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The global breast cancer screening tests market was valued at US$ 1.9 Billion in 2022 and is expected to reach US$ 4.4 Billion by 2033. The imaging tests segment with around 54.3% value share, has topped the global market within the product category in 2022 and is expected to grow at a CAGR of close to 7.8% over the forecast period (2023 to 2033).
Data Points | Market Insights |
---|---|
Market Value 2022 |
US$ 1.9 Billion |
Market Value 2023 |
US$ 2.1 Billion |
Market Value 2033 |
US$ 4.4 Billion |
CAGR 2023 to 2033 |
7.8% |
Market Share of Top 5 Countries |
54.5% |
Key Market Players |
AstraZeneca, Novartis, Sanofi, Pfizer, Bayer AG, GlaxoSmithKline plc, and Siemens Healthineers, Hologic Inc. |
Report Scope as Per Breast Cancer Screening Test Industry Analysis
Attribute | Details |
---|---|
Forecast Period |
2023 to 2033 |
Historical Data Available for |
2017 to 2022 |
Market Analysis |
US$ Million for Value |
Key Regions Covered |
North America, Latin America, Europe, South Asia, East Asia, Oceania, Middle East and Africa (MEA) |
Key Countries Covered |
USA, Canada, Brazil, Mexico, Argentina, Germany, Italy, France, UK, Spain, BENELUX, Russia, China, Japan, South Korea, India, Indonesia, Thailand, Philippines, Malaysia, Australia, New Zealand, GCC countries, Türkiye, Northern Africa and South Africa. |
Key Market Segments Covered |
Diagnostic Test Type, End User, and Region |
Key Companies Profiled |
|
Report Coverage |
Market Forecast, Competition Intelligence, DROT Analysis, Market Dynamics and Challenges, Strategic Growth Initiatives |
Pricing |
Available upon Request |
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The Breast Cancer Screening Tests Market is Segmented by Test (Genomic Tests and Imaging Tests) and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America). The report offers the value (in USD million) for the above segments.
According to the WHO, breast cancer is the most commonly occurring cancer worldwide. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet breast cancer mortality in high-income countries has dropped by 40% since the 1980s when health authorities implemented regular mammography screening in age groups considered at risk. Early detection and treatment are critical to reducing cancer fatalities, and your machine learning skills could help streamline the process radiologists use to evaluate screening mammograms. Currently, early detection of breast cancer requires the expertise of highly-trained human observers, making screening mammography programs expensive to conduct. RSNA collected screening mammograms and supporting information from two sites, totaling just under 20,000 imaging studies.
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South Korea increased 1.7points of Breast Cancer Screening (Survey) in 2019, compared to a year earlier.
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The report covers North America Breast Cancer Diagnosis Test and the market is Segmented by Test (Genomic Tests and Imaging Tests) and Geography (United States, Canada, and Mexico). The market provides the value (in USD million) for the above-mentioned segments.
All variables are presented in an excel sheet (xlxs). The objective of this study is to analyze whether women's level of knowledge about the benefits and harms of screening, their time perspective and their concern about breast cancer providing detailed information on the benefits and adverse effects of breast cancer screening affects, directly or indirectly, affects the intention to participate in it. The database includes variables that allow us to construct the outcome of informed choice, a variable that combines knowledge, attitudes and intentions. Other variables that reflect women's perceptions of how their decision to participate, or not, in screening affects them, now or in the future, are also collected: decisional conflict; anxiety about screening participation; concern about breast cancer; anticipated regret; time perspective; perceived importance of the benefits/harms of screening.
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The aim of this study was to assess the acceptability and feasibility of offering risk-based breast cancer screening and its integration into regular clinical practice. A single-arm proof-of-concept trial was conducted with a sample of 387 women aged 40–50 years residing in the city of Lleida (Spain). The study intervention consisted of breast cancer risk estimation, risk communication and screening recommendations, and a follow-up. A polygenic risk score with 83 single nucleotide polymorphisms was used to update the Breast Cancer Surveillance Consortium risk model and estimate the 5-year absolute risk of breast cancer. The women expressed a positive attitude towards varying the frequency of breast screening according to individual risk and, especially, more frequently inviting women at higher-than-average risk. A lower intensity screening for women at lower risk was not as welcome, although half of the participants would accept it. Knowledge of the benefits and harms of breast screening was low, especially with regard to false positives and overdiagnosis. The women expressed a high understanding of individual risk and screening recommendations. The participants’ intention to participate in risk-based screening and satisfaction at 1-year were very high.
MethodsFrom January 2019 to February 2021, 387 women aged 40 to 50 years were enrolled in the study. Potential participants were the 2038 women living in the “Primer de Maig” Basic Health Area in Lleida, Catalonia, on 31 December 2018, who would have turned between 40 to 50 years of age during the following 1.5 years. Accrual was suspended because of the COVID-19 pandemic in March 2020 when 252 women had been included and resumed in October 2020.
All women who turned 50 during the study period would have received the first invitation to participate in the population-based Breast Cancer Early Detection Program. Instead, they were invited to participate in our study. Women that declined were invited by the early detection program. From women that turned 40 to 49 years during the study period, random samples of 20 to 50 women were selected from the potential participants on a monthly basis, and the women were invited to participate until the accrual goal was achieved.
Exclusion criteria included having a previous diagnosis of breast cancer, undergoing a current breast study, or fulfilling clinical criteria for cancer-related genetic counseling. We also excluded women not understanding or speaking Catalan or Spanish or those with a physical or cognitive disability that prevented breast screening or the main outcome’s assessment.
The study intervention consisted of a baseline visit, the breast cancer risk estimation, a visit for risk communication and screening recommendations, the administration of a follow-up questionnaire, and a phone call to assess satisfaction after one year.
The baseline visit was held at the Primary Care center, where the healthcare professional provided information about the study objectives; facilitated an informative brochure about the benefits and adverse effects of breast cancer screening; obtained information on sociodemographic variables, risk factors, previous screening experience, perceived personal risk of breast cancer, and general screening knowledge, attitudes, and intentions; obtained a saliva sample to determine the genomic profile; and scheduled a screening mammogram with breast density measurement. For women that had a mammogram during the year before the first visit, breast density and presence/absence of benign lesions were obtained from that mammogram and the radiologist’s report.
Breast density was classified according to the Breast Imaging Reporting and Data System (BI-RADS), 5th edition, scoring system: almost entirely fatty (a), scattered areas of fibroglandular density (b), heterogeneously dense (c), and extremely dense (d). Mammographic findings were coded from 0 (incomplete—additional imaging needed) to 6 (known biopsy—proven malignancy). In the case of abnormal results, additional tests were requested.
Collection, conservation, and delivery of saliva samples was completed following the saliva collection protocol provided by the University of Lleida’s Proteomics and Genomics Service. Details about the genotyping process can be found in the protocol. The PRS was obtained using the 83 SNPs associated with breast cancer, based on Shieh et al.’s or Mavaddat et al.’s studies, as a composite likelihood ratio representing the individual effects of each SNP.
The primary outcome measures were attitude towards, intention to participate in, and satisfaction with personalized breast cancer screening by participating women. Attitude was measured with a three-item scale, each item ranging from 1 to 5, with higher scores indicating more positive attitudes. A “positive attitude” was defined as a total score greater than or equal to 12. Intention to participate was measured with a 5-point Likert scale from definitely will (1) to definitely will not (5). The variable was also dichotomized as intending to participate (definitely or likely) or not. Satisfaction was assessed after one year of recruitment and was measured on a 5-point Likert scale from very unsatisfied (1) to very satisfied (5). Secondary outcomes (e.g., attitude towards screening mammography, attitude towards measuring breast cancer risk, emotional impact of the measure of breast cancer risk, preference with regard to the current screening, knowledge, decisional conflict, confidence, and participation) have been detailed in full in the study protocol.
The R programming language and the RStudio environment were used for the data analysis. The Likert function of the HH package was used to obtain the graphical representation of the primary outcomes measured as Likert scales.
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Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.
Here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases. The dataset contains four components: (1) DICOM images, (2) a spreadsheet indicating which group each case belongs to (3) annotation boxes, and (4) Image paths for patients/studies/views. A detailed description of this dataset can be found in the following paper; please reference this paper if you use this dataset:
M. Buda, A. Saha, R. Walsh, S. Ghate, N. Li, A. Święcicki, J. Y. Lo, M. A. Mazurowski, Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model. (https://doi.org/10.1001/jamanetworkopen.2021.19100).
Additional information and resources related to this dataset can be found here: https://sites.duke.edu/mazurowski/resources/digital-breast-tomosynthesis-database/
A Version 1 of the dataset contains only a subset of all data described in the paper above. More data will be share in subsequent versions.
Please visit this discussion forum for any questions related to the data: https://www.reddit.com/r/DukeDBTData/
For some of the images, the laterality stored in the DICOM header and/or image orientation are incorrect. The reference standard "truth" boxes are defined with respect to the corrected image orientation in these instances. Therefore, it is crucial to provide your results for images in the correct image orientation. Python functions for loading image data from a DICOM file into 3D array of pixel values in the correct orientation and for displaying "truth" boxes (if any) are on GitHub. Please see the readme file there for instructions.
The DBTex lesion detection challenge tasked participating teams with detecting lesions in the BCS-DBT test set. The challenge had two phases: DBTex1 and DBTex2. Here we provide the BCS-DBT lesion predictions made by all participating teams for both phases, for both the BCS-DBT test and validation sets, as “team_predictions_bothphases.zip”. Please see here under “Output format for the DBTex2 Challenge test set results” for a description of how these results are formatted. Finally, when comparing lesion bounding box predictions to the image data, be sure to load the images correctly according to the above “Required Preprocessing of DBT Images”.
If you use these predictions, please reference the DBTex challenge paper:
Konz N, Buda M, Gu H, et al. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. JAMA Netw Open. 2023;6(2):e230524. doi:10.1001/jamanetworkopen.2023.0524
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Get the sample copy of Breast Cancer Screening Test Market Report 2024 (Global Edition) which includes data such as Market Size, Share, Growth, CAGR, Forecast, Revenue, list of Breast Cancer Screening Test Companies (Hologic Inc, Biocrates Life Sciences AG, OncoCyte Corporation, POC Medical Systems Inc, Siemens Healthcare Diagnostics Inc, Myriad Genetics, BioTime Inc, Provista Diagnostics Inc, Metabolomic Technologies Inc, A&G Pharmaceutical Inc), Market Segmented by Type (Paclitaxel, Vincristine, Others), by Application (Hospitals, Research institutes, Others)
Proportion of women (age 53-70) offered screening for breast cancer by borough.
Women between the ages of 50 and 70 are invited for regular breast screening (every three years) under a national programme. This is intended to detect breast cancer at an early stage.
Click here to find out how to access historical data from The Health Needs Assessment toolkit as well as how to access more recent data.
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The dataset contains x-ray images, mammography, from breast cancer screening at the Karolinska University Hospital, Stockholm, Sweden, collected by principal investigator Fredrik Strand at Karolinska Institutet. The purpose for compiling the dataset was to perform AI research to improve screening, diagnostics and prognostics of breast cancer.
The dataset is based on a selection of cases with and without a breast cancer diagnosis, taken from a more comprehensive source dataset.
1,103 cases of first-time breast cancer for women in the screening age range (40-74 years) during the included time period (November 2008 to December 2015) were included. Of these, a random selection of 873 cases have been included in the published dataset.
A random selection of 10,000 healthy controls during the same time period were included. Of these, a random selection of 7,850 cases have been included in the published dataset.
For each individual all screening mammograms, also repeated over time, were included; as well as the date of screening and the age. In addition, there are pixel-level annotations of the tumors created by a breast radiologist (small lesions such as micro-calcifications have been annotated as an area). Annotations were also drawn in mammograms prior to diagnosis; if these contain a single pixel it means no cancer was seen but the estimated location of the center of the future cancer was shown by a single pixel annotation.
In addition to images, the dataset also contains cancer data created at the Karolinska University Hospital and extracted through the Regional Cancer Center Stockholm-Gotland. This data contains information about the time of diagnosis and cancer characteristics including tumor size, histology and lymph node metastasis.
The precision of non-image data was decreased, through categorisation and jittering, to ensure that no single individual can be identified.
The following types of files are available: - CSV: The following data is included (if applicable): cancer/no cancer (meaning breast cancer during 2008 to 2015), age group at screening, days from image to diagnosis (if any), cancer histology, cancer size group, ipsilateral axillary lymph node metastasis. There is one csv file for the entire dataset, with one row per image. Any information about cancer diagnosis is repeated for all rows for an individual who was diagnosed (i.e., it is also included in rows before diagnosis). For each exam date there is the assessment by radiologist 1, radiologist 2 and the consensus decision. - DICOM: Mammograms. For each screening, four images for the standard views were acuqired: left and right, mediolateral oblique and craniocaudal. There should be four files per examination date. - PNG: Cancer annotations. For each DICOM image containing a visible tumor.
Access: The dataset is available upon request due to the size of the material. The image files in DICOM and PNG format comprises approximately 2.5 TB. Access to the CSV file including parametric data is possible via download as associated documentation.
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Breast Screening Programme, England, 2022-23
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Breast cancer and cervical cancer screenings - historical data (2000-2021)
14711 digital mammography (DM) examinations from screening, usually including two projections (mediolateral oblique and craniocaudal) of each breast. Categorized cancer positive/negative. 95 cancer cases diagnosed on DM.
Part of this dataset is directly available for inspection for partners on the AIDA platform, and the rest can be made available on request.
This dataset is an anonymized excerpt from a dataset with richer associated data, collected in a research project which is still ongoing. The authors welcome proposals for new impactful research collaborations.
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Source: Cancer Screening Database, HPA. Data by February 8, 2014. Note: 1. Follow-up rate for positive cases Number of positive cases completed follow-up / Number of positive cases. 2. Positive breast cancer cases are defined as: Breast screening results are "0, 4, 5", and screening dates are between October 1, 2012, and September 30, 2013.
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NHS Breast Screening Programme, England 2020-21
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The Europe Breast Cancer Screening Tests Market is Segmented by Test Type (Genomic Tests and Imaging Test) and Geography (Germany, France, United Kingdom, Italy, Spain, and Rest of Europe). The report offers the value (in USD million) for the above segments.
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PurposeTo evaluate mammography uptake and subsequent breast cancer diagnoses, as well as the prospect of additive cancer detection via a liquid biopsy multi-cancer early detection (MCED) screening test during a routine preventive care exam (PCE).MethodsPatients with incident breast cancer were identified from five years of longitudinal Blue Health Intelligence® (BHI®) claims data (2014-19) and their screening mammogram and PCE utilization were characterized. Ordinal logistic regression analyses were performed to identify the association of a biennial screening mammogram with stage at diagnosis. Additional screening opportunities for breast cancer during a PCE within two years before diagnosis were identified, and the method extrapolated to all cancers, including those without recommended screening modalities.ResultsClaims for biennial screening mammograms and the time from screening to diagnosis were found to be predictors of breast cancer stage at diagnosis. When compared to women who received a screening mammogram proximal to their breast cancer diagnosis (0-4 months), women who were adherent to guidelines but had a longer time window from their screening mammogram to diagnosis (4-24 months) had a 87% increased odds of a later-stage (stages III or IV) breast cancer diagnosis (p-value
Between 2021 and 2022, about 70.4 percent of women aged 50 to 69 years in Italy had a mammogram. Preventive breast cancer screening through a mammogram every two years is most recommended for those aged 50 to 69 years. Between 2021 and 2022, breast cancer screening was much more common in the Northern Italian regions, with rates often above 80 percent.
Breast cancer screening over time Before 2020, when COVID-19 hit Italy, the share of women between 50 and 69 years undergoing breast cancer screening at least once in the previous two years, had an increasing trend overall. However, the share of women who underwent preventive examinations for breast cancer after 2019 had a considerable decrease compared to the previous years. As a matter of fact, in 2019 the share of women with breast cancer screening amounted to 75.1 percent, while in 2020 it was 72.5 and in 2021 dropped to 68.8 percent.
Cervical cancer screening In Italy, women between the ages of 25 and 64 years are recommended to do a cervical cancer screening every three years. Since 2008, the percentage of women aged 25 to 65 who underwent cervical cancer screening in the previous three years, fluctuated yearly from 75.2 to 81 percent. This peak was reached in 2019 and was followed by a steep decrease in 2020, in correspondence with the spread of COVID-19. Between 2021 and 2022, when the share of women with cervical cancer screening amounted to 77.8 percent in Italy, geographical differences could be observed across the country: among Northern regions, with 84.1 percent of women underwent this screening, while in Southern regions this share amounted to only 69.2 percent.
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Source: Cancer Screening Database, HPA. Data up to February 8, 2014. Note: 1. Screening rate calculation method: the number of women aged 45-69 who have undergone breast cancer screening in the past 2 years divided by the population of women aged 45-69 in that year. 2. Since July 2004, breast cancer screening has been provided every two years for women aged 50-69, and the service was expanded to include women aged 45-69 starting November 17, 2009.
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The global breast cancer screening tests market was valued at US$ 1.9 Billion in 2022 and is expected to reach US$ 4.4 Billion by 2033. The imaging tests segment with around 54.3% value share, has topped the global market within the product category in 2022 and is expected to grow at a CAGR of close to 7.8% over the forecast period (2023 to 2033).
Data Points | Market Insights |
---|---|
Market Value 2022 |
US$ 1.9 Billion |
Market Value 2023 |
US$ 2.1 Billion |
Market Value 2033 |
US$ 4.4 Billion |
CAGR 2023 to 2033 |
7.8% |
Market Share of Top 5 Countries |
54.5% |
Key Market Players |
AstraZeneca, Novartis, Sanofi, Pfizer, Bayer AG, GlaxoSmithKline plc, and Siemens Healthineers, Hologic Inc. |
Report Scope as Per Breast Cancer Screening Test Industry Analysis
Attribute | Details |
---|---|
Forecast Period |
2023 to 2033 |
Historical Data Available for |
2017 to 2022 |
Market Analysis |
US$ Million for Value |
Key Regions Covered |
North America, Latin America, Europe, South Asia, East Asia, Oceania, Middle East and Africa (MEA) |
Key Countries Covered |
USA, Canada, Brazil, Mexico, Argentina, Germany, Italy, France, UK, Spain, BENELUX, Russia, China, Japan, South Korea, India, Indonesia, Thailand, Philippines, Malaysia, Australia, New Zealand, GCC countries, Türkiye, Northern Africa and South Africa. |
Key Market Segments Covered |
Diagnostic Test Type, End User, and Region |
Key Companies Profiled |
|
Report Coverage |
Market Forecast, Competition Intelligence, DROT Analysis, Market Dynamics and Challenges, Strategic Growth Initiatives |
Pricing |
Available upon Request |