Editor’s Choice

• Global Breast Cancer Market size is expected to be worth around USD 49.2 Bn by 2032 from USD 19.8 Bn in 2022, growing at a CAGR of 9.8% during the forecast period from 2022 to 2032.
• Breast cancer is the most common cancer among women worldwide. In 2020, there were about 2.3 million new cases of breast cancer diagnosed globally.
• Breast cancer is the leading cause of cancer-related deaths in women. In 2020, it was responsible for approximately 685,000 deaths worldwide.
• The survival rate of breast cancer has improved over the years. In the United States, the overall five-year survival rate of breast cancer is around 90%.
• The American Cancer Society recommends annual mammograms starting at age 40 for women at average risk.
• Although rare, breast cancer also occurs in men. Less than 1% of breast cancer cases are diagnosed in males.

(Source: WHO, American Cancer Society)

Breast cancer is a disease which affects much more women than men. In England in 2022, over 50 thousand new cases of breast cancer were registered among women. The most affected age group was women aged 65 to 69 years of age with over 6.3 thousand cases reported.

In 2024, there were 8,750 DCIS and 37,650 invasive breast cancer cases among women in the U.S. aged between 40 and 49 years. This statistic shows the number of breast cancer cases (in situ and invasive) among women in the U.S. in 2024, by age.

Breast cancer remains a significant health concern for women in the United States, with the risk increasing as women age. For women aged 30, the probability of developing invasive breast cancer in the next ten years is *** percent. However, this risk rises substantially to *** percent for women in their ***. The lifetime risk of developing invasive breast cancer for American women stands at **** percent, highlighting the importance of regular screenings and early detection. Prevalence and impact As of January 2022, approximately **** million women in the U.S. had been diagnosed with breast cancer and survived. While breast cancer is the most common type of cancer among women in the country, lung and bronchus cancer accounts for the highest number of cancer-related deaths. Despite this, breast cancer remains a leading cause of concern, with an estimated ******* new cases among women in 2025. The impact of breast cancer extends beyond those diagnosed, as a 2022 survey found that about a quarter of women reported they or a family member had a history of the disease. Trends and developments Encouragingly, breast cancer mortality rates have decreased over recent decades. In 2022, the death rate due to breast cancer was **** per 100,000 population, a significant improvement from **** per 100,000 in 1990. This decline is attributed to factors such as early detection, improved therapies, and increased awareness of risk factors. However, breast cancer remains the second most deadly form of cancer among women in the United States. In 2025, there were estimated to be around 42,170 deaths due to breast cancer among women in the United States.

IntroductionLung and breast cancer are important in the working-age population both in terms of incidence and costs. The study aims were to estimate the 10-year risk of lung and breast cancer by occupation and smoking status and to create easy to use age-, and sex-specific 10-year risk charts.MethodsNew lung and breast cancer cases between 2010 and 2014 from all 5 cancer registries of Western Switzerland, matched with the Swiss National Cohort were used. The 10-year risks of lung and breast cancer by occupational category were estimated. For lung cancer, estimates were additionally stratified by smoking status using data on smoking prevalence from the 2007 Swiss Health Survey.ResultsThe risks of lung and breast cancer increased with age and were the highest for current smokers. Men in elementary professions had a higher 10-year risk of developing lung cancer compared to men in intermediate and managerial professions. Women in intermediate professions had a higher 10-year risk of developing lung cancer compared to elementary and managerial professions. However, women in managerial professions had the highest risk of developing breast cancer.DiscussionThe 10-year risk of lung and breast cancer differs substantially between occupational categories. Smoking creates greater changes in 10-year risk than occupation for both sexes. The 10-year risk is interesting for both patients and professionals to inform choices related to cancer risk, such as screening and health behaviors. The risk charts can also be used as public health indicators and to inform policies to protect workers.

The rate of breast cancer deaths in the U.S. has dramatically declined since 1950. As of 2023, the death rate from breast cancer was **** per 100,000 population. However, cancer is a serious public health issue in the United States and is the second leading cause of death among women. Breast cancer incidence Breast cancer symptoms include lumps or thickening of the breast tissue and may include changes to the skin. Breast cancer is driven by many factors, but age is a known risk factor. Among all age groups, the highest number of invasive breast cancer cases were among those aged 60 to 69. The incidence rate of new breast cancer cases is higher in some ethnicities than others. White, non-Hispanic women have the highest incidence rate of breast cancer, followed by non-Hispanic Black women. Breast cancer treatment Breast cancer treatments usually involve several methods, including surgery, chemotherapy and biological therapy. Types of cancer diagnosed at earlier stages often require fewer treatments. A majority of early stage breast cancer cases in the U.S. receive breast conserving surgery and radiation therapy.

Supporting information on methods used and results obtained, containing Tables S1 to S15 and Figures S1 to S7. Table S1, Distribution of stages at diagnosis of BC. Table S2, Relative risk of breast cancer based on age and breast density. Table S3, Prevalences of risk factors by age group for each category of breast density. Table S4, Characteristics of the 2,625 screening strategies analized. Table S5, The utilities for the general population and for women diagnosed with BC, either DCIS or invasive. Table S6, Model for false positives of non-invasive tests. Table S7, Model for false positives of invasive tests. Table S8, Distribution of stages at diagnosis of BC for screen-detected cases. Different overdiagnosis rates. Table S9, Linear regression model with dependent variable being the DCIS rate per women-year. Table S10, Cost-effectiveness and harm-benefit analysis. Lives extended. Table S11, Cost-effectiveness and harm-benefit analysis. Quality-adjusted life years (QALY). Table S12, Number of mammograms and detection rates for screen-detected and interval cases and program sensitivity by age groups. Invasive cancer (DCIS not included). Table S13, Distribution of stages at diagnosis of BC. Table S14, Sensitivity analysis. Changes in lives extended. Table S15, Sensitivity analysis. Changes in QALY. Figure S1, Incidence curves for twelve risk profiles grouped by risk level: (A) Low Risk, (B) Medium-Low Risk, (C) Medium-High Risk, and (D) High Risk. Graphic (E) shows the smoothed incidence rates for each risk group. Figure S2, Observed and smoothed DCIS rates over time in Catalonia (1983–2008). Figure S3, Index of mammography use (IMU) and smoothed DCIS rates over time in Catalonia (1983–2008). Figure S4, Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies, with uniform strategies marked. Effect measured in lives extended. Figure S5, Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies, with uniform strategies marked. Effect measured in QALY. Figure S6, Sensitivity analysis of a change in the risk groups distribution. Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies. Effect measured in lives extended. Figure S7, Sensitivity analysis of a change in the risk groups distribution. Cost-effectiveness and harm-benefit analyses for 2,625 early detection strategies. Effect measured in QALY. (PDF)

Introduction

Breast Cancer Statistics: Breast cancer stands as one of the most widespread and significant health challenges affecting women across the globe. As the most commonly diagnosed cancer in women, it poses a critical public health issue with far-reaching clinical and societal consequences. Each year, millions of women and a notable number of men receive a breast cancer diagnosis, confronting not only medical treatment but also emotional and financial hardships.

The global incidence of breast cancer continues to rise, fueled by factors such as an aging population, lifestyle-related risks, and improved awareness and diagnostic capabilities. Despite progress in early detection, advanced therapies, and survivorship programs, stark disparities persist between high- and low-income countries, largely due to unequal access to quality care and resources.

This section comprehensively analyses breast cancer statistics, covering incidence, mortality, survival rates, geographical variations, and economic burden. A clear understanding of these trends is vital for guiding policy decisions, resource allocation, and advancing breast cancer research and care delivery efforts.

Breast cancer incidence rates among women in the United States vary by race and ethnicity. Non-Hispanic white women face the highest risk, with ***** cases per 100,000 population from 2017 to 2021. In comparison, the incidence rate for breast cancer among Hispanic women during this period was *** per 100,000 population. This stark contrast in incidence rates highlights the importance of understanding racial and ethnic disparities in breast cancer diagnosis and treatment. Demographic factors influence breast cancer risk While non-Hispanic white women have the highest incidence rate, other racial and ethnic groups also face substantial risks. Non-Hispanic Black women have the second-highest rate at ***** cases per 100,000, followed by American Indian/Alaska Native women at *****. These variations underscore the need for targeted prevention and screening efforts. Interestingly, breast cancer incidence rates also differ by state, with Connecticut reporting the highest rate with *** cases per 100,000 population in 2021. Molecular subtypes and age impact breast cancer incidence The distribution of breast cancer subtypes varies among racial and ethnic groups, potentially contributing to differences in incidence rates. For white women, the hormone receptor positive/human epidermal growth factor receptor 2 negative (HR+/HER2-) subtype accounts for ** percent of cases, which is generally less aggressive and slower growing. Age also plays a significant role in breast cancer risk, with women aged 60 to 69 accounting for ****** ductal carcinoma in situ (DCIS) cases and ****** invasive breast cancer cases in 2024. These factors emphasize the complexity of breast cancer epidemiology and the need for comprehensive research and prevention strategies.

This study explored the barriers and facilitators to the implementation of a risk-based breast cancer screening program from the point of view of Spanish health professionals. A cross-sectional study with 220 Spanish health professionals was designed. Data were collected in 2020 via a web-based survey and included advantages and disadvantages of risk-based screening and barriers and facilitators for the implementation of the program. Descriptive statistics and Likert scale responses analyzed as category-ordered data were obtained. Risk-based screening was considered important or very important to reduce breast cancer mortality and promote a more proactive role for women in breast cancer prevention, to increase coverage for women under 50 years, to promote a breast cancer prevention strategy for women at high risk and to increase efficiency and effectiveness. Switching to a risk-based program from an age-based program was rated as important or very important by 85% of participants. As barriers for implementation, risk communication, workload of health professionals and limited human and financial resources were mentioned. Despite the barriers, there is good acceptance and it seems feasible, from the perspective of health professionals, to implement a risk-based breast cancer screening program in Spain. However, this poses a number of organizational and resource challenges. Methods We conducted an exploratory cross-sectional study with health professionals whose work was or was not directly related to breast cancer screening. Our research team contacted and presented the study to the board of directors of several Catalan and Spanish health related societies and scientific groups (public health, family and community medicine, and breast specialists). We asked them to invite the society or group members to participate in the study which consisted of responding to a questionnaire with an estimated completion time of 20 minutes. The study information and the link to the questionnaire were posted in the scientific societies' web pages or newsletters. For data protection reasons, we did not have information on the number of potential participants, or their demographic or job characteristics Therefore, a self-selection sampling method where individuals choose to take part in research on their own accord, was used. Data were collected between July and November of 2020, using a web-based survey. The questionnaire was built on the Typeform platform (https://www.typeform.com/) in the Spanish and Catalan languages. A pilot test with a convenience sample of 20 participants was conducted and some changes were made based on their suggestions. A survey sample size of 210 professionals was chosen as appropriate so that 95% confidence intervals of the true proportion responding positively would be approximately 7% either side of the observed proportion. We closed the data collection when 220 health professionals had completed the survey.

Sociodemographic data: age, gender, professional field (nurse, doctor, other), medical specialty or professional profile, years of practice, type of work center (public, private, both, university, other), type of relationship or employment contract, and work relation with early detection of breast cancer (yes/no); Advantages of risk-based screening for the health of women with an individual risk of breast cancer higher (6 items) /lower (6 items) than the population average; Disadvantages of risk-based screening for women’s health (6 items); Advantages of risk-based screening, in relation to current screening, for the Spanish National Health System (4 items); Barriers (15 items) and facilitators (6 items) for the implementation of risk-based screening; Implementation of shared decision-making in breast cancer screening (12 items); Aspects of the organizational structure to consider for the implementation of a risk-based screening program (9 items); Communication of the benefits and harms of breast cancer screening (7 items); Coordination of the risk-based screening program (3 items);

Except sociodemographic data, all items were scored on 5-point Likert scales. For the first six sections of the questionnaire, related to the risk-based program, the importance given to the statements was assessed as: 1-unimportant, 2-slightly important, 3-moderately important, 4-important, and 5-very important. For the last two sections -communication of the benefits and harms and coordination of the risk-based screening program-, the level of agreement given to the statements was assessed as: 1-strongly disagree, 2-disagree, 3-undecided, 4-agree, 5-strongly agree. In addition, the survey included these two questions:

Considering the advantages and disadvantages, how important is it for you to move from the current Screening Program to a personalized Breast Cancer Screening Program? Answer: 1 to 5 Likert scale, where 1-very little or nothing and 5-a lot; Given the current Breast Cancer Screening Program, do you think Primary Care should be the gateway to a future personalized breast cancer screening program? Answer: Yes/No.

According to Cognitive Market Research, the Global Breast Cancer Diagnostics Market Size was USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

• Based on type, In 2022, the imaging section held a 52.74% revenue share, making it the dominant segment overall. The market is divided into categories based on kind, including imaging, biopsy, genetic testing, blood testing, and others.

• The market is divided into four segments based on application: screening, diagnostic & predictive, prognostic, and research. In 2022, the diagnostic and predictive sector held a 48.4% revenue share, dominating the overall market.

• Based on the End-users, the market is divided into hospitals and clinics, diagnostic centers and medical laboratories, and other segments based on the end-use. With a revenue share of 50.6% in 2022, the hospitals & clinics sector led the overall market, largely because of the rising number of hospitalizations and rising incidence of breast cancer

• On the basis of Product, The market is divided into platform-based products and instruments-based products based on the kind of product. In 2022, the instrument-based products sector held a 71.8% revenue share, making it the dominant segment in the total market.

• The North America region accounted for the highest market share in the Global Breast Cancer Diagnostics Market. CURRENT SCENARIO OF THE BREAST CANCER DIAGNOSTICS MARKET

Key factors driving the Breast Cancer diagnostics Market

The rising prevalence of breast cancer coupled with the rise in the emergence of new technologies in early screening and diagnosis is expected to drive the global breast cancer diagnostic market growth

The incidence of breast cancer is proliferating. As per the report presented by the World Health Organization (WHO), around 2.3 million women were affected due to breast cancer across the world in the year 2020 with 6,85,000 fatalities.

In 2023, an estimated 297,790 new cases of invasive breast cancer are expected to be diagnosed in U.S. women, along with 55,720 new cases of DCIS. (Source: https://www.breastcancer.org/facts-statistics)

Globally, breast cancer has been identified by the WHO as the most common type of cancer, with new cases surpassing 2.3 million in 2021. Hence, the increasing prevalence of breast cancer cases is booming the global breast cancer diagnostic market over the forecast period.

Furthermore, technological advancements in imaging are creating improvements and opportunities in early detection and screening of the disease. One of the advanced technologies is 3-D mammography which is also known as tomosynthesis. This test takes images from various angles of the breast and develops them into a 3D shape.

For instance, on July 10th, 2019, Arizona-based SimonMed Imaging announced its implementation of the first U.S. Food and Drug Administration (FDA)-cleared artificial intelligence (AI) program for significantly enhancing early breast cancer detection for 3-D mammography. The AI program, called ProFound AI for digital breast tomosynthesis (DBT), was developed by iCAD Inc. (Source:https://www.itnonline.com/content/simonmed-imaging-implements-profound-ai-3-d-tomosynthesis)

Growing Aging Population to drive the market 

Aging is one of the greatest risk factors for breast cancer. With age, the immune system is affected. This increases susceptibility to various diseases.

According to Cancer Treatment Centers of America, women above 60 years of age are more likely to be diagnosed and only about 10% to 15% of cases occur in women younger than 45 years of age. (Source:https://www.cancer.org/cancer/types/breast-cancer/about/how-common-is-breast-cancer.html)

Breast cancer diagnosis is relatively rare in younger women; only about one out of eight invasive breast cancers are diagnosed in women younger than 45, whereas about two out of three invasive breast cancers are found in women 55 or older. (Source: https://www.breastcancer.org/facts-statistics)

Therefore, improved healthcare services are needed for the elderly population, particularly for the treatment and management of chronic illnesses like breast cancer.

Breast cancer is the most frequent cancer among women, and in developing nations, where the majority of cases are detected at l...

Age-period-cohort (APC) models are widely used to analyze population-level rates, particularly cancer incidence and mortality. These models are used for descriptive epidemiology, comparative risk analysis, and extrapolating future disease burden. Traditional APC models have two major limitations: 1) they lack parsimony because they require estimation of deviations from linear trends for each level of age, period, and cohort; and 2) rates observed at similar ages, periods, and cohorts are treated as independent, ignoring any correlations between them that may lead to biased parameter estimates and inefficient standard errors. We propose a novel approach to estimation of APC models using a spatially-correlated Poisson model that accounts for over-dispersion and correlations in age, period, and cohort, simultaneously. We treat the outcome of interest as event rates occurring over a grid defined by values of age, period, and cohort. Rates defined in this manner lend themselves to well-established approaches from spatial statistics in which correlation among proximate observations may be modeled using a spatial random effect. Through simulations, we show that in the presence of spatial dependence and over-dispersion: 1) the correlated Poisson model attains lower AIC; 2) the traditional APC model produces biased trend parameter estimates; and 3) the correlated Poisson model corrects most of this bias. We illustrate our approach using brain and breast cancer incidence rates from the Surveillance Epidemiology and End Results Program of the United States. Our approach can be easily extended to accommodate comparative risk analyses and interpolation of cells in the Lexis with sparse data.

This study aims to evaluate the feasibility of applying a method of estimating the incidence of cancer to regions of the state of São Paulo, Brazil, from real data (not estimated) and retrospectively comparing the results obtained with the official estimates. A method based on mortality and on the incidence to mortality (I/M) ration was used according to sex, age, and tumor location. In the I/M numerator, new cases of cancer were used from the population records of Jaú and São Paulo from 2006-2010; in the denominator, deaths from 2006-2010 in the respective areas, extracted from the national mortality system. The estimates resulted from the multiplication of I/M by the number of cancer deaths in 2010 for each region. Population data from the 2010 Demographic Census were used to estimate incidence rates. For the adjustment by age, the world standard population was used. We calculated the relative differences between the gross incidence rates estimated in this study and the official ones. Age-adjusted cancer incidence rates were 260.9/100,000 for men and 216.6/100,000 for women. Prostate cancer was the most common in males, whereas breast cancer was most common in females. Differences between the rates of this study and the official rates were 3.3% and 1.5% for each sex. The estimated incidence was compatible with the officially presented state profile, indicating that the application of real data did not alter the morbidity profile, while it did indicate different risk magnitudes. Despite the over-representativeness of the cancer registry with greater population coverage, the selected method proved feasible to point out different patterns within the state.

Studying the effects of groups of single nucleotide polymorphisms (SNPs), as in a gene, genetic pathway, or network, can provide novel insight into complex diseases such as breast cancer, uncovering new genetic associations and augmenting the information that can be gleaned from studying SNPs individually. Common challenges in set-based genetic association testing include weak effect sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with individual SNP effects often ranging from extremely sparse to moderately sparse in number. Motivated by these challenges, we propose the Generalized Berk–Jones (GBJ) test for the association between a SNP-set and outcome. The GBJ extends the Berk–Jones statistic by accounting for correlation among SNPs, and it provides advantages over the Generalized Higher Criticism test when signals in a SNP-set are moderately sparse. We also provide an analytic p-value calculation for SNP-sets of any finite size, and we develop an omnibus statistic that is robust to the degree of signal sparsity. An additional advantage of our work is the ability to conduct inference using individual SNP summary statistics from a genome-wide association study (GWAS). We evaluate the finite sample performance of the GBJ through simulation and apply the method to identify breast cancer risk genes in a GWAS conducted by the Cancer Genetic Markers of Susceptibility Consortium. Our results suggest evidence of association between FGFR2 and breast cancer and also identify other potential susceptibility genes, complementing conventional SNP-level analysis. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer–Cuzick, are widely utilized, machine learning-based approaches may offer enhanced predictive performance. This systematic review and meta-analysis compare the accuracy of traditional statistical models and machine learning models in breast cancer risk prediction. A total of 144 studies from 27 countries were systematically reviewed, incorporating genetic, clinical, and imaging data. Pooled C-statistics were calculated to assess model discrimination, while observed-to-expected (O/E) ratios were used to evaluate calibration. Subgroup and sensitivity analyses were conducted to examine heterogeneity and assess the influence of study bias across various populations. Machine learning-based models demonstrated superior performance, with a pooled C-statistic of 0.74, compared to 0.67 for traditional models. Models that integrated genetic and imaging data showed the highest levels of accuracy, although performance varied by population. Sensitivity analyses excluding high-bias studies showed improved discrimination in models incorporating genetic factors, with the pooled C-statistic increasing to 0.72. Traditional models, such as Gail, exhibited notably poor predictive accuracy in non-Western populations, as evidenced by a C-statistic of 0.543 in Chinese cohorts. Machine learning models provide significantly greater predictive accuracy for breast cancer risk, particularly when incorporating multidimensional data. However, issues related to model generalizability and interpretability remain, particularly in diverse populations. Future research should focus on developing more interpretable models and expanding global validation efforts to improve model applicability across different demographic groups.

BackgroundEarlier studies of breast cancer, screening mammography, and mortality reduction may have inflated lifetime and long-term risk estimates for invasive breast cancer due to limitations in their data collection methods and interpretation.ObjectiveTo estimate the percentage of asymptomatic peri/postmenopausal women who will be diagnosed with a first invasive breast cancer over their next 25 years of life.MethodsA systematic review identified peer-reviewed published studies that: 1) enrolled no study participants with a history of invasive breast cancer; 2) specified the number of women enrolled; 3) reported the number of women diagnosed with a first invasive breast cancer; 4) did not overcount [count a woman multiple times]; and, 5) defined the length of follow-up. Data sources included PubMed, Cochrane Library, and an annotated library of 4,409 full-text menopause-related papers collected and reviewed by the first author from 1974 through 2008. Linear regression predicted incidence of first invasive breast cancer, based on follow-up duration in all studies that met the our inclusion criteria, and in a subset of these studies that included only women who were 1) at least 50 years old and 2) either at least 50 or less than 50 but surgically menopausal at enrollment.ResultsNineteen studies met the inclusion criteria. They included a total of 2,305,427 peri/postmenopasual women. The mean cumulative incidence rate of first invasive breast cancer increased by 0.20% for each year of age (95% CI: 0.17, 0.23; p < 0.01; R2 = 0.90). Over 25 years of follow-up, an estimated 94.55% of women will remain breast cancer-free (95% CI: 93.97, 95.13). In the 12 studies (n = 1,711,178) that enrolled only postmenopausal women, an estimated 0.23% of women will be diagnosed with a first invasive breast cancer each year (95% CI: 0.18, 0.28; p < 0.01, R2 = 0.88).ConclusionThe vast majority (99.75%) of screened asymptomatic peri/postmenopasual women will not be diagnosed with invasive breast cancer each year. Approximately 95% will not be diagnosed with invasive breast cancer during 25 years of follow-up. Women who receive clinical examinations, but do not have mammograms, will have higher cancer-free rates because innocuous positives (comprising 30-50% of mammography diagnoses) will remain undetected. Informed consent to asymptomatic women should include these results and consideration of the benefits of avoiding mammograms.

In 2022, there were an estimated 2.48 million new cases of trachea, bronchus, and lung cancer worldwide. Breast cancer was the second most common cancer type at that time with around 2.3 million new cases worldwide.

Number of new cancer cases

Cancer can be caused by internal factors like genetics and mutations, as well as external factors such as smoking and radiation. It occurs in the presence of uncontrolled growth and spread of abnormal cells. However, many cancer cases could be prevented, for example, by omitting cigarette usage and heavy alcohol consumption. Risk of developing cancer tends to increase with age and is most common in older adults. Nevertheless, cancer can develop in individuals of any age. Cancer can be treated through surgery, radiation, and chemotherapy, among other methods.

In the United States, there will be an estimated two million new cancer cases and 611,720 deaths in 2024. Among U.S. men, prostate cancer and lung and bronchus cancers are the most common cancer types as of 2024, totaling an estimated 299,010 and 116,310 cases, respectively. In women, breast cancer and lung and bronchus cancer are the most common newly diagnosed types, totaling 310,720 and 118,270 cases, respectively.

BackgroundSurgical therapy of breast cancer and bone metastasis can effectively improve the prognosis of breast cancer. However, after the first operation, the relationship between preoperative indicators and outcomes in patients who underwent metastatic bone surgery remained to be studied. Purpose 1. Recognize clinical and laboratory prognosis factors available to clinical doctors before the operation for bone metastatic breast cancer patients. 2. Develop a risk prediction model for 3-year postoperative survival in patients with breast cancer bone metastasis.MethodsFrom 2014 to 2020, patients who suffered from breast cancer bone metastasis and received therapeutic procedures in our institution were included for analyses (n=145). For patients who underwent both breast cancer radical surgery and bone metastasis surgery, comprehensive datasets of the parameters of interest (clinical features, laboratory factors, and patient prognoses) were collected (n=69). We performed Multivariate Cox regression to identify factors that were associated with postoperative outcome. 3-year survival prediction model and nomograms were established by 100 bootstrapping. Its benefit was evaluated by calibration plot, C-index, and decision curve analysis. The Surveillance, Epidemiology, and End Results database was also used for external validation.ResultsRadiotherapy for primary cancer, pathological type of metastatic breast cancer, lymph node metastasis, elevated serum alkaline phosphatase, lactate dehydrogenase were associated with postoperative prognosis. Pathological types of metastatic breast cancer, multiple bone metastasis, organ metastases, and elevated serum lactate dehydrogenase were associated with 3-year survival. Then those significant variables and serum alkaline phosphatase counts were integrated to construct nomograms for 3-year survival. The C-statistic of the established predictive model was 0.83. The calibration plot presents a graphical representation of calibration. In the decision curve analysis, the benefits are higher than those of the extreme curve. The receiver operating characteristic of the external validation of the model was 0.82, indicating a favored fitting degree of the two models.ConclusionOur study suggests that several clinical features and serological markers can predict the overall survival among the patients who are about to receive bone metastasis surgery after breast cancer surgery. The model can guide the preoperative evaluation and clinical decision-making for patients. Level of evidence Level III, prognostic study.

Data set linked to the paper, "Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations". Pre-print of the paper is here: https://doi.org/10.1101/2020.06.16.146803.

cross_cancer_sum_stats.txt.gz contains summary genome-wide association statistics for susceptibility to single cancers (breast (BR), prostate (PR), ovarian (OV), endometrial (EN), estrogen receptor (ER)-positive breast (POS), ER-negative breast (NEG), and high-grade serous ovarian (HGS) cancers) and from the cross-cancer meta-analysis (main [main] and subtype-focused [sub]). EA in the header refers to the effect allele, OA is the other allele, EAF is the effect allele frequency in the largest of the single cancer data sets (BR), IMPR2 is the imputation quality in the largest of the single cancer data sets (BR), SE is the standard error, PVAL is the P-value, RE2Cs1 is the RE2C statistic mean effect part, RE2Cs2 is the RE2C statistic heterogeneity part, RE2Cp* is the RE2C* P-value. More on RE2Cp* can be found here: http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite and in https://academic.oup.com/bioinformatics/article/33/14/i379/3953957 SNP names in cross_cancer_sum_stats.txt.gz include the chromosome and build 37 position.

main_tetrachoric_corr_matrix.txt and subtype_tetrachoric_corr_matrix.txt provide the tetrachoric correlation matrices used in the main and subtype-focused meta-analyses. These were also used to specify the cryptic.cor argument of the exh.abf function of MetABF. More on MetABF can be found here: https://github.com/trochet/metabf and in https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202

prior_sigmas_for_metabf.txt contains the values used to specify the prior.sigma argument of the exh.abf function in MetABF.

The breast cancer data used are described in PMID 29059683 and can be downloaded from http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/ (this link also includes acknowledgements). The prostate cancer data are described in PMID 29892016 and can be downloaded from: http://practical.icr.ac.uk/blog/?page_id=8164 (this link also includes acknowledgements). The ovarian cancer data used are described in PMID 28346442 and can be downloaded from https://www.ebi.ac.uk/gwas/studies/GCST004415. The endometrial cancer data are described in PMID 30093612 and can be downloaded from https://www.ebi.ac.uk/gwas/studies/GCST006464. These links point to the same data that form the basis of the cross_cancer_sum_stats.txt.gz file.

The sample size and precision of the data presented should preclude identification of any individual study participant. However, in downloading these data, you undertake not to attempt to identify individual study participant and not to re-post these data to a third-party website. Please cite the PMIDs highlighted above along with the appropriate acknowledements if you use the cross_cancer_sum_stats.txt.gz file.

If you have any questions about this repository, please email Siddhartha Kar at siddhartha dot kar at bristol dot ac dot uk

In 2024, the number of ****** cancer cases among women in Japan reached approximately ******, which made it the most common type of cancer for women. The estimated total number of cancer cases for Japanese women in that year amounted to almost 421,000. Most common types of cancer in Japan Following breast cancer, colon and rectum, lung, as well as stomach were the most frequently diagnosed cancer sites among women in Japan. In contrast, prostate cancer was the most frequently diagnosed cancer among men, followed by stomach, colon and rectum, and lung cancer.Different types of cancer rank among the most common causes of death among Japanese people. In terms of cancer-related mortality among women in Japan, lung cancer claimed the highest number of lives in recent years, followed by pancreatic cancer.  Prevention and treatment of breast cancer In recent years, colon, cervix, lung, breast, and stomach were the most common cancer sites for screening in Japan. This was supported by a survey from 2023, in which over 36 percent of Japanese women stated that they had a cancer screening in the past two years.More attention has been given to breast cancer treatment as the incidence of breast cancer in Japan has grown throughout the past decades. Consequently, the number of general hospitals equipped with breast surgery departments increased as well.The early detection of breast cancer is crucial to increase the chance of survival. The primary approach to breast cancer treatment involves surgical removal of the cancer, though preoperative drug therapy may be administered based on the cancer's condition.