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 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 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.

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.

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.

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.

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.

ObjectiveOvarian cancer in Black women is common in many West African countries but is relatively rare in North America. Black women have worse survival outcomes when compared to White women. Ovarian cancer histotype, diagnosis, and age at presentation are known prognostic factors for outcome. We sought to conduct a preliminary comparative assessment of these factors across the African diaspora.MethodsPatients diagnosed with ovarian cancer (all histologies) between June 2016-December 2019 in Departments of Pathology at 25 participating sites in Nigeria were identified. Comparative population-based data, inclusive of Caribbean-born Blacks (CBB) and US-born Blacks (USB), were additionally captured from the International Agency for Research on Cancer and Florida Cancer Data Systems. Histology, country of birth, and age at diagnosis data were collected and evaluated across the three subgroups: USB, CBB and Nigerians. Statistical analyses were done using chi-square and student’s t-test with significance set at p<0.05.ResultsNigerians had the highest proportion of germ cell tumor (GCT, 11.5%) and sex-cord stromal (SCST, 16.2%) ovarian cancers relative to CBB and USB (p=0.001). CBB (79.4%) and USB (77.3%) women were diagnosed with a larger proportion of serous ovarian cancer than Nigerians (60.4%) (p<0.0001). Nigerians were diagnosed with epithelial ovarian cancers at the youngest age (51.7± 12.8 years) relative to USB (58.9 ± 15.0) and CBB (59.0± 13.0,p<0.001). Black women [CBB (25.2 ± 15.0), Nigerians (29.5 ± 15.1), and USB (33.9 ± 17.9)] were diagnosed with GCT younger than White women (35.4 ± 20.5, p=0.011). Black women [Nigerians (47.5 ± 15.9), USB (50.9 ± 18.3) and CBB (50.9 ± 18.3)] were also diagnosed with SCST younger than White women (55.6 ± 16.5, p<0.01).ConclusionThere is significant variation in age of diagnosis and distribution of ovarian cancer histotype/diagnosis across the African diaspora. The etiology of these findings requires further investigation.

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.

Yearly citation counts for the publication titled "Epithelial ovarian cancer in younger age versus older age groups: Survival and clinicopathological features".

This statistic shows the death rate of ovary cancer per 100,000 age-adjusted population in the United States from 1999 to 2021. The maximum death rate in the given period for ovary cancer stood at nine in 2001 and 2002.

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.

• 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

Ovarian Cancer Multimarker Algorithms Market Outlook

According to our latest research, the global ovarian cancer multimarker algorithms market size reached USD 247.8 million in 2024 and is anticipated to expand at a CAGR of 13.4% during the forecast period of 2025 to 2033, ultimately reaching USD 740.2 million by 2033. The market is experiencing robust growth, primarily driven by the increasing incidence of ovarian cancer worldwide, the rising demand for early detection tools, and the adoption of advanced diagnostic technologies that improve patient outcomes.

One of the primary growth factors for the ovarian cancer multimarker algorithms market is the rising prevalence of ovarian cancer globally. With ovarian cancer ranking among the leading causes of cancer-related deaths in women, there is a critical need for more precise and earlier diagnostic solutions. Traditional single-marker tests have shown limitations in sensitivity and specificity, often resulting in late-stage diagnoses when treatment options are limited. The integration of multimarker algorithms, which combine multiple biomarkers and clinical parameters, has been proven to enhance diagnostic accuracy, enabling earlier intervention and improved survival rates. This clinical advantage is compelling healthcare providers and institutions to adopt these technologies, thereby fueling market expansion.

Technological advancements in diagnostic kits, assay platforms, and data analytics are also playing a pivotal role in propelling the market forward. The development of sophisticated analyzers and software capable of processing and interpreting complex biomarker data has made multimarker algorithms more accessible and reliable. Companies are investing heavily in research and development to enhance the sensitivity and specificity of their products, as well as to comply with evolving regulatory standards. The increasing acceptance and integration of artificial intelligence and machine learning in diagnostic algorithms further amplify the accuracy and predictive power of these tools, making them indispensable in modern oncology practices. This technological evolution is expected to continue driving the adoption of ovarian cancer multimarker algorithms across diverse healthcare settings.

Another significant driver is the growing awareness among patients and healthcare professionals regarding the benefits of early detection and personalized medicine in ovarian cancer management. Public and private initiatives aimed at improving cancer screening rates, coupled with favorable reimbursement policies in developed markets, have created a conducive environment for the adoption of multimarker diagnostic solutions. Additionally, the expansion of healthcare infrastructure and diagnostic facilities in emerging economies is opening new avenues for market growth. As healthcare systems worldwide focus on reducing the burden of late-stage cancer diagnoses and optimizing patient outcomes, the demand for advanced diagnostic algorithms is expected to surge.

From a regional perspective, North America continues to dominate the ovarian cancer multimarker algorithms market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The region's leadership is attributed to its well-established healthcare infrastructure, high awareness levels, and significant investments in cancer research and diagnostics. Meanwhile, the Asia Pacific region is projected to witness the fastest growth over the forecast period, driven by increasing cancer incidence, improving healthcare access, and rising investments in diagnostic technology. Latin America and the Middle East & Africa are also showing promising growth potential, albeit from a smaller base, as awareness and diagnostic capabilities continue to improve.

Product Type Analysis

The product type segment in the ovarian cancer multimarker algorithms market is comprised of diagnostic kits, assay kits, analyzers, and software. Diagnostic kits represent a significant portion of the market, as they are essential for the rapid and accurate detection of ovarian cancer biomarkers in patient samples. These kits are designed for use in both clinical laboratories and point-of-care settings, offering healthcare professionals reliable tools for early detection and monitoring. The increasing demand for minimally invasive diagnostic options and the growing emphasis on early intervention have driven the adoption of adva

According to Cognitive Market Research, the global ovarian cancer market size is USD 1751.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 24.30% from 2024 to 2031.

North America held the major market of around 40% of the global revenue with a market size of USD 700.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 22.5% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 525.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 402.78 million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.3% from 2024 to 2031.
Latin America market of around 5% of the global revenue with a market size of USD 87.56 million in 2024 and will grow at a compound annual growth rate (CAGR) of 23.7% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 35.02 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.0% from 2024 to 2031.
The targeted therapy held the highest growth rate in ovarian cancer market in 2024.

Market Dynamics of Ovarian Cancer Market

Key Drivers of Ovarian Cancer Market

Rising Incidence of Ovarian Cancer Globally to Increase the Global Demand

The rising global incidence of ovarian cancer is a significant driver amplifying demand within the market. Ovarian cancer ranks among the most prevalent gynaecological malignancies worldwide, with incidence rates steadily climbing. Factors such as aging populations, lifestyle changes, genetic predispositions, and environmental factors contribute to this uptrend. As more cases emerge, there is a heightened need for comprehensive screening, accurate diagnosis, and effective treatment options. This surge in demand extends across various segments of the ovarian cancer market, including diagnostic imaging, pharmaceuticals, and therapeutic interventions. Healthcare providers are under pressure to meet the escalating demand for services, driving investments in infrastructure, research, and innovation. Additionally, governmental and non-governmental initiatives focused on raising awareness and improving access to healthcare further bolster the market's growth trajectory amidst this concerning trend.

For instance, according to the American Cancer Society, more than 19,880 women in the United States are expected to be diagnosed with ovarian cancer in 2021.

(Source:https://www.cancer.org/cancer/types/ovarian-cancer/about/key-statistics.html )

Technological Advancements in Diagnostic Imaging to Propel the Growth

Technological advancements in diagnostic imaging are poised to revolutionize the landscape of ovarian cancer detection and management, driving substantial market growth. Innovations such as high-resolution ultrasound, magnetic resonance imaging (MRI), and positron emission tomography (PET) scans offer enhanced visualization and accuracy in detecting ovarian tumours, leading to earlier diagnosis and improved patient outcomes. Moreover, developments in molecular imaging techniques enable clinicians to identify specific biomarkers associated with ovarian cancer, facilitating personalized treatment strategies. Artificial intelligence (AI) and machine learning algorithms further augment diagnostic capabilities by analyzing complex imaging data, aiding in the early detection and precise characterization of ovarian lesions. As healthcare providers increasingly adopt these cutting-edge imaging technologies, the demand for advanced diagnostic equipment and services is expected to soar, propelling significant growth opportunities in the ovarian cancer market while improving patient care and prognosis. • For instance, Imunon has announced the commencement of subject enrolment at Memorial Sloan Kettering Center Center in Phase I/II clinical trial for DNA-based interleukin-12 immunotherapy IMNN-001, in patients with advanced ovarian cancer. https://www.clinicaltrialsarena.com/news/imunon-ovarian-cancer-trial/ (Source:https://www.clinicaltrialsarena.com/news/imunon-ovarian-cancer-trial/ )

• For instance, Avastin is a precision cancer medicine that targets a protein known as VEGF. VEGF plays a key role in the development of new blood vessels necessary for cancer cell growth. By blocking VEGF, Avasti...

In 2022, there were ***** recorded cases of ovarian cancer in South Korea. This is more than double the number of cases recorded in 1999. There has been an overall trend of increase in ovarian cancer cases.

This dataset, released September 2017, contains data on the female cancer incidences during 2006-2010 by Breast cancer, Colorectal Cancer, Melanoma of the skin, Lung cancer, Uterine cancer, Lymphoma cancer, Leukaemia cancer, Thyroid cancer, Ovarian cancer, Pancreatic cancer, All other cancers and All cancers combined. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Compiled by PHIDU from an analysis by the Australian Institute of Health and Welfare (AIHW) of theAustralian Cancer Database (ACD) 2012. The ACD is compiled at the AIHW from cancer data provided by state andterritory cancer registries. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

In our study, we have generated proteomic and genomic (RNA sequencing and whole genome sequencing) profiles from high grade serous ovarian cancer (HGSOC) tumor biopsies. All biospecimens are formalin-fixed, parrafin-embedded (FFPE) tissues and annotated for patient sensitivity to platinum chemotherapy (refractory or sensitive). For all 174 tumors that were analyzed, we have H&E-stained and imaged the first and last sections (“bookend”) cut from each FFPE block to allow study of tumor pathology. These H&E pathology images are uploaded in this dataset. The 174 tumors represented 158 unique patients (imaging was performed on two FFPE blocks for a small subset of patients where additional tumor mass was required for proteomic analysis). The bookend FFPE slides were cut at 4 μm thickness using a microtome and mounted on glass slides (Leica Biosystems Cat# 3800040) for H&E staining. Digital images of the H&E slides were recorded using a ScanScope AT Slide Scanner (Leica Aperio Technologies, Vista, CA, USA) under 20X objective magnification (0.5 μm resolution). Images were analyzed by HALO Image Analysis Platform software (Indicta Labs, Albuquerque, New Mexico, USA).

The following clinical data are also provided for these subjects:

• Image file name (combination of Image Name and Image ID) and corresponding sample IDs
• Chemotherapy response status: (sensitive/refractory; refractory is defined in our study as clinical ovarian cancer that progresses while on platinum-based therapy or within 4 weeks)
• neo-adjuvant treatment (yes/no)
• Tumor location
• Tumor grade, stage, substage
• Patient age
• Patient ethnicity
• Patient race
• Age of sample

The goals of the study were to understand mechanisms of platinum resistance in epithelial ovarian cancers (EOCs) in order to: i) predict EOCs that will respond to DNA-damaging platinum therapy, and ii) identify potential new drug targets in resistant disease to point to desperately needed new therapeutic approaches. The ability to predict platinum-resistant/refractory disease would be clinically impactful by enabling the immediate triage of patients with refractory disease to clinical trials of experimental therapies, avoiding use of ineffective standard of care chemotherapy and helping to identify novel treatments. In this study, we generated proteomic and genomic (RNA sequencing and whole genome sequencing) profiling datasets and H&E images from high grade serous ovarian cancer (HGSOC) tumor biopsies representing platinum-sensitive and platinum-refractory disease.

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.

This dataset, released September 2020, contains data on the female cancer incidences during 2010-2014 by Breast cancer, Colorectal Cancer, Melanoma of the skin, Lung cancer, Uterine cancer, Lymphoma cancer, Leukaemia cancer, Thyroid cancer, Ovarian cancer, Pancreatic cancer, All other cancers and All cancers combined. The data is by Local Government Area (LGA) 2016 geographic boundaries.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU from an analysis by the Australian Institute of Health and Welfare (AIHW) of the Australian Cancer Database (ACD) 2015. The ACD is compiled at the AIHW from cancer data provided by state and territory cancer registries: for further information on the ACD see link. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.