Effect of screening mammography on breast cancer mortality: Quasi-experimental evidence from rollout of the Dutch population-based program with 17-year follow-up of a cohort

Quality of life of women with a screen-detected versus clinically detected breast cancer in the Netherlands: a prospective cohort study

PURPOSE

Breast cancer (BC) screening enables early detection of BC, which may lead to improved quality of life (QoL). We aim to compare QoL between women with a screen-detected and clinically detected BC in the Netherlands.

METHODS

We used data from the 'Utrecht cohort for Multiple BREast cancer intervention studies and Long-term evaluation' (UMBRELLA) between October 2013 and March 2022. Patients were categorized as screen-detected or clinically detected. We analysed three questionnaires, namely EORTC QLQ C-30, BR23, and HADS (Hospital Anxiety and Depression Scale) completed by BC patients shortly after diagnosis (T1) and one-year after treatment (T2). Independent t-tests were performed to compare QoL average differences between the two groups. Bonferroni-corrected p-value significance threshold of 0.00057 was used. The magnitude of differences was calculated using Cohen's d. The clinical relevance of QLQ-C30 differences was assessed based on interpretation guideline of EORTC-QLQ-C30 results.

RESULTS

After applying inclusion and exclusion criteria, there were 691 women with screen-detected BC and 480 with clinically detected BC. Generally, screen-detected BC patients reported a better QoL. At T1, their average QLQ-C30 summary score was higher (86.1) than clinically detected BC patients (83.0) (p < 0.0001). Cohen's d for all items ranged between 0.00 and 0.39. A few QLQ-C30 score differences were clinically relevant, indicating better outcomes in emotional functioning, general health, constipation, and fatigue for women with screen-detected BC.

CONCLUSIONS

In the Netherlands, women with screen-detected BC reported statistically significant and better QoL than women with clinically detected BC. However, clinical relevance of the differences is limited.

Targeted Screening for Cancer: Learnings and Applicability to Melanoma: A Scoping Review

This scoping review aims to systematically gather evidence from personalized cancer-screening studies across various cancers, summarize key components and outcomes, and provide implications for a future personalized melanoma-screening strategy. Peer-reviewed articles and clinical trial databases were searched for, with restrictions on language and publication date. Sixteen distinct studies were identified and included in this review. The studies' results were synthesized according to key components, including risk assessment, risk thresholds, screening pathways, and primary outcomes of interest. Studies most frequently reported about breast cancers (n = 7), followed by colorectal (n = 5), prostate (n = 2), lung (n = 1), and ovarian cancers (n = 1). The identified screening programs were evaluated predominately in Europe (n = 6) and North America (n = 4). The studies employed multiple different risk assessment tools, screening schedules, and outcome measurements, with few consistent approaches identified across the studies. The benefit-harm assessment of each proposed personalized screening program indicated that the majority were feasible and effective. The establishment of a personalized screening program is complex, but results of the reviewed studies indicate that it is feasible, can improve participation rates, and screening outcomes. While the review primarily examines screening programs for cancers other than melanoma, the insights can be used to inform the development of a personalized melanoma screening strategy.

Screening and Diagnostic Mammography During Pregnancy and Lactation: A Systematic Review of the Literature

In recent years, the age of childbearing has been increasing in Western countries, and consequently the need to conduct mammography during pregnancy and lactation is also increasing. The aim of the present study was to systematically review the existing evidence regarding the overall use of mammography during pregnancy and lactation. A systematic review of the literature was conducted in PubMed, Epistemonikos, and clinicaltrials.gov, by using the search terms "pregnancy" AND "mammography", and "lactation" AND "mammography". The review protocol was prospectively registered in PROSPERO (CRD42024543971). Initially, 1,038 articles were identified; the titles and abstracts of 441 studies were screened; 40 studies were retrieved; after assessment of full texts, 20 studies were included for data extraction and further analysis. All 20 studies were retrospective; 14 studies included women with pregnancy-associated breast cancer, five studies included women with breast symptoms during pregnancy and/or lactation and one study included young breast cancer patients under age 40. Overall, 420 diagnostic and one incidental screening mammography examinations were performed during pregnancy and/or lactation with a 78.6% cumulative detection rate of breast cancer. The role of mammography was confounded by the use of breast ultrasound in most studies. In conclusion, the use of mammography during pregnancy and lactation is based on empirical data from retrospective studies, not directly addressing this issue. Hence, well-designed, focused, prospective clinical studies are needed in order to improve existing evidence regarding the use of diagnostic and screening mammography during pregnancy and lactation.

Breast cancer: A review of risk factors and diagnosis

Breast cancer remains a complex and prevalent health concern affecting millions of individuals worldwide. This review paper presents a comprehensive analysis of the multifaceted landscape of breast cancer, elucidating the diverse spectrum of risk factors contributing to its occurrence and exploring advancements in diagnostic methodologies. Through an extensive examination of current literature, various risk factors have been identified, encompassing genetic predispositions such as BRCA mutations, hormonal influences, lifestyle factors, and reproductive patterns. Age, family history, and environmental factors further contribute to the intricate tapestry of breast cancer etiology. Moreover, this review delineates the pivotal role of diagnostic tools in the early detection and management of breast cancer. Mammography, the cornerstone of breast cancer screening, is augmented by emerging technologies like magnetic resonance imaging and molecular testing, enabling improved sensitivity and specificity in diagnosing breast malignancies. Despite these advancements, challenges persist in ensuring widespread accessibility to screening programs, particularly in resource-limited settings. In conclusion, this review underscores the importance of understanding diverse risk factors in the development of breast cancer and emphasizes the critical role of evolving diagnostic modalities in enhancing early detection. The synthesis of current knowledge in this review aims to contribute to a deeper comprehension of breast cancer's multifactorial nature and inform future directions in research, screening strategies, and preventive interventions.

Effects of organized screening programs on breast cancer screening, incidence, and mortality in Europe

We link data on regional Organized Screening Programs (OSPs) throughout Europe with survey data and population-based cancer registries to estimate effects of OSPs on breast cancer screening (mammography), incidence, and mortality. Identification is from regional variation in the existence and timing of OSPs, and in their age-eligibility criteria. We estimate that OSPs, on average, increase mammography by 25 percentage points, increase breast cancer incidence by 16% five years after the OSPs implementation, and reduce breast cancer mortality by about 10% ten years after.

Participation and adherence to mammography screening in the Capital Region of Denmark: The importance of age over time

Mammography screening's effectiveness depends on high participation levels. Understanding adherence patterns over time is important for more accurately predicting future effectiveness. This study analyzed longitudinal adherence to the biennial invitations in the Capital Region of Denmark from 2008-2017. We analyzed participation rates for five-year age groups along with their percent changes in each invitation round using linear regressions. Participation in the mammography screening program increased from 73.1% to 83.1% from 2008-2017. The participation rate among all age groups increased from the first to the fifth round, with the oldest age group having the largest increase (average percent change = 3.66; p-value = 0.03).

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Is mammography screening an effective public health intervention? Evidence from a natural experiment

Population-based breast screening programmes aim to improve clinical outcomes, alleviate health inequalities, and reduce healthcare costs. However, while screening can bring about immediate changes in mode of presentation and stage at diagnosis of breast cancer cases, the benefits and harms of these programmes can only be observed at a population level, and only over a long enough timeframe for the cascade of events triggered by screening to culminate in disease-specific mortality reductions. In this paper we exploit a natural experiment resulting from the phased geographic rollout of a national mammography screening programme to examine the impact of screening on breast cancer outcomes from both a patient cohort and a population perspective. Using data on 33,722 breast cancer cases over the period 1994-2011, we employ a difference-in-differences research design using ten-year follow-up data for cases diagnosed before and after the introduction of the programme in screened and unscreened regions. We conclude that although the programme produced the intended intermediate effects on breast cancer presentation and incidence, these failed to translate into significant decreases in overall population-level mortality, though screening may have helped to reduce socioeconomic disparities in late stage breast cancer incidence.

Are there employment and income gains of a national breast cancer screening program?

BACKGROUND

The Dutch national breast cancer screening program invites women aged 50-75 for screening. By detecting the disease in an early phase, the program aims to achieve lower breast cancer mortality and improve breast cancer survivors' health. Arguably, the latter also improves the employability of diagnosed women.

OBJECTIVE

This study investigates the effects of the Dutch national breast cancer screening program on diagnosed women's employment and income.

METHODS

The empirical analysis uses data of 229,357 women aged 40-59, of whom 10,515 were diagnosed with breast cancer at an age in the range 47-53. A regression-based difference-in-differences estimator is used to identify program effects by comparing outcomes for women diagnosed at ages 47-49 with the outcomes for those diagnosed at ages 50-53. The empirical models account for individual fixed effects, and for age and year fixed effects by using a control group of women who were not diagnosed with breast cancer.

RESULTS

Women's employment rates declined in the six-year period after a breast cancer diagnosis with, on average, about 3 percentage points and their incomes declined with, on average, about 5% over this period. The empirical evidence, based on a comparison of outcomes for women diagnosed at ages 47-49 with the outcomes for those diagnosed at ages 50-53 when covered by the breast cancer screening program, does not support that these declines in employment and income were affected by the program. The evidence also does not support short or medium-term survival gains of the program.

CONCLUSIONS

The findings of this study suggest that the Dutch national breast cancer screening program yields no discernible short or medium-term employment and income gains for women diagnosed with breast cancer.

Artificial intelligence for breast cancer analysis: Trends & directions

Breast cancer is one of the leading causes of death among women. Early detection of breast cancer can significantly improve the lives of millions of women across the globe. Given importance of finding solution/framework for early detection and diagnosis, recently many AI researchers are focusing to automate this task. The other reasons for surge in research activities in this direction are advent of robust AI algorithms (deep learning), availability of hardware that can run/train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths and limitations. It also enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade to detect breast cancer using various imaging modalities. Primarily, in this article we have focused on reviewing frameworks that have reported results using mammograms as it is the most widely used breast imaging modality that serves as the first test that medical practitioners usually prescribe for the detection of breast cancer. Another reason for focusing on mammogram imaging modalities is the availability of its labelled datasets. Datasets availability is one of the most important aspects for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

Nationwide mammographic screening and breast cancer mortality in Taiwan: an interrupted time-series analysis

BACKGROUND

In Taiwan, breast cancer is the third leading cause of cancer death in women. A nationwide screening program with biennial mammography for women aged 40-69 in Taiwan was implemented since July 2004, but the impact on breast cancer mortality has not been investigated.

METHODS

The interrupted time-series analysis was used to estimate the impact of mammographic screening on temporal trends of breast cancer mortality and to calculate the level of temporal changes due to the mammographic screening.

RESULTS

The annual average percentage changes of the age-standardized breast cancer mortality rates for all women aged 40-69 were 1.06% from 1991 to 2004 (before mammographic screening) and 0.33-0.34% from 2005 to 2019 (after mammographic screening). For all women aged 40-69, the results of interrupted time-series analysis showed that the increasing trends of breast cancer mortality were all attenuated after the implementation of mammographic screening. An estimation of 2114 women prevented from death of breast cancer may be attributable to screening. For women aged 40-44, 55-59, 60-64 and 65-69, the percentage changes in mortality rates were - 12.1% (- 5.1 to - 19.6%), - 20.8% (- 16.5 to - 25.2%), - 12.8% (- 8.5 to - 17.3%) and - 13.0% (- 7.9 to - 18.3%), respectively, after screening. For women aged 45-49 and 50-54, the reduction of deaths and mortality rates of breast cancer were a little.

CONCLUSIONS

This study revealed that the nationwide screening program with biennial mammography may be associated with the attenuation of breast cancer mortality trends in women aged 40-69 in Taiwan.