTS

All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative.

CONCLUSION

AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting.

CLINICAL RELEVANCE STATEMENT

In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score.

KEY POINTS

• All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.

An evaluation of CT radiation doses within the Yorkshire Lung Screening Trial

OBJECTIVES

To evaluate radiation doses for all low-dose CT scans performed during the first year of a lung screening trial.

METHODS

For all lung screening scans that were performed using a CT protocol that delivered image quality meeting the RSNA QIBA criteria, radiation dose metrics, participant height, weight, gender, and age were recorded. Values of volume CT dose index (CTDIvol) and dose length product (DLP) were evaluated as a function of weight in order to assess the performance of the scan protocol across the participant cohort. Calculated effective doses were used to establish the additional lifetime attributable cancer risks arising from trial scans.

RESULTS

Median values of CTDIvol, DLP, and effective dose (IQR) from the 3521 scans were 1.1 mGy (0.70), 42.4 mGycm (24.9), and 1.15 mSv (0.67), whilst for 60-80kg participants the values were 1.0 mGy (0.30), 35.8 mGycm (11.4), and 0.97 mSv (0.31). A statistically significant correlation between CTDIvol and weight was identified for males (r = 0.9123, P < .001) and females (r = 0.9052, P < .001), however, the effect of gender on CTDIvol was not statistically significant (P = .2328) despite notable differences existing at the extremes of the weight range. The additional lifetime attributable cancer risks from a single scan were in the range 0.001%-0.006%.

CONCLUSIONS

Low radiation doses can be achieved across a typical lung screening cohort using scan protocols that have been shown to deliver high levels of image quality. The observed dose levels may be considered as typical values for lung screening scans on similar types of scanners for an equivalent participant cohort.

ADVANCES IN KNOWLEDGE

Presentation of typical radiation dose levels for CT lung screening examinations in a large UK trial. Effective radiation doses can be of the order of 1 mSv for standard sized participants. Lifetime attributable cancer risks resulting from a single low-dose CT scan did not exceed 0.006%.

Multiscale Monte Carlo simulations for dosimetry in x-ray breast imaging: Part I - Macroscopic scales

BACKGROUND

X-ray breast imaging modalities are commonly employed for breast cancer detection, from screening programs to diagnosis. Thus, dosimetry studies are important for quality control and risk estimation since ionizing radiation is used.

PURPOSE

To perform multiscale dosimetry assessments for different breast imaging modalities and for a variety of breast sizes and compositions. The first part of our study is focused on macroscopic scales (down to millimeters).

METHODS

Nine anthropomorphic breast phantoms with a voxel resolution of 0.5 mm were computationally generated using the BreastPhantom software, representing three breast sizes with three distinct values of volume glandular fraction (VGF) for each size. Four breast imaging modalities were studied: digital mammography (DM), contrast-enhanced digital mammography (CEDM), digital breast tomosynthesis (DBT) and dedicated breast computed tomography (BCT). Additionally, the impact of tissue elemental compositions from two databases were compared. Monte Carlo (MC) simulations were performed with the MC-GPU code to obtain the 3D glandular dose distribution (GDD) for each case considered with the mean glandular dose (MGD) fixed at 4 mGy (to facilitate comparisons).

RESULTS

The GDD within the breast is more uniform for CEDM and BCT compared to DM and DBT. For large breasts and high VGF, the ratio between the minimum/maximum glandular dose to MGD is 0.12/4.02 for DM and 0.46/1.77 for BCT; the corresponding results for a small breast and low VGF are 0.35/1.98 (DM) and 0.63/1.42 (BCT). The elemental compositions of skin, adipose and glandular tissue have a considerable impact on the MGD, with variations up to 30% compared to the baseline. The inclusion of tissues other than glandular and adipose within the breast has a minor impact on MGD, with differences below 2%. Variations in the final compressed breast thickness alter the shape of the GDD, with a higher compression resulting in a more uniform GDD.

CONCLUSIONS

For a constant MGD, the GDD varies with imaging modality and breast compression. Elemental tissue compositions are an important factor for obtaining MGD values, being a source of systematic uncertainties in MC simulations and, consequently, in breast dosimetry.

Sociodemographic Factors Associated with Breast Cancer Screening among Women in Serbia, National Health Survey

Background

Approximately 2.3 million female breast cancer cases were identified globally in 2020, resulting in 685,000 fatalities among women. Serbia too experiences a high breast cancer burden. Effective reduction of breast cancer incidence and mortality necessitates strategic measures encompassing the implementation of cost-effective screening technology. However, various impediments to screening implementation persist. We aimed to estimate the impact of socioeconomic factors on breast cancer screening in Serbia.

Methods

Data from the 2019 National Health Survey of the population of Serbia was. The research was a descriptive, cross-sectional analytical study by design, on a representative sample of the population of Serbia. Data from women aged 15+ yr were used to examine the demographic and socioeconomic factors associated with breast cancer screening inequalities.

Results

In Serbia the age group of women who predominantly participated in organized breast cancer screening (39.5%) were the ones aged 65+ yr. Women with a secondary education were 2.1x more likely to undergo a screening exam voluntarily (57.5%), compared to women with a higher education background (26.6%). When considering marital and financial circumstances, married/unmarried women from an affluent financial category exhibited a notably higher frequency of self-initiating a mammography (73% and 48.5%) in comparison to those financially struggling (27.6%).

Conclusion

Strong support is imperative for countries to establish prevention and early detection programs for cancer.

Isolating the effect of confounding from the observed survival benefit of screening participants - a methodological approach illustrated by data from the German mammography screening programme

BACKGROUND

Mammography screening programmes (MSP) aim to reduce breast cancer mortality by shifting diagnoses to earlier stages. However, it is difficult to evaluate the effectiveness of current MSP because analyses can only rely on observational data, comparing women who participate in screening with women who do not. These comparisons are subject to several biases: one of the most important is self-selection into the MSP, which introduces confounding and is difficult to control for. Here, we propose an approach to quantify confounding based on breast cancer survival analyses using readily available routine data sources.

METHODS

Using data from the Cancer Registry of North Rhine-Westphalia, Germany, we estimate the relative contribution of confounding to the observed survival benefit of participants of the German MSP. This is accomplished by comparing non-participants, participants with screen-detected and participants with interval breast cancers for the endpoints "death from breast cancer" and "death from all causes other than breast cancer" - the latter being assumed to be unrelated to any MSP effect. By using different contrasts, we eliminate the effects of stage shift, lead and length time bias. The association of breast cancer detection mode with survival is analysed using Cox models in 68,230 women, aged 50-69 years, with breast cancer diagnosed in 2006-2014 and followed up until 2018.

RESULTS

The hazard of dying from breast cancer was lower in participants with screen-detected cancer than in non-participants (HR = 0.21, 95% CI: 0.20-0.22), but biased by lead and length time bias, and confounding. When comparing participants with interval cancers and non-participants, the survival advantage was considerably smaller (HR = 0.62, 95% CI: 0.58-0.66), due to the elimination of stage shift and lead time bias. Finally, considering only mortality from causes other than breast cancer in the latter comparison, length time bias was minimised, but a survival advantage was still present (HR = 0.63, 95% CI: 0.56-0.70), which we attribute to confounding.

CONCLUSIONS

This study shows that, in addition to stage shift, lead and length time bias, confounding is an essential component when comparing the survival of MSP participants and non-participants. We further show that the confounding effect can be quantified without explicit knowledge of potential confounders by using a negative control outcome.

Student nurses' practices and willingness to teach relatives breast self-examination in Nigeria

Background

Breast cancer is the most common cancer and the leading cause of cancer-related death for women worldwide. Breast self-examination (BSE) is an essential, low-cost, and simple tool for detecting breast cancer early. Employing the idea of 'charity begins at home' by involving student nurses in teaching BSE to relatives will improve early detection.

Aim

To assess nursing students' practice and willingness to teach BSE to their relatives.

Setting

A college of nursing and midwifery in one state under North-Central Nigeria.

Methods

A cross-sectional descriptive design was employed. Through incidental sampling technique 197 respondents were selected from the first to the third year. Data were collected using a structured questionnaire. Descriptive and inferential analyses, with a p-value of 0.05 were conducted.

Results

Respondents indicated where they learned about BSE. There were 98.5% respondents who had heard about BSE, and 89.8% of them had good practice of BSE. However, a quarter did not teach BSE to relatives. There were no statistically significant associations noted.

Conclusion

Most of the nursing students were aware of BSE and knew how to perform it, although a quarter did not teach BSE to their relatives. Therefore, it may be necessary to sensitise nurses to cultivate the habit of teaching BSE to relatives and women in the community.

Contribution

It is crucial to provide nurses with the skills and knowledge required to carry out BSE effectively, as well as teach women how to perform it on themselves, to improve breast cancer detection rates in Nigeria.

Harms and benefits of mammographic screening for breast cancer in Brazil

INTRODUCTION

In the absence of evidence on the effect of mammographic screening on overall mortality, comparing the number of deaths avoided with the number of deaths caused by screening would be ideal, but the only existing models of this type adopt a very narrow definition of harms. The objective of the present study was to estimate the number of deaths prevented and induced by various mammography screening protocols in Brazil.

METHODS

A simulation study of cohorts of Brazilian women screened, considering various age groups and screening interval protocols, was performed based on life tables. The number of deaths avoided and caused by screening was estimated, as was the absolute risk reduction, the number needed to invite for screening-NNS, the net benefit of screening, and the ratio of "lives saved" to "lives lost". Nine possible combinations of balances between benefits and harms were performed for each protocol, in addition to other sensitivity analyses.

RESULTS AND CONCLUSIONS

The most efficient protocol was biennial screening from 60 to 69 years of age, with almost three times more deaths avoided than biennial screening from 50 to 59 years of age, with a similar number of deaths avoided by biennial screening from 50 to 69 years of age and with the greatest net benefit. Compared with the best scenario of annual screening from 40 to 49 years of age, the NNS of the protocol with biennial screening from 60 to 69 years of age was three-fold lower. Even in its best scenario, the addition of annual screening from 40 to 49 years of age to biennial screening from 50 to 69 years of age results in a decreased net benefit. However, even in the 50-69 year age group, the estimated reduction in breast cancer mortality for Brazil was half that estimated for the United Kingdom.

Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.

Screening in Patients With Dense Breasts: Comparison of Mammography, Artificial Intelligence, and Supplementary Ultrasound

BACKGROUND. Screening mammography has decreased performance in patients with dense breasts. Supplementary screening ultrasound is a recommended option in such patients, although it has yielded mixed results in prior investigations. OBJECTIVE. The purpose of this article is to compare the performance characteristics of screening mammography alone, standalone artificial intelligence (AI), ultrasound alone, and mammography in combination with AI and/or ultrasound in patients with dense breasts. METHODS. This retrospective study included 1325 women (mean age, 53 years) with dense breasts who underwent both screening mammography and supplementary breast ultrasound within a 1-month interval from January 2017 to December 2017; prior mammography and prior ultrasound examinations were available for comparison in 91.2% and 91.8%, respectively. Mammography and ultrasound examinations were interpreted by one of 15 radiologists (five staff; 10 fellows); clinical reports were used for the present analysis. A commercial AI tool was used to retrospectively evaluate mammographic examinations for presence of cancer. Screening performances were compared among mammography, AI, ultrasound, and test combinations, using generalized estimating equations. Benign diagnoses required 24 months or longer of imaging stability. RESULTS. Twelve cancers (six invasive ductal carcinoma; six ductal carcinoma in situ) were diagnosed. Mammography, standalone AI, and ultrasound showed cancer detection rates (per 1000 patients) of 6.0, 6.8, and 6.0 (all p > .05); recall rates of 4.4%, 11.9%, and 9.2% (all p < .05); sensitivity of 66.7%, 75.0%, and 66.7% (all p > .05); specificity of 96.2%, 88.7%, and 91.3% (all p < .05); and accuracy of 95.9%, 88.5%, and 91.1% (all p < .05). Mammography with AI, mammography with ultrasound, and mammography with both ultrasound and AI showed cancer detection rates of 7.5, 9.1, and 9.1 (all p > .05); recall rates of 14.9, 11.7, and 21.4 (all p < .05); sensitivity of 83.3%, 100.0%, and 100.0% (all p > .05); specificity of 85.8%, 89.1%, and 79.4% (all p < .05); and accuracy of 85.7%, 89.2%, and 79.5% (all p < .05). CONCLUSION. Mammography with supplementary ultrasound showed higher accuracy, higher specificity, and lower recall rate in comparison with mammography with AI and in comparison with mammography with both ultrasound and AI. CLINICAL IMPACT. The findings fail to show benefit of AI with respect to screening mammography performed with supplementary breast ultrasound in patients with dense breasts.

Dose to Organ at Risk and its Characteristic Variation with the Clinically Used Different Prescription Levels for Early-stage Left-sided Breast Cancer

AIMS

To evaluate the organ at risk (OAR) dose and its characteristic variation with different clinically usable prescription doses (RxD) for breast and chest wall radiotherapy in patients with early-stage left-sided breast cancer.

MATERIALS AND METHODS

In total, 145 patients with early-stage breast cancers (T1N0M0-T2N0M0) on the left side were treated with radiotherapy after a modified radical mastectomy or breast conservation surgery, with a mean age of 45.1 ± 21.6 years. The patient received 4050 cGy of field-in-field (three-dimensional conformal radiotherapy) treatment limited to the breast or chest wall, excluding the supraclavicular node, axillary node and internal mammary chain, over 15 fractions. Additional plans of 5000 cGy/25 fractions, 4500 cGy/20 fractions and 2600 cGy/5 fractions were created with no or minor changes to the original plan. Mathematical modelling was used to study the distinctive change in the dose-volume characteristics for various OARs as a function of the RxD. OAR dosages, both absolute and normalised, were expressed in terms of the RxD. The mathematical (functional) relationship between OAR doses and different prescription levels was deduced by the least squares fit method.

RESULT

The left lung mean dose, V5Gy (%), V10Gy (%) and V20Gy (%) and the heart mean dose, V10Gy (%) and V20Gy (%) were evaluated. The dose-volume parameters showed a parabolic variation (x2) with the RxD. Prescription normalised OAR doses showed a linear relationship with the RxD; relative dose increased with diminishing RxD. Normalised lung and heart mean doses exhibited saturation (linear relationship) with RxD variation. Paired sample t-test results between RxD versus all evaluated parameters were found to be statistically significant (P = 0.004). The Pearson correlation coefficient between different prescription levels for left lung mean dose (range 0.942-1.0), heart mean dose (range 1.0-1.0), left lung V5Gy (%) (range 0.987-1.0), left lung V10Gy (%) (range 0.991-0.999), heart V10Gy (%) (range 0.998-1.0).

CONCLUSION

The functional form of absolute OAR dose-volume parameters versus RxD is parabolic and the RxD normalised OAR dose-volume parameter versus RxD is a straight line with a negative slope as RxD increases. This indicates an increase in the relative OAR dose-volume parameters if the RxD is reduced. This study is the first of its kind to compare the OAR doses as a function of clinically used degenerate prescription levels. These data will help to comprehend the OAR doses while adopting a new dose fractionation regimen and reviewing the radiotherapy treatment plans.

Breast screening participation and degree of spread of invasive breast cancer at diagnosis in mental health service users: A population linkage study

BACKGROUND

Women living with mental health conditions may not have shared in improvements in breast cancer screening and care. No studies have directly examined the link between reduced screening participation and breast cancer spread in women using mental health (MH) services.

METHODS

Population-wide linkage of a population cancer register, BreastScreen register, and mental health service data set in women aged 50 to 74 years in New South Wales, Australia, from 2008 to 2017. Incident invasive breast cancers were identified. Predictors of degree of spread (local, regional, metastatic) at diagnosis were examined using partial proportional odds regression, adjusting for age, socioeconomic status, rurality, and patterns of screening participation.

RESULTS

A total of 29 966 incident cancers were identified and included 686 (2.4%) in women with MH service before cancer diagnoses. More than half of MH service users had regional or metastatic spread at diagnosis (adjusted odds ratio, 1.63; 95% CI, 1.41-1.89). MH service users had lower screening participation; however, advanced cancer was more common even when adjusting for screening status (adjusted odds ratio, 1.53; 95% CI, 1.32-1.77). Advanced cancer was more common in women with severe or persistent MH conditions.

CONCLUSIONS

Low screening participation rates explain only small part of the risk of more advanced breast cancer in women who use MH services. More study is needed to understand possible mechanisms contributing to more advanced breast cancer in women living with MH conditions. Health systems need strategies to ensure that women living with MH conditions enjoy population gains in breast cancer outcomes.

Deciphering a proteomic signature for the early detection of breast cancer from breast milk: the role of quantitative proteomics

INTRODUCTION

Breast cancer is one of the most prevalent cancers among women in the United States. Current research regarding breast milk has been focused on the composition and its role in infant growth and development. There is little information about the proteins, immune cells, and epithelial cells present in breast milk which can be indicative of the emergence of BC cells and tumors.

AREAS COVERED

We summarize all breast milk studies previously done in our group using proteomics. These studies include 1D-PAGE and 2D-PAGE analysis of breast milk samples, which include within woman and across woman comparisons to identify dysregulated proteins in breast milk and the roles of these proteins in both the development of BC and its diagnosis. Our projected outlook for the use of milk for cancer detection is also discussed.

EXPERT OPINION

Analyzing the samples by multiple methods allows one to interrogate a set of samples with various biochemical methods that complement each other, thus providing a more comprehensive proteome. Complementing methods like 1D-PAGE, 2D-PAGE, in-solution digestion and proteomics analysis with PTM-omics, peptidomics, degradomics, or interactomics will provide a better understanding of the dysregulated proteins, but also the modifications or interactions between these proteins.

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Breast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods to enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN) to construct a breast cancer detection model with a high level of accuracy. By leveraging Federated Learning (FL), this collaborative framework effectively utilizes the combined knowledge and data assets of several healthcare organizations while ensuring the protection of patient privacy and data security. The model described in this study showcases significant progress in the field of breast cancer diagnoses, with a maximum accuracy rate of 97.54%, precision of 96.5%, and recall of 98.0%, by using an optimum feature selection technique. Data augmentation approaches play a crucial role in decreasing loss and improving model performance. Significantly, the F1-Score, a comprehensive metric for evaluating performance, turns out to be 97%. This study signifies a notable advancement in the field of breast cancer screening, fostering hope for improved patient outcomes via increased accuracy and reliability. This study highlights the potential impact of collaborative learning, namely, in the field of FL, in transforming breast cancer detection. The incorporation of privacy considerations and the use of diverse data sources contribute to the advancement of early detection and the treatment of breast cancer, hence yielding significant benefits for patients on a global scale.

Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence

Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.

Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer

Mammography is the primary breast cancer screening strategy. Recent methods have been developed using the mammogram image to improve breast cancer risk prediction. However, it is unclear on the extent to which the effect of risk factors on breast cancer risk is mediated through tissue features summarized in mammogram images and the extent to which it is through other pathways. While mediation analysis has been conducted using mammographic density (a summary measure within the image), the mammogram image is not necessarily well described by a single summary measure and, in addition, such a measure provides no spatial information about the relationship between the exposure risk factor and the risk of breast cancer. Thus, to better understand the role of the mammogram images that provide spatial information about the state of the breast tissue that is causally predictive of the future occurrence of breast cancer, we propose a novel method of causal mediation analysis using mammogram image mediator while accommodating the irregular shape of the breast. We apply the proposed method to data from the Joanne Knight Breast Health Cohort and leverage new insights on the decomposition of the total association between risk factor and breast cancer risk that was mediated by the texture of the underlying breast tissue summarized in the mammogram image.

Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention

Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. The incorporation of genetic information and breast density has been shown to improve predictions for existing models, but detailed image-based features are yet to be included despite correlating with risk. Complex information can be extracted from mammograms using deep-learning algorithms, however, this is a challenging area of research, partly due to the lack of data within the field, and partly due to the computational burden. We propose an attention-based Multiple Instance Learning (MIL) model that can make accurate, short-term risk predictions from mammograms taken prior to the detection of cancer at full resolution. Current screen-detected cancers are mixed in with priors during model development to promote the detection of features associated with risk specifically and features associated with cancer formation, in addition to alleviating data scarcity issues. MAI-risk achieves an AUC of 0.747 [0.711, 0.783] in cancer-free screening mammograms of women who went on to develop a screen-detected or interval cancer between 5 and 55 months, outperforming both IBIS (AUC 0.594 [0.557, 0.633]) and VAS (AUC 0.649 [0.614, 0.683]) alone when accounting for established clinical risk factors.

Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images

Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.

Impact of Mammography Screening Frequency on Breast Cancer Mortality Rates

The frequency of mammography screening remains a topic of ongoing debate. This meta-analysis aimed to investigate the impact of annual vs. biennial mammography screenings on breast cancer mortality rates. A comprehensive search of relevant literature published up to 2021 was performed, with the primary outcome being the difference in breast cancer mortality rates between annual and biennial screenings. The extracted data included relative risks and 95% confidence intervals (CIs), with studies selected based on predetermined inclusion and exclusion criteria, emphasizing the quality of methodology and minimization of bias. Of the included studies, thirteen met the criteria, covering diverse demographic cohorts and screening frequencies. The synthesized data revealed a pattern of lower relative risk in annual screenings compared to biennial screenings across all studies. Notably, subgroup analyses indicated that age and racial background might modulate the effectiveness of screening frequency. In conclusion, this meta-analysis offers strong evidence suggesting that annual mammography screenings could be more effective than biennial screenings in reducing breast cancer mortality rates, especially in certain high-risk demographics. The results emphasize the importance of personalized, evidence-based approaches to mammography, with a call for future research to validate these findings and delve deeper into optimizing breast cancer screening strategies.

The cost-effectiveness of risk-stratified breast cancer screening in the UK

BACKGROUND

There has been growing interest in the UK and internationally of risk-stratified breast screening whereby individualised risk assessment may inform screening frequency, starting age, screening instrument used, or even decisions not to screen. This study evaluates the cost-effectiveness of eight proposals for risk-stratified screening regimens compared to both the current UK screening programme and no national screening.

METHODS

A person-level microsimulation model was developed to estimate health-related quality of life, cancer survival and NHS costs over the lifetime of the female population eligible for screening in the UK.

RESULTS

Compared with both the current screening programme and no screening, risk-stratified regimens generated additional costs and QALYs, and had a larger net health benefit. The likelihood of the current screening programme being the optimal scenario was less than 1%. No screening amongst the lowest risk group, and triannual, biennial and annual screening amongst the three higher risk groups was the optimal screening strategy from those evaluated.

CONCLUSIONS

We found that risk-stratified breast cancer screening has the potential to be beneficial for women at the population level, but the net health benefit will depend on the particular risk-based strategy.

Differences and similarities in false interpretations by AI CAD and radiologists in screening mammography

OBJECTIVE

We aimed to evaluate the false interpretations between artificial intelligence (AI) and radiologists in screening mammography to get a better understanding of how the distribution of diagnostic mistakes might change when moving from entirely radiologist-driven to AI-integrated breast cancer screening.

METHODS AND MATERIALS

This retrospective case-control study was based on a mammography screening cohort from 2008 to 2015. The final study population included screening examinations for 714 women diagnosed with breast cancer and 8029 randomly selected healthy controls. Oversampling of controls was applied to attain a similar cancer proportion as in the source screening cohort. We examined how false-positive (FP) and false-negative (FN) assessments by AI, the first reader (RAD 1) and the second reader (RAD 2), were associated with age, density, tumor histology and cancer invasiveness in a single- and double-reader setting.

RESULTS

For each reader, the FN assessments were distributed between low- and high-density females with 53 (42%) and 72 (58%) for AI; 59 (36%) and 104 (64%) for RAD 1 and 47 (36%) and 84 (64%) for RAD 2. The corresponding numbers for FP assessments were 1820 (47%) and 2016 (53%) for AI; 1568 (46%) and 1834 (54%) for RAD 1 and 1190 (43%) and 1610 (58%) for RAD 2. For ductal cancer, the FN assessments were 79 (77%) for AI CAD; with 120 (83%) for RAD 1 and with 96 (16%) for RAD 2. For the double-reading simulation, the FP assessments were distributed between younger and older females with 2828 (2.5%) and 1554 (1.4%) for RAD 1 + RAD 2; 3850 (3.4%) and 2940 (2.6%) for AI+RAD 1 and 3430 (3%) and 2772 (2.5%) for AI+RAD 2.

CONCLUSION

The most pronounced decrease in FN assessments was noted for females over the age of 55 and for high density-women. In conclusion, AI could have an important complementary role when combined with radiologists to increase sensitivity for high-density and older females.

ADVANCES IN KNOWLEDGE

Our results highlight the potential impact of integrating AI in breast cancer screening, particularly to improve interpretation accuracy. The use of AI could enhance screening outcomes for high-density and older females.

Years of life lost due to cancer in the United Kingdom from 1988 to 2017

BACKGROUND

We investigated the application of years of life lost (YLL) in routine cancer statistics using cancer mortality data from 1988 to 2017.

METHODS

Cancer mortality data for 17 cancers and all cancers in the UK from 1988 to 2017 were provided by the UK Association of Cancer Registries by sex, 5-year age group, and year. YLL, age-standardised YLL rate (ASYR) and age-standardised mortality rate (ASMR) were estimated.

RESULTS

The annual average YLL due to cancer, in the time periods 1988-1992 and 2013-2017, were about 2.2 and 2.3 million years, corresponding to 4510 and 3823 ASYR per 100,000 years, respectively. During 2013-2017, the largest number of YLL occurred in lung, bowel and breast cancer. YLL by age groups for all cancers showed a peak between 60-64 and 75-79. The relative contributions to incidence, mortality, and YLL differ between cancers. For instance, pancreas (in women and men) made up a smaller proportion of incidence (3%) but bigger proportion of mortality (6 and 5%) and YLL (5 and 6%), whereas prostate cancer (26% of incidence) contributed 13% mortality and 9% YLL.

CONCLUSION

YLL is a useful measure of the impact different cancers have on society and puts a higher weight on cancer deaths in younger individuals.

Recruiting women with ductal carcinoma in situ to a randomised controlled trial: lessons from the LORIS study

BACKGROUND

The LOw RISk DCIS (LORIS) study was set up to compare conventional surgical treatment with active monitoring in women with ductal carcinoma in situ (DCIS). Recruitment to trials with a surveillance arm is known to be challenging, so strategies to maximise patient recruitment, aimed at both patients and recruiting centres, were implemented.

METHODS

Women aged ≥ 46 years with a histologically confirmed diagnosis of non-high-grade DCIS were eligible for 1:1 randomisation to either surgery or active monitoring. Prior to randomisation, all eligible women were invited to complete: (1) the Clinical Trials Questionnaire (CTQ) examining reasons for or against participation, and (2) interviews exploring in depth opinions about the study information sheets and film. Women agreeing to randomisation completed validated questionnaires assessing health status, physical and mental health, and anxiety levels. Hospital site staff were invited to communication workshops and refresher site initiation visits to support recruitment. Their perspectives on LORIS recruitment were collected via surveys and interviews.

RESULTS

Eighty percent (181/227) of eligible women agreed to be randomised. Over 40% of participants had high anxiety levels at baseline. On the CTQ, the most frequent most important reasons for accepting randomisation were altruism and belief that the trial offered the best treatment, whilst worries about randomisation and the influences of others were the most frequent most important reasons for declining. Most women found the study information provided clear and useful. Communication workshops for site staff improved knowledge and confidence but only about half said they themselves would join LORIS if eligible. The most common recruitment barriers identified by staff were low numbers of eligible patients and patient preference.

CONCLUSIONS

Recruitment to LORIS was challenging despite strategies aimed at both patients and site staff. Ensuring that recruiting staff support the study could improve recruitment in similar future trials.

TRIAL REGISTRATION

ISRCTN27544579, prospectively registered on 22 May 2014.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

REPRESENT recommendations: improving inclusion and trust in cancer early detection research

Detecting cancer early is essential to improving cancer outcomes. Minoritized groups remain underrepresented in early detection cancer research, which means that findings and interventions are not generalisable across the population, thus exacerbating disparities in cancer outcomes. In light of these challenges, this paper sets out twelve recommendations to build relations of trust and include minoritized groups in ED cancer research. The Recommendations were formulated by a range of stakeholders at the 2022 REPRESENT consensus-building workshop and are based on empirical data, including a systematic literature review and two ethnographic case studies in the US and the UK. The recommendations focus on: Long-term relationships that build trust; Sharing available resources; Inclusive and accessible communication; Harnessing community expertise; Unique risks and benefits; Compensation and support; Representative samples; Demographic data; Post-research support; Sharing results; Research training; Diversifying research teams. For each recommendation, the paper outlines the rationale, specifications for how different stakeholders may implement it, and advice for best practices. Instead of isolated recruitment, public involvement and engagement activities, the recommendations here aim to advance mutually beneficial and trusting relationships between researchers and research participants embedded in ED cancer research institutions.

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review

OBJECTIVE

Assumptions on the natural history of ductal carcinoma in situ (DCIS) are necessary to accurately model it and estimate overdiagnosis. To improve current estimates of overdiagnosis (0-91%), the purpose of this review was to identify and analyse assumptions made in modelling studies on the natural history of DCIS in women.

METHODS

A systematic review of English full-text articles using PubMed, Embase, and Web of Science was conducted up to February 6, 2023. Eligibility and all assessments were done independently by two reviewers. Risk of bias and quality assessments were performed. Discrepancies were resolved by consensus. Reader agreement was quantified with Cohen's kappa. Data extraction was performed with three forms on study characteristics, model assessment, and tumour progression.

RESULTS

Thirty models were distinguished. The most important assumptions regarding the natural history of DCIS were addition of non-progressive DCIS of 20-100%, classification of DCIS into three grades, where high grade DCIS had an increased chance of progression to invasive breast cancer (IBC), and regression possibilities of 1-4%, depending on age and grade. Other identified risk factors of progression of DCIS to IBC were younger age, birth cohort, larger tumour size, and individual risk.

CONCLUSION

To accurately model the natural history of DCIS, aspects to consider are DCIS grades, non-progressive DCIS (9-80%), regression from DCIS to no cancer (below 10%), and use of well-established risk factors for progression probabilities (age). Improved knowledge on key factors to consider when studying DCIS can improve estimates of overdiagnosis and optimization of screening.

Data-based modeling of breast cancer and optimal therapy

Excessive accumulation of β-catenin proteins is a vital driver in the development of breast cancer. Many clinical assessments incorporating immunotherapy with targeted mRNA of β-catenin are costly endeavor. This paper develops novel mathematical models for different treatments by invoking available clinical data to calibrate models, along with the selection and evaluation of therapy strategies in a faster manner with lower cost. Firstly, in order to explore the interactions between cancer cells and the immune system within the tumor microenvironment, we construct different types of breast cancer treatment models based on RNA interference technique and immune checkpoint inhibitors, which have been proved to be an effective combined therapy in pre-clinical trials associated with the inhibition of β-catenin proteins to enhance intrinsic anti-tumor immune response. Secondly, various techniques including MCMC are adopted to estimate multiple parameters and thus simulations in agreement with experimental results sustain the validity of our models. Furthermore, the gradient descent method and particle swarm algorithm are designed to optimize therapy schemes to inhibit the growth of tumor and lower the treatment cost. Considering the mechanisms of drug resistance in vivo, simulations exhibit that therapies are ineffective resulting in cancer relapse in the prolonged time. For this reason, parametric sensitivity analysis sheds light on the choice of new treatments which indicate that, in addition to inhibiting β-catenin proteins and improving self-immunity, the injection of dendritic cells promoting immunity may provide a novel vision for the future of cancer treatment. Overall, our study provides witness of principle from a mathematical perspective to guide clinical trials and the selection of treatment regimens.

Decomposing socioeconomic disparity in the utilization of screening mammography: A cross-sectional analysis from the RaNCD cohort study

We aimed to examine the degree of socioeconomic inequality in screening mammography among Kurdish women of Iran. Data from the Ravansar Non-Communicable Diseases (RaNCD) Cohort Study were used. A total of 3,219 women aged 35-65 years were studied. The concentration index (CIn) was used to measure the magnitude of socioeconomic-related inequalities in screening mammography. Decomposition analysis was employed to calculate the contribution of each explanatory variable to the observed inequality. The participation rate for screening mammography was 19.7%. The CIn of screening mammography was 0.142 (95% CI: 0.0197, 0.0656), indicating that screening mammography is more concentrated among high-SES women. Socioeconomic status, education level and area of residence were the main contributors to the observed inequality, respectively. We found a pro-rich inequality in screening mammography among Iranian Kurdish women. For mitigating socioeconomic inequality in screening mammography policymakers should focus more on the poor and rural communities.