This statistic shows the number of registrations of newly diagnosed cases of ovarian cancer in England in 2022, by age group. The most affected age group was among 75 to 79 year olds, with 908 cases reported in 2022.

This statistic shows the 10-year probability of a women developing ovarian cancer in the United States as of 2018. According to the data, a women at the age of ** has a *** percent probability of developing ovarian cancer within the next 10 years. However, a women at the age of ** has a *** percent probability of developing ovarian cancer within the next 10 years.

BackgroundThe growing burden of ovarian cancer is attracting widespread attention; the impact factors and the evolution trend of ovarian cancer burden need to be further studied.MethodsOvarian cancer disease burden data for Chinese women were obtained from the Global Burden of Disease study 2021. We performed Age-Period-Cohort (APC) analysis to evaluate evolution trends across age, period, and cohort dimensions and identify contributing factors. Using the Bayesian Age-Period-Cohort (BAPC) model, we projected incidence and mortality trends through 2040.ResultsIn 2021, China recorded approximately 41,240 new ovarian cancer cases and 25,140 related deaths. From 1990 to 2021, age-standardized rates (ASRs) for incidence, mortality, and disability-adjusted life years fluctuated but increased steadily after 2015, with annual percentage changes of 1.6% (95%CI: 1.4%, 1.8%), 1.6% (95%CI: 1.4%, 1.9%), and 1.5% (95%CI: 1.3%, 1.6%), respectively. The APC model revealed a significant age effect with peak incidence occurring at 65–69 years; a period effect showing incidence and mortality rates resurged after 2015; and the cohort effects demonstrating bimodal incidence peaks in the birth cohorts of 1910–1914 and 1935–1939. Specifically, a 1% increase in the obesity rate was associated with a 3.06 (95%CI: 0.84, 5.28; p = 0.007) per 100,000 rise in ovarian cancer incidence. BAPC projections suggest that the ASRs of incidence and mortality of ovarian cancer in China will continue rising through 2040, possibly exceeding global trends.ConclusionsThe burden of ovarian cancer in China remains significant; the increasing obesity rate in women may be a driver. The ovarian cancer burden has resurged in China since 2015, and it is projected to continue increasing by 2040.

Additional file 2: Supplementary Table 2S. Estimated variance parameters of incidence for ovarian cancer in BAPC models. The BAPC models assumes that the observed age- and period-specific new cases fit a Poisson distribution, and the mean of the age- and period-specific new cases then is regressed on the effects of age, period, and cohort, using the corresponding population as the offset to predict future incidence. The mean, standard deviation, 2.5% quantile, median, and 97.5% quantile by age categories in incidence of ovarian cancer are estimated.

In 2023, the incidence rate for ovary cancer in Canada was expected to be **** per 100,000 population among females. This statistic displays the age-standardized rate of ovary cancer cases among females in Canada between 1988 and 2020, with forecasts from 2021 to 2023.

According to the Department of Health of the government of Hong Kong, the age-standardized death rate of ovarian and peritoneal cancer in Hong Kong was around 3.4 per 100,000 female population in 2023. This indicated an increase in fatality rate compared to the previous years.

This dataset, titled "Ovarian Cancer Risk and Progression Data," contains 200,100 hourly patient records collected between January 2019 and December 2024. The data originates from a healthcare repository hosted by a leading research institute in Munich, Germany. It includes an extensive array of features spanning clinical, genetic, imaging, and demographic dimensions. The dataset represents a diverse population from Munich's urban and suburban regions, ensuring broad demographic and socioeconomic variety. Ethical protocols were strictly followed, and all personal identifiers were removed to protect patient privacy. This dataset provides invaluable resources for ovarian cancer risk prediction, cancer progression modeling, and advanced machine learning research.

Dataset Composition: The dataset encompasses the following categories of features:

Clinical Features:

Age: Patient's age at diagnosis, ranging from 18 to 90 years. BMI: Body Mass Index values (15–50), indicating health and weight status. Comorbidities: Presence of additional diseases, with 30% of patients reporting comorbid conditions. Symptoms: Binary feature indicating the presence of symptoms like abdominal pain or bloating. CA-125 Levels: A critical biomarker for ovarian cancer, ranging from 0 to 200. Cancer Stage: Classification into Stages 0 to IV, reflecting disease progression. Histopathology: Cancer subtypes (serous, mucinous, clear cell) based on tissue analysis. Previous Treatments: History of chemotherapy, surgery, or radiation. Menstrual History: Regular or irregular menstrual patterns. Demographic Features:

Ethnicity: Patient's ethnic background (Caucasian, Asian, African, Hispanic). Smoking & Alcohol: Lifestyle habits, with binary indicators. Residence: Urban or rural living environments. Socioeconomic Status: Economic categories (Low, Middle, High). Genetic Features:

BRCA Mutation: Binary indicator for BRCA1/BRCA2 mutations. Gene Expression: Normalized gene activity values. SNP Status: Presence of significant single nucleotide polymorphisms. DNA Methylation & miRNA Levels: Continuous variables capturing molecular markers. Imaging-Derived Features:

Tumor Size & Location: Dimensions and anatomical origin (Ovary, Fallopian Tube, Peritoneum). Radiomic Features: Texture, intensity, and shape metrics derived from imaging. Enhancement Patterns: Contrast enhancement in imaging. Doppler Velocity: Blood flow velocity within tumors. Reproductive and Hormonal Features:

Parity: Number of pregnancies (0–3). Oral Contraceptives & Hormone Therapy: Binary indicators for usage history. Menarche & Menopause Age: Age at the onset of menstruation and menopause. Target Variables:

Risk Label: Multi-class classification (0: No Risk, 1: Low Risk, 2: Medium Risk, 3: High Risk). Progression Probability: Continuous variable (0–1) representing the likelihood of disease progression. Dataset Utility: This dataset is curated for advancing research in ovarian cancer risk assessment and progression modeling. It is designed to support studies leveraging machine learning and deep learning techniques, providing a real-world, comprehensive feature set. Applications include multi-modal classification, risk stratification, and personalized medicine development. The high-dimensional and balanced representation ensures robust training and evaluation for predictive models. This dataset can be instrumental for researchers aiming to improve ovarian cancer diagnosis and intervention strategies.

Participants in this cohort are women identified from medical records and family records from the Department of Oncology , Lund. The inclusion criteria is that the woman should belong to families with a high risk of developing breast cancer as well as being known carriers of mutations in BRCA1 or BRCA2, or be related in the first or second generation to an individual with breast cancer or individual without breast cancer who are carriers of mutations in the genes BRCA1 or BRCA2. Study participants must be under 40 years old and still be menstruating.

“A family at high-risk for breast cancer” is considered if three women had been diagnosed with breast cancer and at least one of these was below age 50 years at diagnosis, or if two women had been diagnosed with breast cancer and at least one was below age 40 years at diagnosis, or if one woman diagnosed with breast cancer prior to age 30 years.

The collection, which is still ongoing, started in 1996 and consisted of 300 cases in 2010.

The selected women were contacted with a letter with information about the study and thereafter called and asked if they wanted to participate. A letter containing a comprehensive questionnaire was sent out to the women who had chosen to participate. The questions concerned areas such as fertility , breastfeeding, contraceptives, other medications, smoking and dietary issues. Participants were also called in for sampling and body measuremens (such as weight, length and different breast dimensions). Blood samples were taken on two occasions during the menstrual cycle : day 5-10 and ~ 18-23 days.

Purpose:

To study risk factors in young women of high risk for breast and ovarian cancer.

Data collection is ongoing. In 2010, the study consisted of 300 cases.

Ovarian Cancer Diagnostics and Therapeutics Market Outlook

The global ovarian cancer diagnostics and therapeutics market size was valued at approximately USD 2.6 billion in 2023 and is projected to reach USD 5.5 billion by 2032, registering a compound annual growth rate (CAGR) of 8.5% during the forecast period. The growth in this market is primarily driven by advancements in medical technology, increased awareness about cancer diagnosis and treatment options, and the rising prevalence of ovarian cancer worldwide. With improved diagnostic methodologies and an expanding portfolio of therapeutic options, the market is poised for significant progress over the next decade.

One of the primary growth factors for the ovarian cancer diagnostics and therapeutics market is the increasing global incidence of ovarian cancer. Ovarian cancer is one of the leading causes of cancer deaths among women, and its growing prevalence is a critical factor necessitating the development and implementation of advanced diagnostic and therapeutic solutions. The aging population, especially in developed regions, is also contributing to the rising incidence rates, as ovarian cancer risk significantly increases with age. Moreover, lifestyle changes and genetic predispositions are further exacerbating the potential for increased cases, thereby fueling market demand for effective diagnostic tools and therapeutic options.

Another significant growth factor is the technological advancements in the field of cancer diagnostics and treatment. Breakthroughs in imaging technologies, molecular diagnostics, and personalized medicine have revolutionized how ovarian cancer is detected and treated. Innovations such as next-generation sequencing and liquid biopsies are transforming the diagnostic landscape by enabling early detection and personalized treatment regimens. Additionally, the development and approval of new drugs and treatment modalities, including targeted therapy and immunotherapy, are enhancing treatment efficacy and patient outcomes, thus propelling market growth.

Government initiatives and funding for cancer research and treatment also play a crucial role in the growth of the ovarian cancer diagnostics and therapeutics market. Increased government spending on healthcare infrastructure, coupled with supportive policies to facilitate cancer research, is encouraging the development of new diagnostic and therapeutic solutions. Public awareness campaigns and educational programs about the importance of early detection and treatment of ovarian cancer are also contributing to the growth of the market by increasing patient awareness and encouraging proactive healthcare measures.

Regionally, North America holds a dominant position in the ovarian cancer diagnostics and therapeutics market, owing to the presence of advanced healthcare infrastructure, high healthcare expenditure, and a strong focus on research and development. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare investments, improving healthcare infrastructure, and a rising patient pool due to the increasing prevalence of ovarian cancer. The rapid economic growth in countries like China and India, coupled with growing awareness and availability of advanced healthcare solutions, are bolstering the market prospects in this region.

Product Type Analysis

The ovarian cancer diagnostics and therapeutics market is segmented into two primary product types: diagnostics and therapeutics. The diagnostics segment includes various methods used for early detection and confirmation of ovarian cancer, such as imaging tests, blood tests, and biopsies. Imaging tests, including ultrasound and CT scans, are essential tools in diagnosing ovarian cancer as they provide detailed images of the ovaries and surrounding areas, helping detect any abnormal growths. Blood tests, particularly those measuring cancer antigens like CA-125, are widely utilized as they offer non-invasive and cost-effective means of preliminary cancer detection. Additionally, biopsy procedures, though invasive, provide definitive diagnosis by allowing pathological examination of ovarian tissue.

The therapeutics segment encompasses several treatment modalities used to manage and treat ovarian cancer, including chemotherapy, targeted therapy, immunotherapy, and hormonal therapy. Chemotherapy remains the cornerstone of ovarian cancer treatment, often administered post-surgery to eradicate any residual cancer cells. Despite its effectiveness, chemotherapy is associated with significant side effects, prompting

Aim: We aimed to estimate the disease burden and risk factors attributable to ovarian cancer, and epidemiological trends at global, regional, and national levels.Methods: We described ovarian cancer data on incidence, mortality, and disability-adjusted life-years as well as age-standardized rates from 1990 to 2017 from the Global Health Data Exchange database. We also estimated the risk factors attributable to ovarian cancer deaths and disability-adjusted life-years. Measures were stratified by region, country, age, and socio-demographic index. The estimated annual percentage changes and age-standardized rates were calculated to evaluate temporal trends.Results: Globally, ovarian cancer incident, death cases, and disability-adjusted life-years increased by 88.01, 84.20, and 78.00%, respectively. However, all the corresponding age-standardized rates showed downward trends with an estimated annual percentage change of −0.10 (−0.03 to 0.16), −0.33 (−0.38 to −0.27), and −0.38 (−0.32 to 0.25), respectively. South and East Asia and Western Europe carried the heaviest disease burden. The highest incidence, deaths, and disability-adjusted life-years were mainly in people aged 50–69 years from 1990 to 2017. High fasting plasma glucose level was the greatest contributor in age-standardized disability-adjusted life-years rate globally as well as in all socio-demographic index quintiles and most Global Disease Burden regions. Other important factors were high body mass index and occupational exposure to asbestos.Conclusion: Our study provides valuable information on patterns and trends of disease burden and risk factors attributable to ovarian cancer across age, socio-demographic index, region, and country, which may help improve the rational allocation of health resources as well as inform health policies.

IntroductionOvarian cancer is one of the most lethal gynecological cancers. Despite diagnosis and treatment advances, survival rates have not increased over the past 32 years. This study estimated and reported the global burden of ovarian cancer during the past 32 years to inform preventative and control strategies.MethodsWe examined ovarian cancer incidence, mortality, and disability-adjusted life years (DALYs) using age-standardized rates from the Global Burden of Disease, Injuries, and Risk Factors Study 2021. high body mass index and occupational asbestos exposure were linked with death and DALYs. Data are presented as averages with 95% uncertainty intervals (UIs).ResultsIndonesia had 13 250 (8 574–21 565) ovarian cancer cases in 2021, with 5 296 (3 520–8958) deaths and 186 917 (121 866–309 820) DALYs. The burden increased by 233.53% for new cases, 221.95% for mortalities, and 206.65% for DALYs. The age-standardized rate also increased from 1990 to 2021. Ovarian cancer burden increased with age but declined in the 50+ year age group. According to the sociodemographic index, the gross domestic product per capita and number of obstetricians and oncologic gynecologists in provinces showed different trends.ConclusionsIndonesian ovarian cancer rates are rising despite gynecologic oncologists in 24 of 34 provinces. These findings will help policymakers and healthcare providers identify ovarian cancer prevention and control gaps.

Neural network analyses of circulating miRNAs have shown potential as non-invasive screening tests for ovarian cancer. A clinically useful test would detect occult disease when complete cytoreduction is most feasible. Here we used murine xenografts to sensitize a neural network model to detect low volume disease and applied the model to sera from 75 early-stage ovarian cancer cases age-matched to 200 benign adnexal masses or healthy controls. The 14-miRNA model efficiently discriminated tumor bearing animals from controls with 100% sensitivity down to tumor inoculums of 50,000 cells. Among early-stage patient samples, the model performed well with 73% sensitivity at 91% specificity. Applied to a population with 1% disease prevalence, we hypothesize the model would detect most early-stage ovarian cancers while maintaining a negative predictive value of 99.97% (95% CI 99.95%-99.98%). Overall, this supports the concept that miRNAs may be useful as screening markers for early-stage disease.

O objetivo é analisar o efeito da idade, período e coorte (APC) na mortalidade por câncer de ovário nas regiões Sul e Nordeste do Brasil. Os modelos APC foram estimados por regressão de Poisson por meio de funções estimáveis em mulheres com 30 anos ou mais residentes nos estados das regiões Sul e Nordeste. Estimados os modelos APC, verificou-se aumento nas taxas de mortalidade com o avançar da idade em todas as localidades. A região Sul apresentou redução do risco de morte nos dois últimos períodos (RR2010-2014 0,94; RR2015-2019 0,90, p<0,001) e redução do risco nas coortes de 1900 a 1929 (RR1900-04 0,55, RR1925-1929 0,89, p<0,001); perfil semelhante foi observado em todos os estados. No Nordeste, houve aumento progressivo do risco de morte nos últimos períodos, variando de 1,02 a 1,11 (2010-2014 vs. 2015-2019, p<0,001). E aumento do risco de morte nas coortes mais jovens, variando de 0,31 a 1,54 (1900-1904 vs. 1985-1989). Resultados semelhantes foram observados na maioria de seus estados isso pode estar correlacionado com os diferentes ritmos do processo de envelhecimento populacional e com as mudanças no comportamento reprodutivo das mulheres dessas duas regiões, realidade intrinsecamente ligada ao desenvolvimento socioeconômico e ao acesso aos serviços de saúde. The scope of this study was to conduct an analysis on the effect of the Age-Period-Cohort (APC) on ovarian cancer mortality in the South and Northeast regions of Brazil. The APC models were estimated by Poisson regression through estimable functions in women aged 30 and over residing in the states of the South and Northeast regions. Upon estimating the APC models, a positive gradient was found in mortality rates with advancing age in all locations The South region showed a reduction in the risk of death in the last two periods (RR2010-2014 0.94; RR2015-2019 0.90, p<0.001) and a reduction in risk in the cohorts from 1900 to 1929 (RR1900-04 0.55, RR1925-1929 0.89, p<0.001); a similar profile was observed in all states. In the Northeast, there was a progressive increase in the risk of death in the last periods, ranging from 1.02 to 1.11 (2010-2014 vs. 2015-2019, p<0.001). An increased risk of death was observed in younger cohorts, varying from 0.31 to 1.54 (cohort 1900-1904 vs. 1985-1989). Similar results were observed in most of the states.: The conclusion drawn is that heterogeneity in the APC effect on ovarian cancer mortality, which may be correlated with the different rates of the population aging process, changes in the reproductive behavior of women, and inequalities in access to health services. El objetivo es analizar el efecto de la edad, período y cohorte (EPC) sobre la mortalidad por cáncer de ovario en las regiones Sur y Nordeste de Brasil. Se estimaron modelos EPC mediante regresión de Poisson utilizando funciones estimables en mujeres de 30 años o más residentes en los estados de las regiones Sur y Nordeste. Luego de estimar los modelos EPC, se observó un aumento en las tasas de mortalidad con el avance de la edad en todas las localidades. La región Sur mostró una reducción del riesgo de muerte en los dos últimos periodos (RR2010-2014 0,94; RR2015-2019 0,90, p<0,001) y una reducción del riesgo en las cohortes de 1900 a 1929 (RR1900-04 0,55; RR1925-1929 0,89, p<0,001); Se observó un perfil similar en todos los estados. En el Nordeste, se observó un aumento progresivo del riesgo de muerte en los últimos períodos, variando de 1,02 a 1,11 (2010-2014 vs. 2015-2019, p<0,001). Y un mayor riesgo de muerte en cohortes más jóvenes, que oscila entre 0,31 y 1,54 (1900-1904 frente a 1985-1989). Resultados similares se observaron en la mayoría de sus estados, lo que puede estar correlacionado con las diferentes tasas del proceso de envejecimiento poblacional y con los cambios en el comportamiento reproductivo de las mujeres en estas dos regiones, realidad intrínsecamente ligada al desarrollo socioeconómico y al acceso a los servicios de salud. Supplementary Material Table 1. Characterization of states in the South and Northeast regions according to sociodemographic variables, health indicators and access to health services, Brazil. Supplementary Material Table S2. Analysis of the Akaike Information Criterion (AIC) in the sequential construction of age, period and cohort models for ovarian cancer fitting by estimable functions, Northeast and South, Brazil, 1980-2019. Supplementary Material Table 1. Characterization of states in the South and Northeast regions according to sociodemographic variables, health indicators and access to health services, Brazil. Supplementary Material Table S2. Analysis of the Akaike Information Criterion (AIC) in the sequential construction of age, period and cohort models for ovarian cancer fitting by estimable functions, Northeast and South, Brazil, 1980-2019. Supplementary Material Table 1. Characterization of states in the South and Northeast regions according to sociodemographic variables, health indicators and...

This statistic displays the most dangerous age for getting ovarian cancer according to women in Hong Kong as of 2017. According to a telephone interview conducted between December 2016 and January 2017, almost 40 percent of the respondents thought that women between 45 and 54 years old were most likely to get ovarian cancer.

Liposomal Doxorubicin Market size was valued at USD 1.34 Billion in 2024 and is projected to reach USD 2.40 Billion by 2032, growing at a CAGR of 7.3% during the forecast period 2026 to 2032.Global Liposomal Doxorubicin Market DriversThe market drivers for the liposomal doxorubicin market can be influenced by various factors. These may include:Increasing Cancer Incidence Rates: A substantial rise in cancer prevalence is being witnessed globally across various demographics and age groups. Higher diagnosis rates of breast cancer, ovarian cancer, and sarcomas are being recorded, creating greater demand for effective chemotherapy treatments like liposomal doxorubicin.

BackgroundVarious risk factors have been associated with epithelial ovarian cancer risk in observational epidemiological studies. However, the causal nature of the risk factors reported, and thus their suitability as effective intervention targets, is unclear given the susceptibility of conventional observational designs to residual confounding and reverse causation. Mendelian randomization (MR) uses genetic variants as proxies for risk factors to strengthen causal inference in observational studies. We used MR to evaluate the association of 12 previously reported risk factors (reproductive, anthropometric, clinical, lifestyle, and molecular factors) with risk of invasive epithelial ovarian cancer, invasive epithelial ovarian cancer histotypes, and low malignant potential tumours.Methods and findingsGenetic instruments to proxy 12 risk factors were constructed by identifying single nucleotide polymorphisms (SNPs) that were robustly (P < 5 × 10−8) and independently associated with each respective risk factor in previously reported genome-wide association studies. These risk factors included genetic liability to 3 factors (endometriosis, polycystic ovary syndrome, type 2 diabetes) scaled to reflect a 50% higher odds liability to disease. We obtained summary statistics for the association of these SNPs with risk of overall and histotype-specific invasive epithelial ovarian cancer (22,406 cases; 40,941 controls) and low malignant potential tumours (3,103 cases; 40,941 controls) from the Ovarian Cancer Association Consortium (OCAC). The OCAC dataset comprises 63 genotyping project/case–control sets with participants of European ancestry recruited from 14 countries (US, Australia, Belarus, Germany, Belgium, Denmark, Finland, Norway, Canada, Poland, UK, Spain, Netherlands, and Sweden). SNPs were combined into multi-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 95% confidence intervals (CIs). Three complementary sensitivity analyses were performed to examine violations of MR assumptions: MR–Egger regression and weighted median and mode estimators. A Bonferroni-corrected P value threshold was used to establish strong evidence (P < 0.0042) and suggestive evidence (0.0042 < P < 0.05) for associations. In MR analyses, there was strong or suggestive evidence that 2 of the 12 risk factors were associated with invasive epithelial ovarian cancer and 8 of the 12 were associated with 1 or more invasive epithelial ovarian cancer histotypes. There was strong evidence that genetic liability to endometriosis was associated with an increased risk of invasive epithelial ovarian cancer (odds ratio [OR] per 50% higher odds liability: 1.10, 95% CI 1.06–1.15; P = 6.94 × 10−7) and suggestive evidence that lifetime smoking exposure was associated with an increased risk of invasive epithelial ovarian cancer (OR per unit increase in smoking score: 1.36, 95% CI 1.04–1.78; P = 0.02). In analyses examining histotypes and low malignant potential tumours, the strongest associations found were between height and clear cell carcinoma (OR per SD increase: 1.36, 95% CI 1.15–1.61; P = 0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset: 1.09, 95% CI 1.02–1.16; P = 0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per 50% higher odds liability: 0.89, 95% CI 0.82–0.96; P = 0.002). There was little evidence for an association of genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone binding globulin with ovarian cancer or its subtypes. The primary limitations of this analysis include the modest statistical power for analyses of risk factors in relation to some less common ovarian cancer histotypes (low grade serous, mucinous, and clear cell carcinomas), the inability to directly examine the association of some ovarian cancer risk factors that did not have robust genetic variants available to serve as proxies (e.g., oral contraceptive use, hormone replacement therapy), and the assumption of linear relationships between risk factors and ovarian cancer risk.ConclusionsOur comprehensive examination of possible aetiological drivers of ovarian carcinogenesis using germline genetic variants to proxy risk factors supports a role for few of these factors in invasive epithelial ovarian cancer overall and suggests distinct aetiologies across histotypes. The identification of novel risk factors remains an important priority for the prevention of epithelial ovarian cancer.

BackgroundRisk-reducing salpingo-oophorectomy (RRSO) reduces ovarian cancer risk in BRCA1/2 mutation carriers. Premenopausal RRSO is hypothesized to increase fracture risk more than natural menopause. Elevated bone turnover markers (BTMs) might predict fracture risk. We investigated BTM levels after RRSO and aimed to identify clinical characteristics associated with elevated BTMs.MethodsOsteocalcin (OC), procollagen type I N-terminal peptide (PINP) and serum C-telopeptide of type I collagen (sCTx) were measured in 210 women ≥ 2 years after RRSO before age 53. BTM Z-scores were calculated using an existing reference cohort of age-matched women. Clinical characteristics were assessed by questionnaire.ResultsBTMs after RRSO were higher than age-matched reference values: median Z-scores OC 0.11, p = 0.003; PINP 0.84, p < 0.001; sCTx 0.53, p < 0.001 (compared to Z = 0). After excluding women with recent fractures or BTM interfering medication, Z-scores increased to 0.34, 1.14 and 0.88, respectively. Z-scores for OC and PINP were inversely correlated to age at RRSO. No correlation was found with fracture incidence or history of breast cancer.ConclusionsFive years after RRSO, BTMs were higher than age-matched reference values. Since elevated BTMs might predict higher fracture risk, prospective studies are required to evaluate the clinical implications of this finding.

Ovarian cancer incidence, mortality, and DALYs by provincial level in Indonesia, 1990–2021.

ObjectivesTo examine age-specific trends and risk factors in the burden of women’s cancers (WCs) in China from 1990 to 2019 to inform strategies.MethodsData were sourced from the Global Burden of Disease 2019 and World Population Prospects 2019. Time trends, age differences, and key factors for breast, cervical, and ovarian cancers (BC, CC, and OC) were analyzed based on age-standardized incidence rate (ASIR) and disability-adjusted life years (DALYs) rate.ResultsASIRs for BC and CC increased over the study period, with a slower growth rate for CC after 2005, likely due to targeted HPV prevention. OC showed the highest ASIR and DALY increases, indicating a growing concern. Peak ASIR for BC and CC was in women aged 50–55, while OC showed a higher burden in women aged 70–79. Lower DALYs in women born after 1985 suggest improved healthcare access.ConclusionThis study highlights significant trends in cancer burden among Chinese women, driven by age and reproductive health policies. Future efforts should enhance screening, health literacy, and age-targeted risk reduction for specific cancer types.

The pan lymphocyte marker CD45 exists in various isoforms arising from alternative splicing of the exons 4, 5 and 6. While naïve T cells express CD45RA translated from an mRNA containing exon 4, exons 4–6 are spliced out to encode the shorter CD45R0 in antigen-experienced effector/memory T cells. The SNP C77G (rs17612648) is located in exon 4 and blocks the exon’s differential splicing from the pre-mRNA, enforcing expression of CD45RA. Several studies have linked C77G to autoimmune diseases but lack of validation in other cohorts has left its role elusive. An incidental finding in an ovarian cancer patient cohort from West Norway (Bergen region, n = 312), suggested that the frequency of C77G was higher among ovarian cancer patients than in healthy Norwegians (n = 1,357) (3.0% vs. 1.8% allele frequency). However, this finding could not be validated in a larger patient cohort from South-East Norway (Oslo region, n = 1,198) with 1.2% allele frequency. Hence, C77G is not associated with ovarian cancer in the Norwegian population. However, its frequency was increased in patients with FIGO stage II, endometrioid histology or an age at diagnosis of 60 years or older indicating a possible association with a less aggressive cancer type.