In 2021, more than ****** new cases of ovarian cancer were recorded in China. The prevalence of the disease is expected to grow even further, exceeding ****** cases in 2026. Ovarian cancer is one of the most common cancers among women. In its early stages, there may be few symptoms, making early diagnosis relatively difficult.

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.

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 rate of registrations of newly diagnosed cases of ovarian cancer per 100,000 population in England in 2020, by region. With a rate of 22.4 newly diagnosed females with ovarian cancer per 100,000 population in 2020, the regions most affected by ovarian cancer was North West.

*Epithelial ovarian cancer ** Grade cannot be assessed.

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.

BackgroundOvarian cancer contributes significantly to the morbidity and mortality rates for women worldwide. As observed with other types of cancer, health disparities disproportionately affect ovarian cancer incidence rates and outcomes, especially in African American and older women. However, the trends in ovarian cancer mortality rates up until 2023 with regard to various demographic identifiers have not been fully elucidated, which this study aims to rectify.MethodsMortality trends due to malignant neoplasms of the ovary in individuals 25 and older in the US from 1999 to 2023 were analyzed using the Centers for Disease Control Wide Ranging Online Data for Epidemiological Research (CDC WONDER) database. Trends in age-adjusted mortality rate (AAMR) were analyzed on the basis of race, 10-year age-group, region and urban/rural designation.ResultsBetween 1999 and 2023, the AAMR related to malignant neoplasms of the ovary fell from 14.62 in 1999 to 9.52 in 2023. All races analyzed saw a decrease in overall mortality related to malignant neoplasms of the ovary, with the largest decrease being observed in White patients (AAPC: -1.78). Regionally, the Northeast (AAPC: -1.95), Midwest (AAPC: -1.99), South (AAPC: -1.72), and West (AAPC: -1.73) regions of the United States (US) all saw reduced ovarian neoplasm mortality rates. Similarly, rates also decreased in urban (AAPC: -1.83) and rural (AAPC: -1.75) localities, as well as in each ten-year age category analyzed, with the largest decrease seen in the 55–64 years old category (AAPC: -2.15). States such as Delaware, South Carolina, and Idaho experienced some of the largest decreases in AAMR, whereas the District of Columbia saw an increase in AAMR during this period.ConclusionsOver the last twenty-years, mortality rates for malignant neoplasms of the ovary have declined, with the largest decreases being seen in White patients, those residing in the Midwest, urban locality, and women between 55–64 years olds. While mortality rates have declined, health disparities still continue to negatively affect ovarian cancer outcomes, and more research is needed to improve accessibility, availability, and affordability of care for patients.

Ovarian 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. We 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). Indonesia 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. Indonesian 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.

• Wang et al. Weakly Supervised Deep Learning for Prediction of Treatment Effectiveness on Ovarian Cancer from Histopathology Images. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2022.102093
• Wang, CW., Chang, CC., Khalil, M.A. et al. Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer. Sci Data 9, 25 (2022). https://doi.org/10.1038/s41597-022-01127-6

This statistic shows the incidence rate for ovarian cancer among U.S. women from 2010 to 2014, by ethnicity. According to the data, non-Hispanic, white women have an incidence rate of ovarian cancer of 12 women per every 100,000 female population.

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.

endometrioid adenocarcinoma N = 11, Clear cell neoplasms N = 6 and carcinosarcoma N = 4.*No significant difference for the subset of 127 matched patients with benign conditions: Median age: 64 (range 54–90).

This statistic shows the five-year survival rate for ovarian cancer cases among U.S. women from 2008 to 2014, by stage at diagnosis. According to the data, 92 percent of women that are diagnosed with a localized stage of ovarian cancer will survive five years after their diagnosis.

Additional File 2.

BackgroundAlthough screening is widely used to reduce cancer burden, untargeted cancers are frequently missed after single cancer screening. Joint cancer screening is presumed as a more effective strategy to reduce overall cancer burden.MethodsGender-specific screening effects on PLCO cancer incidence, PLCO cancer mortality, all-neoplasms mortality and all-cause mortality were evaluated, and meta-analyses based on gender-specific screening effects were conducted to achieve the pooled effects. The cut-off value of time-dependent receiver-operating-characteristic curve of 10-year combined PLCO cancer risk was used to reclassify participants into low- and high-risk subgroups. Further analyses were conducted to investigate screening effects stratified by risk groups and screening compliance.ResultsAfter a median follow-up of 10.48 years for incidence and 16.85 years for mortality, a total of 5,506 PLCO cancer cases, 1,845 PLCO cancer deaths, 3,970 all-neoplasms deaths, and 14,221 all-cause deaths were documented in the screening arm, while 6,261, 2,417, 5,091, and 18,516 outcome-specific events in the control arm. Joint cancer screening did not significantly reduce PLCO cancer incidence, but significantly reduced male-specific PLCO cancer mortality (hazard ratio and 95% confidence intervals [HR(95%CIs)]: 0.88(0.82, 0.95)) and pooled mortality [0.89(0.84, 0.95)]. More importantly, joint cancer screening significantly reduced both gender-specific all-neoplasm mortality [0.91(0.86, 0.96) for males, 0.91(0.85, 0.98) for females, and 0.91(0.87, 0.95) for meta-analyses] and all-cause mortality [0.90(0.88, 0.93) for male, 0.88(0.85, 0.92) for female, and 0.89(0.87, 0.91) for meta-analyses]. Further analyses showed decreased risks of all-neoplasm mortality was observed with good compliance [0.72(0.67, 0.77) for male and 0.72(0.65, 0.80) for female] and increased risks with poor compliance [1.61(1.40, 1.85) for male and 1.30(1.13, 1.40) for female].ConclusionJoint cancer screening could be recommended as a potentially strategy to reduce the overall cancer burden. More compliance, more benefits. However, organizing a joint cancer screening not only requires more ingenious design, but also needs more attentions to the potential harms.Trial registrationNCT00002540 (Prostate), NCT01696968 (Lung), NCT01696981 (Colorectal), NCT01696994 (Ovarian).

Ovarian Cancer Multimarker Algorithms Market Outlook

According to our latest research, the global ovarian cancer multimarker algorithms market size reached USD 1.48 billion in 2024, reflecting robust demand for advanced diagnostic solutions in oncology. The market is projected to expand at a CAGR of 11.2% from 2025 to 2033, reaching a value of USD 4.14 billion by 2033. This growth is primarily driven by the rising incidence of ovarian cancer worldwide and the increasing adoption of multimarker algorithms for early detection and accurate disease management. These algorithms are revolutionizing the diagnostic landscape by integrating multiple biomarkers and advanced analytics, thus enhancing sensitivity and specificity in ovarian cancer detection.

A critical growth factor for the ovarian cancer multimarker algorithms market is the persistent increase in ovarian cancer prevalence across the globe. Ovarian cancer remains one of the most lethal gynecological malignancies, often diagnosed at advanced stages due to the lack of specific symptoms and effective early screening tools. The adoption of multimarker algorithms, which combine biomarkers such as CA-125, HE4, LDH, and beta-hCG, is significantly improving early detection rates and clinical outcomes. Healthcare systems are increasingly prioritizing investments in advanced diagnostics, fueled by supportive government initiatives and heightened awareness among clinicians and patients regarding the benefits of early and accurate diagnosis.

Technological advancements in diagnostic kits, analyzers, and algorithmic software have further propelled the market’s growth trajectory. The integration of artificial intelligence and machine learning into multimarker algorithms is enabling real-time data analysis and interpretation, facilitating faster and more reliable results. Major industry players are actively collaborating with research institutes to develop next-generation platforms that can process complex datasets while maintaining high accuracy. Additionally, regulatory approvals for new diagnostic products and algorithms are accelerating market entry and adoption, particularly in developed economies where healthcare infrastructure is highly advanced.

Another vital growth driver is the expanding application of multimarker algorithms beyond diagnosis to include screening, prognosis, and monitoring of ovarian cancer. The versatility of these algorithms allows for risk stratification, disease progression assessment, and treatment response evaluation, thereby supporting personalized medicine approaches. As the paradigm shifts towards value-based healthcare, payers and providers are increasingly recognizing the cost-effectiveness and clinical utility of multimarker algorithms, further fostering market expansion. The growing trend of decentralizing diagnostics, with an emphasis on point-of-care and home-based testing, is also expected to create new opportunities for market players in the coming years.

From a regional perspective, North America currently dominates the ovarian cancer multimarker algorithms market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The high prevalence of ovarian cancer, well-established healthcare infrastructure, and rapid adoption of innovative diagnostic technologies are key factors supporting market leadership in North America. Europe is witnessing steady growth, driven by increasing government funding for cancer research and rising awareness about early detection. The Asia Pacific region is anticipated to exhibit the fastest CAGR during the forecast period, propelled by improving healthcare access, rising cancer burden, and growing investments in diagnostic infrastructure across emerging economies such as China and India.

Product Type Analysis

The product type segment of the ovarian cancer multimarker algorithms market is broadly categorized into diagnostic kits, analyzers, and software. Diagnostic kits form the backbone of this market, offering ready-t

ObjectivesIt is important to assess the burden of ovarian cancer related premature death so as to develop appropriate evidence-based care and improve women’s health. This study aimed to characterize the long-term trends in mortality, survival and disease burden of ovarian cancer in Shanghai, China.Materials and MethodsCo-morbidities, crude mortality rate (CMR), age-standardised mortality rate by Segi’s world standard population (ASMRW), years of life lost (YLL), and survival rates were analysed. Temporal trends for the mortality rates and disease burden were analyzed using the Joinpoint Regression Program. Mortality rate increases by demographic and non-demographic factors were estimated by the decomposition method.ResultsA total of 1088 ovarian cancer as underlying cause of deaths were recorded. CMR and ASMRW were 4.82/105 and 2.32/105 person-years, respectively. The YLL was 16372.96 years, and the YLL rate was 72.46/105 person-years. The YLL rate increased only in the age group of 70-79 years (P = 0.017). The survival rates of ovarian cancer patients did not improve during the ten year period (2005-2015). The top co-morbidities were diseases of the respiratory system, digestive system, and circulatory system. The rates of ovarian cancer deaths caused by non-demographic and demographic factors increased by 21.29% (95%CI: 4.01% to 41.44%, P = 0.018) and 25.23% (95%CI: 14.64% to 36.81%, P < 0.001), respectively.ConclusionsPopulation ageing and all cause of death may affect ovarian cancer related deaths in Pudong, Shanghai. The high mortality and the stagnant survival rates suggest the need for more efforts in targeted prevention and treatment of this disease.

Ovarian Cancer Market Outlook

The global ovarian cancer market size was estimated to be USD 3.5 billion in 2023, with a projected compound annual growth rate (CAGR) of 7.2% from 2024 to 2032. This robust growth is driven by several factors, including advancements in medical technology, increasing awareness about early diagnosis, and the rising prevalence of ovarian cancer worldwide. By 2032, the market is expected to reach approximately USD 6.5 billion, underscoring the critical need for innovative treatment strategies and accessible healthcare solutions across various regions. Such growth is largely attributed to increasing investments in research and development, leading to novel therapeutic options and the expansion of healthcare infrastructure globally.

One of the significant growth factors for the ovarian cancer market is the increasing incidence and prevalence of the disease. Ovarian cancer remains one of the leading causes of cancer-related deaths among women, primarily due to late diagnosis. Early-stage ovarian cancer often presents with nonspecific symptoms, resulting in late-stage detection when treatment options are limited. This challenge has prompted greater focus on developing better diagnostic tools and screening methods, which is expected to drive market growth. Additionally, lifestyle factors, genetic predispositions, and increasing age are contributing to the rising incidence rates, further scaling the demand for effective treatment solutions.

The advent of precision medicine is another pivotal factor propelling the growth of the ovarian cancer market. Precision medicine involves tailoring healthcare treatments to individual patients based on genetic, environmental, and lifestyle factors. This approach is gaining traction as it promises to improve treatment efficacy and patient outcomes. The development of targeted therapies, which aim to attack specific cancer cells while sparing healthy ones, represents a significant advancement. These therapies are becoming increasingly integrated into standard treatment protocols, supported by favorable clinical outcomes and regulatory approvals. As a result, the market is witnessing an influx of targeted therapy options, offering renewed hope for patients and driving market expansion.

Increasing awareness and advocacy efforts are also playing a crucial role in the growth of the ovarian cancer market. Various organizations and stakeholders are actively involved in raising awareness about ovarian cancer symptoms, risk factors, and the importance of regular medical check-ups. Such initiatives have led to greater patient education and improved healthcare-seeking behaviors, contributing to earlier diagnoses and better management of the disease. Additionally, governmental and non-governmental funding for ovarian cancer research is bolstering the market by facilitating the development of new drugs and treatment methodologies. These collaborative efforts are poised to sustain the market's growth trajectory in the coming years.

The development and availability of Gynaecological Cancer Drugs are pivotal in addressing the treatment needs of ovarian cancer patients. These drugs are specifically designed to target the unique biological characteristics of gynecological cancers, including ovarian cancer, providing more effective and tailored treatment options. The innovation in this field is driven by extensive research and development efforts, focusing on improving drug efficacy and minimizing side effects. As a result, patients have access to a broader range of therapeutic options, enhancing their chances of successful treatment outcomes. The integration of these drugs into standard treatment protocols is supported by clinical evidence demonstrating their benefits in managing ovarian cancer. As the market for gynaecological cancer drugs continues to expand, it is expected to play a significant role in the overall growth of the ovarian cancer treatment landscape.

Regionally, North America dominates the ovarian cancer market, owing to its well-established healthcare infrastructure, high prevalence of the disease, and extensive research activities. The presence of leading pharmaceutical companies and research institutions further supports market growth in this region. Europe follows closely, with countries like Germany, France, and the UK making substantial investments in cancer research and treatment. The Asia Pacific region is expected to witness the fastest growth rate, driven by increasing healthcare awareness and improving healthcare infrastructure. Rapid urbanization and e

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.

This statistic shows the distribution of major types of ovarian cancer among different ethnicities from 2010 to 2014. According to the data, among all races, ** percent of ovarian cancer cases are epithelial ovarian cancer, compared to just * percent of ovarian cancer cases that are sex cord-stromal ovarian cancer.