Review ArticleReview
Open Access

Global epidemiology of ovarian cancer: patterns, trends, and risk factors

Ruyuan Li, Anqi Zhao, Meicen Liu, Lingeng Lu, Bin Li and Hongmei Zeng
Cancer Biology & Medicine March 2026, 20250619; DOI: https://doi.org/10.20892/j.issn.2095-3941.2025.0619

Abstract

Ovarian cancer was the eighth most frequently diagnosed cancer among women in 2022. The global age-standardized incidence rate of ovarian cancer decreased from 7.22/100,000 to 6.71/100,000 from 1990 to 2021. However, incidence trends varied across countries. Declining ovarian cancer incidence rates were reported in high-income countries, such as the United States, Austria, the Netherlands, and Norway, while there were increasing incidence rates in Africa and parts of Asia, including Japan and India. The global age-standardized mortality rate of ovarian cancer decreased from 4.73/100,000 to 4.06/100,000 between 1999 and 2021 with varying trends among countries. Moreover, the age-standardized 5-year net ovarian cancer survival rate in most countries remained < 50%. Several specific factors related to ovarian cancer risk have been identified, including reproductive factors, use of oral contraceptives, anti-inflammatory diets, endometriosis, pelvic inflammatory disease, obesity, diabetes, and occupational asbestos exposure. No screening or prevention strategy has been proven effective in downstaging or reducing mortality from ovarian cancer in an average-risk population without a family cancer history or pathogenic variants. Indeed, risk-reducing salpingo-oophorectomy remains the gold standard for lowering the risk of ovarian cancer in high-risk individuals with hereditary mutations. This review provides a comprehensive overview of the epidemiology, risk factors, screening, and prevention of ovarian cancer, aiming to offer a global perspective on public health strategies for addressing the disease.

keywords

Introduction

Ovarian cancer ranks as the eighth most frequently diagnosed cancer and the eighth leading cause of cancer-related deaths in women worldwide. An estimated 324,603 women were diagnosed with ovarian cancer in 2022 and 206,956 died of the disease according to GLOBOCAN 20221. The global number of ovarian cancer cases and deaths is projected to increase by 46.9% and 62.7%, respectively, reaching 476,912 cases and 336,637 deaths by 20502. Although there have been advances in ovarian cancer screening, detection, and treatment methods over the past several decades, particularly targeted therapy and immunotherapy3, ovarian cancer remains largely incurable with a 5-year net survival < 50% in most countries4. The substantial disease burden underscores the public health significance given the persistent challenges in treating ovarian cancer. A structured search of PubMed, Web of Science, and EMBASE (January 1990–May 2025) was performed using terms related to ovarian cancer epidemiology, risk factors, screening, and prevention. Peer-reviewed original studies, pooled analyses, meta-analyses, and large population-based investigations were included, while non-English publications, case reports, conference abstracts, and studies lacking methodologic detail were excluded. Additional sources were identified through manual reference screening. This review presents the most recent and comprehensive evidence on the epidemiology, risk factors, screening, and prevention of ovarian cancer with the aim of enhancing global understanding and informing public health strategies to reduce the burden of disease.

Pathologic classification of ovarian cancer

Ovarian cancer encompasses a heterogeneous group that originates from epithelial and non-epithelial cells, resulting in two major subtypes (epithelial and non-epithelial ovarian cancer). Each subtype is characterized by distinct origins, pathologic features, epidemiologic patterns, and risk factors.

Epithelial ovarian cancer is the most common form, accounting for 90%–95% of all ovarian cancers5,6. Epithelial cancer is classified histologically into serous (52%), endometrioid (10%), mucinous (6%), clear cell (6%), and unspecified subtypes (approximately 25%)7. Low-grade serous carcinomas are thought to originate from fallopian tube epithelium (endosalpingiosis) or serous ovarian borderline tumors, whereas endometrioid and clear cell carcinomas originate from endometrial tissue (endometriosis)8. Most mucinous carcinomas are believed to derive from transitional epithelium at the tuboperitoneal junction9.

Epithelial ovarian cancer can be further grouped as type I or II ovarian cancer according to clinicopathologic and molecular characteristics9. Type I epithelial ovarian cancers are generally low-grade and indolent, genetically stable, large, unilateral, cystic tumors that are confined to the ovary and are believed to develop from extraovarian benign lesions. Type II epithelial ovarian cancers typically present in an advanced stage and are high-grade bilateral types with aggressive behavior and lethal survival. Type II epithelial ovarian cancers are thought to originate as fallopian tube fimbriae carcinomas that spread to the ovaries and/or peritoneum9,10.

Non-epithelial ovarian cancer consists of sex cord-stromal tumors (e.g., granulosa cell tumors and thecomas) and germ cell tumors (e.g., teratomas and dysgerminomas). These subtypes are relatively rare and occur more frequently in younger women.

The endometrioid subtype of localized and regional epithelial ovarian cancer exhibits the most favorable prognosis, followed by low-grade serous and mucinous ovarian cancer. Ovarian carcinosarcoma is associated with the poorest prognosis with a 5-year survival rate < 50%. However, distant-stage ovarian cancer, which accounts for the majority of diagnoses, presents a comparatively worse prognosis. Within these classifications, low-grade serous, endometrioid, and high-grade serous ovarian cancer have the best prognosis, followed by clear cell and mucinous subtypes. Ovarian carcinosarcoma has the worst histology of ovarian cancer6.

Descriptive epidemiology and time trend of the cancer burden

The global annual incident cases of ovarian cancer reached 324,603 in 2022 according to GLOBOCAN1; the geographic incidence variation differed worldwide. The highest age-adjusted incidence rates for ovarian cancer were in Eastern Europe at 11.0/100,000, followed by Northern Europe (9.1/100,000), Southern Europe (8.4/100,000), and South-East Asia (8.1/100,000). The lowest age-adjusted incidence rates for ovarian cancer were in Middle Africa (4.3/100,000), Southern Africa (4.9/100,000), and the Caribbean (4.9/100,000). Detailed data for each region are shown in Table 1.

View this table:
Table 1

Age-standardized ovarian cancer incidence and mortality rates in 2022

Countries with a very high human development index (HDI) had the highest age-adjusted incidence rates for ovarian cancer (8.2/100,000). Low HDI countries had the lowest incidence rate for ovarian cancer (4.9/100,000). The global age-standardized incidence rate for ovarian cancer declined from 7.22/100,000 to 6.71/100,000 from 1990 to 2021 with an estimated annual percentage change of −0.38 [95% confidence interval (CI), −0.43 to −0.32]1,11. Detailed data from 2005–2016 revealed a decreased incidence rate for ovarian cancer in Australia, the USA, Denmark, Sweden, Germany, France, Colombia, and Norway, which was due, at least in part, to the increased use of oral contraceptive pills and the decreased administration of menopausal estrogen-only hormone therapy7,12. In contrast, the incidence rates for ovarian cancer in Eastern Europe and some regions of Asia have been increasing, particularly in Belarus, Japan, Thailand, and India12,13. Of note, the lower use of oral contraceptive pills and lower parity might partially explain the increasing rates14. Statistics in China showed the age-standardized incidence rates for ovarian cancer were relatively stable from 2011–201815.

The global new deaths from ovarian cancer have reached 206,956 according to GLOBOCAN 2022. Regionally, the highest age-adjusted mortality rates for ovarian cancer were also in Eastern Europe (6.1/100,000), followed by South-East Asia (5.1/100,000), and Northern Europe (4.8/100,000). Eastern Asia had the lowest age-adjusted mortality rate for ovarian cancer (2.7/100,000). Countries with a medium HDI had the highest age-adjusted mortality rate for ovarian cancer (4.5/100,000), while the high HDI countries had the lowest mortality rate (3.3/100,000). The global age-standardized mortality rate for ovarian cancer declined from 1999–2021 by an estimated annual percentage change of −0.62 (95% CI, −0.68 to −0.57)11. The mortality rate of ovarian cancer in the USA declined between 1976 and 2015 by 33.0%7. However, the mortality rate for ovarian cancer in China showed an upward trend with an average annual percentage change of 4.4% from 2000–201815.

The age-standardized 5-year net ovarian cancer survival rate in most countries was still < 50% for women diagnosed from 2010–2014 according to the CONCORD-34. Survival rates for ovarian cancer ranged from 40.0%–49.0% in Canada, the USA, China, Japan, Korea, Singapore, Austria, Finland, France, Germany, Iceland, Norway, Portugal, Sweden, Switzerland, and Australia. Survival rates for ovarian cancer were < 30% in Chile and < 20% in India. The survival trend for ovarian cancer has remained relatively flat between 1995-1999 and 2010-20144,16. Improvements in 5-year survival for ovarian cancer were reported across 17 countries, including the USA, Japan, Korea, Bulgaria, the Czech Republic, Denmark, France, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Spain, Switzerland, the UK, and Australia. The most remarkable improvement in survival for ovarian cancer was observed in Japan with an increase of 11.1%; detailed data are shown in Figure 1. The updated age-standardized 5-year relative survival rate for ovarian cancer in China between 2019 and 2021 was 39.6%; the rate was stable from 2008–202117. The 5-year relative ovarian cancer survival rate in the US increased from 44.6% to 52.8% between 2000 and 2017 with an average absolute increase of 0.4% according to the SEER database of 21 registries18.

Figure 1
Figure 1

Age-standardized 5-year net ovarian cancer survival in different countries from 2000 to 2014. This figure illustrates the geographic and temporal variations in ovarian cancer survival across select countries. A key box above the graph explains the colored lines representing different time periods. The x-axis shows the countries sorted from highest-to-lowest average survival rate. The y-axis shows the age-standardized 5-year net survival rate expressed as a percentage (%).

Disability-adjusted life years and years lived with disability

Table 2 illustrates the disability-adjusted life years (DALYs) and years lived with disability (YLDs) for ovarian cancer19. The global DALYs count reached 5,160,000/100,000 in 2021. The European region had the highest DALYs count (1,260,000/100,000), followed by South-East Asia (1,130,000/100,000) and the Western Pacific region (1,120,000/100,000). The Eastern Mediterranean region had the lowest DALYs count (320,000/100,000). The global DALYs for ovarian cancer increased from 1990 to 2021 (Figure 2A).

View this table:
Table 2

Ovarian cancer DALYs and YLDs in the global burden of disease study 2021

Figure 2
Figure 2

Global trends in disability-adjusted life years (DALYs) and years lived with disability (YLDs) due to ovarian cancer (1990–2021). This figure presents the global disease burden of ovarian cancer over three decades, as measured by DALYs (A) and YLDs (B). The global trends in DALYs and YLDs from ovarian cancer showed an increasing trend from 1990 to 2021.

The global YLDs count rose to 155,650/100,000 in 2021. The regional distribution of ovarian cancer YLDs was comparable to DALYs. The European region had the highest YLDs count (38,100/100,000), followed by the Western Pacific region (37,280/100,000) and South-East Asia (33,320/100,000). The Eastern Mediterranean region had the lowest YLDs count (8300/100,000). The global YLDs trend for ovarian cancer also exhibited a similar increase from 1990–2021 (Figure 2B).

The age-specific DALYs counts in 2021 are shown in Figure 3A. The DALYs counts increased with advancing age at the time of diagnosis. Women > 70 years of age had the highest DALYs count (1,130,000/100,000). The age-specific YLDs counts in 2021 are shown in Figure 3B. The YLDs increased with advancing age at the time of diagnosis. However, it is worth noting that both DALYs and YLDs counts declined in women who were diagnosed between 60 and 69 years of age, which may be attributed to the use of oral contraceptives12.

Figure 3
Figure 3

Age-specific disability-adjusted life years (DALYs) and years lived with disability (YLDs) for ovarian cancer in 2021. This figure depicts the distribution of the ovarian cancer disease burden across different age groups in 2021. The x-axis represents the age groups at the time of diagnosis. The y-axis represents the DALYs (A) or YLDs counts (B).

Risk factors for ovarian cancer

Several factors have been shown to be associated with the risk of ovarian cancer, including reproductive, behavioral, dietary, metabolic, medical, genetic, and environmental factors (Table 3, Figure 4). The risk estimates presented in Table 3 were extracted from published meta-analyses or pooled studies, each of which used a multivariable-adjusted analytical framework as reported by the original authors. Because effect modifiers and confidence intervals were not uniformly available across studies, these values should be interpreted as summarized associations rather than harmonized effect sizes derived from a single analytical model.

View this table:
Table 3

Factors associated with the risk of ovarian cancer

Figure 4
Figure 4

Summary of risk factors for ovarian cancer. This schematic summarizes and categorizes a spectrum of factors linked to ovarian cancer risk, distinguishing between factors that increase risk and factors that confer protection. The factors are organized into seven interconnected categories for systematic understanding, including genetic, reproductive, behavioral, dietary, metabolic, medical, and environmental factors.

Reproductive factors

Studies have shown that childbirth has a protective effect on epithelial ovarian cancer and the effect is subtype-dependent. The risk of ovarian cancer decreased by 6% [relative risk (RR), 0.94; 95% CI, 0.92–0.96] with each additional birth among women who have had children in a prospective study involving 1.1 million UK women with the greatest reduction in the risk of clear cell carcinoma (RR, 0.75; 95% CI, 0.65–0.85)20. However, in a Finnish cohort study involving 87,929 multiparous women, multiparity (> 5 births) did not provide additional protection against ovarian cancer37. In summary, these findings indicated that while the number of pregnancies is negatively correlated with the risk of ovarian cancer, this protective effect does not follow a linear dose-response relationship with the number of pregnancies. In fact, evidence suggests that most of this protective effect is attributable to the first three pregnancies38.

The current mainstream view is that the cessation of ovulation during pregnancy and breastfeeding inhibits the division and proliferation of ovarian epithelial cells, thereby reducing the chance of initiating or promoting carcinogenesis39. A pooled analysis of 13 case-control studies from the Ovarian Cancer Association Consortium showed that breastfeeding can reduce the risk of invasive ovarian cancer by 24% with the greatest reduction observed in high-grade serous ovarian cancer. The duration of breastfeeding can further reduce the risk of ovarian cancer39. The risk of ovarian cancer can be reduced by approximately 10% for every 12 months of breastfeeding (RR, 0.89; 95% CI, 0.84–0.94)20.

Dietary and metabolic factors

Inflammation is a normal physiologic process but long-term, persistent chronic inflammation may promote carcinogenesis by damaging important cell components, activating tumor-promoting signaling pathways, promoting abnormal proliferation, and inhibiting apoptosis40. Pro-inflammatory diets have been shown to be a risk factor for ovarian cancer in multiple case-control studies41,42. Indeed, there was a positive correlation between dietary inflammatory potential, as measured by the dietary inflammatory index (DII), and the incidence of ovarian cancer in a meta-analysis of six studies. Individuals with higher DII scores had a 42% increased risk for ovarian cancer [odds ratio (OR), 1.42; 95% CI, 1.19–1.65]23. A multicenter case-control study from Italy involving 1031 ovarian cancer cases and 2411 non-ovarian-cancer cases suggested that a diabetes risk reduction diet reduces the risk of ovarian cancer43. A meta-analysis of 97 cohort studies showed that green vegetable, fiber, and green tea intake reduced the risk of ovarian cancer, while total fat, saturated fat, saturated fatty acids, cholesterol, and retinol intake significantly increased the risk21,22.

Several studies have shown that overweight and obesity increase the likelihood of developing ovarian cancer44,45. Cancer cells often exhibit altered metabolic pathways with increased fatty acid oxidation, glycolysis, and glutaminolysis, which contribute to ovarian cancer growth46. Research indicates that overweight or obesity can increase the risk of cancer through multiple pathways, including hyperinsulinemia/insulin resistance and abnormalities in the insulin-like growth factor-I (IGF-I) system and signaling, the biosynthesis and action pathways of sex hormones, subclinical chronic low-grade inflammation, and oxidative stress47. A Mendelian study suggested a causal link between obesity and aggressive epithelial ovarian cancer with body mass index (BMI) differentially associated with histologic subtypes of ovarian cancer48. A pooled analysis of 15 case-control studies in the Ovarian Cancer Association Consortium, including 13,548 cases and 17,913 controls, suggested that high BMI increases the risk of serous borderline ovarian tumor (OR, 1.24; 95% CI, 1.18–1.30), invasive endometrioid ovarian cancer (OR, 1.17; 95% CI, 1.11–1.23), and invasive mucinous ovarian cancer (OR, 1.19; 95% CI, 1.06–1.32) but not high-grade invasive serous cancer24.

Diabetes may also increase the risk of ovarian cancer. Cancer cells rely on aerobic Warburg metabolism to meet the energy needs. Cancer cells also synthesize fatty acids, proteins, and nucleotides. Therefore, cancer cells continuously require an increased supply of glucose and diabetes-related hyperglycemia may fuel this demand47. A meta-analysis of 9 case-control and 27 cohort studies suggested that patients with diabetes had a relatively increased risk of ovarian cancer (RR, 1.32; 95% CI, 1.14–1.52)25 and the association between diabetes and ovarian cancer is more significant in Asian populations49.

Lifestyle and psychological factors

Prolonged sitting time may increase the risk of ovarian cancer50. In a cohort study involving 173,688 participants, women who sat for 10–19 h/week [hazard ratio (HR), 1.25; 95% CI, 1.04–1.51] and women who sat for ≥ 20 h/week (HR, 1.40; 95% CI, 1.14–1.71) had an increased risk compared to women who sat for < 5 h/week26. Another meta-analysis involving 26 studies showed that women who engaged in regular recreational physical activity had a 30%–60% lower risk of ovarian cancer51.

Chronic stress is linked to increased ovarian cancer risk. Stress can lead to increased concentrations of adrenaline and norepinephrine, activate β-adrenergic signaling, then participate in the regulation of various cellular processes involved in the occurrence and development of cancer, such as promoting tumor angiogenesis, inducing DNA damage, inhibiting DNA repair, and reducing tumor cell apoptosis52. In a study that included 115,694 participants with > 21 years of follow-up, women who experienced ≥ 3 distress-related psychosocial factors had a > 70% increased risk of ovarian cancer compared to women who had < 3 distress-related psychosocial factors (HR, 1.71; 95% CI, 1.16–2.52). Notably, when post-traumatic stress disorder was included, the association between distress-related factors and ovarian cancer was strengthened27.

Medical history

Oral contraceptives have long been known to reduce the incidence of ovarian cancer by inhibiting the ovulation process. Ovulation causes repeated microtrauma to the ovarian epithelial surface, which increases the risk of malignant transformation. This process may explain why oral contraceptives reduce the risk of ovarian cancer45, especially in women with endometriosis53. A Meta-analysis involving 23,257 women with ovarian cancer and 87,303 women without ovarian cancer from 45 studies in 21 countries showed that oral contraceptives can prevent ovarian cancer in the long term (RR, 0.73; 95% CI, 0.70–0.76); the duration of oral contraceptive usage and the reduction in risk displayed a dose-response relationship28. However, the protective effect of oral contraceptives may decrease with age or after discontinuation.

Hormone replacement therapy (HRT), which has been widely used to treat menopausal symptoms in women, is associated with an increased risk of ovarian cancer. The main HRT regimens include estrogen alone [estrogen replacement therapy (ERT)] or an estrogen and progestin combination [estrogen-progestin replacement therapy (EPRT)]. The relationship between HRT and the risk of ovarian cancer has not been consistent across studies. A meta-analysis of 52 epidemiologic studies conducted since 1970 showed that for every 1000 women who received hormone therapy for 5 years starting at approximately 50 years of age, 1 additional ovarian cancer patient would be diagnosed. The increase was most pronounced for serous (RR, 1.53; 95% CI, 1.40–1.66) and endometrioid ovarian cancer (RR, 1.42; 95% CI, 1.20–1.67)29. Studies have shown a significant correlation between the use of ERT and the incidence of ovarian cancer, while the use of EPRT alone did not increase the risk54. This finding may be explained by the fact that most ovarian tumors are estrogen receptor-positive and progesterone may counteract the proliferative effect of estrogen by promoting ovarian cell apoptosis45. A meta-analysis of 21 cohort studies showed that the use of HRT increased the risk of ovarian cancer. However, when the research time frame was limited to the past decade, the associated risk was minimal, indicating that the impact of HRT on the incidence of ovarian cancer is not durable55.

Endometriosis is a recognized risk for ovarian cancer that shares overlapping genetic susceptibility with endometrioid and clear cell subtypes56. A large international case-control study including 13,226 controls and 7911 invasive ovarian cancer cases showed that endometriosis was most strongly associated with clear cell carcinoma (OR, 3.05; 95% CI, 2.43–3.84) and endometrioid carcinoma (OR, 2.04; 95% CI, 1.67–2.48)30. In like manner, a cohort study involving 450,906 women (78,476 with endometriosis and 372,430 without) showed that women with endometriosis had a higher risk of type I ovarian cancer than women without endometriosis, especially patients with deep infiltrating endometriosis or ovarian endometriotic cysts53.

Inflammation has been linked to ovarian cancer. In a population-based case-control study including 554 Danish women with invasive ovarian cancer, pelvic inflammatory disease (PID) was associated with increased risk of borderline ovarian tumor (OR, 1.50; 95% CI, 1.08–2.08)31. A Swedish case-control study, including 4782 cases and 45,167 controls also reported an elevated risk of serous borderline ovarian tumor among women with a history of PID (OR, 1.76; 95% CI, 1.36–2.29)32.

Current evidence suggests that high-grade serous ovarian cancer originates from the distal fallopian tube epithelium, forming serous tubal intraepithelial carcinoma that can shed and implant on the ovarian surface57. Women who underwent unilateral or bilateral salpingectomy in a population-based cohort study spanning from 1973–2009 in Sweden (n = 34,433) had a significantly lower risk of ovarian cancer compared to women who had not undergone unilateral or bilateral salpingectomy [n = 5,449,119] (HR, 0.65; 95% CI, 0.52–0.81). The reduction in ovarian cancer risk was greater with bilateral salpingectomy than with unilateral33. Given the long study period, potential variations in diagnostic criteria, surgical practices, and classification systems should be considered. For example, changes in Swedish surgical coding after 1997 prevented distinction between unilateral and bilateral salpingectomy, which limited stratified analyses. Nevertheless, because meaningful risk reduction typically emerges >10 years after salpingectomy, the extended follow-up and large sample size still lend substantial strength to these findings. Evidence linking hysterectomy to the risk of ovarian cancer remains inconsistent. Several studies have found no significant association between hysterectomy for benign gynecologic conditions and ovarian cancer incidence58,59, whereas other studies have reported a modest reduction in risk60.

Genetic factors

Hereditary breast-ovarian cancer syndrome is a major genetic predisposition to ovarian cancer. Hereditary breast-ovarian cancer syndrome results primarily from pathogenic variants (mutations) in the BRCA1 or BRCA2 genes, which have key roles in DNA damage repair61. BRCA1 and BRCA2 gene mutations are associated with a high lifetime risk of ovarian cancer. It is estimated that by 70 years of age, the average cumulative risk of ovarian cancer for BRCA1 mutation carriers is 41% and 15% for BRCA2 mutation carriers62.

Lynch syndrome also contributes to hereditary ovarian cancer. Lynch syndrome is an autosomal dominant disorder caused by germline pathogenic variants in DNA mismatch repair (MMR) genes. Women with a family history of Lynch syndrome have a 6.7% lifetime risk of ovarian cancer35. The increased risk of ovarian cancer in Lynch syndrome is not histology-specific. In contrast, high-grade serous carcinoma is nearly the only histologic type of hereditary ovarian cancer in hereditary breast-ovarian cancer syndrome with BRCA mutations, suggesting that hereditary breast-ovarian cancer may have a different nature from ovarian cancer in Lynch syndrome63.

Environmental and occupational exposures

Some occupational and environmental exposures may increase ovarian cancer risk. A meta-analysis of 18 cohort studies involving women with occupational asbestos exposure showed that asbestos exposure was associated with an increased risk of ovarian cancer (standardized mortality ratio, 1.77; 95% CI, 1.37–2.28)36. Talc, which is structurally similar to asbestos, was among the first environmental risk factors identified for ovarian cancer. Earlier studies suggested that talc use increases the risk of ovarian tumors, especially serous subtypes64. However, recent evidence remains inconsistent. Case-control studies often report a weak positive association, whereas cohort studies consistently showed null results, with the discrepancy likely due to recall bias or residual confounding65–67.

Racial and ethnic disparities

Ovarian cancer risk and outcomes differ across racial and ethnic groups and are influenced by underlying social determinants, such as socioeconomic status (SES) and access to healthcare. Data from the US Centers for Disease Control and Prevention indicated that ovarian cancer rates are the highest among non-Hispanic American Indian, native Alaskan, and non-Hispanic White women, while the rates are low among Hispanic, non-Hispanic Asian and Pacific Islander, and non-Hispanic Black women. The higher rates among non-Hispanic White women may be due to the higher incidence of hereditary breast and ovarian cancer mutations in the Ashkenazi Jewish population68. SES can influence the risk and prognosis of ovarian cancer through multiple pathways. Women from lower-SES backgrounds are more likely to experience adverse lifestyle and metabolic factors, such as obesity and chronic inflammation, and to have lower utilization of preventive healthcare services, all of which may increase ovarian cancer risk69. Limited access to gynecologic care, delay in diagnosis, and decreased adherence to guideline-recommended treatment have also been associated with a higher likelihood of presenting with advanced-stage ovarian cancer and poorer survival outcomes70. Furthermore, a meta-analysis demonstrated marked disparities in treatment adherence and mortality across racial and socioeconomic groups. Black patients had a 25% lower rate of adherence to ovarian cancer treatment (RR, 0.75; 95% CI, 0.66–0.84) and an 18% higher risk of mortality (RR, 1.18; 95% CI, 1.11–1.26) compared to White patients. Patients in the lowest SES category had a 15% lower adherence rate compared to patients in the highest SES group (RR, 0.85; 95% CI, 0.77–0.94) and individuals with fewer hospital visits showed a 30% lower adherence rate compared to patients with more frequent healthcare contact (RR, 0.70; 95% CI, 0.58–0.85)71.

Screening of ovarian cancer

In ovarian cancer screening women are divided into average- and high-risk populations. Women with a family history of cancer or carrying BRCA1, BRCA2, or other pathogenic variants have an increased ≥ 10% lifetime risk of ovarian cancer compared to women with an average risk72,73. Because patients with early-stage ovarian cancer frequently lack symptoms, screening and early detection remain challenging.

Clinical trials, such as SCSOCS (82,487 participants with a mean follow-up of 9.2 years) and PLCO (78,216 participants with a mean follow-up of 12.4 years), demonstrated that screening strategies using serum CA-125 combined with transvaginal ultrasound did not result in downstaging or a reduction in ovarian cancer mortality for women at average risk74. The UKCTOCS trial, which involved 202,638 participants with a median follow-up of 16.3 years, reported that a multimodal screening strategy consisting of longitudinal serum CA-125 levels interpreted by the risk of ovarian cancer algorithm (ROCA) calculation combined with transvaginal ultrasound findings, down-staged ovarian cancer but did not reduce ovarian cancer mortality75. Therefore, screening is not recommended for average-risk individuals according to the international guidelines from the National Comprehensive Cancer Network, the European Society of Medical Oncology, and the Society of Gynaecological Oncology and Ovarian Cancer Alliance76,77.

A GOG trial involving 3692 high-risk women with a strong family history of BRCA1/2 pathogenic variants and a median follow-up of 6 years showed that ROCA-based multimodal screening every 3 months had better sensitivity and high specificity for early-stage ovarian cancer23. The UKFOCSS trial recruited 4348 high-risk women (≥ 10% lifetime risk) and performed ROCA-based multimodal screening every 4 months. This strategy resulted in downstaging of ovarian cancer after a median follow-up of 4.8 years. However, the impact on mortality could not be evaluated42. A serum CA-125 level and transvaginal ultrasound screening findings remain an option with uncertain benefit for high-risk women who wish to delay or decline risk-reducing surgery76,77.

Artificial intelligence (AI)-enabled screening has recently emerged as a potential approach for early detection of ovarian cancer. Research involving 10,992 individuals (1 internal validation set of 3007 individuals and 2 external validation sets of 7985 individuals) from China used an AI model to interpret laboratory tests, achieved an area under the receiver-operating characteristic curve (AUC) of 0.949 in the internal validation set and an AUC of 0.88 in the external validation sets78. Another deep learning model using 17,119 ultrasound images from 3652 patients across 20 centers in 8 countries also reported promising diagnostic accuracy in detecting ovarian cancer79. The integration of AI into ovarian cancer screening offers a promising approach for improving early detection by uncovering complex, non-linear relationships within multi-modal datasets that may elude traditional analysis. However, this potential is limited by significant challenges, including the “black-box” nature of many algorithms, which can obscure the reasoning underlying decisions. Rigorous external validation and prospective clinical trials are also necessary to demonstrate a tangible impact on patient outcomes, such as down-staging or reduced ovarian cancer mortality.

Several novel biomarkers and screening strategies are under investigation, including DNA methylation biomarkers80, circulating tumor (ct) DNA81, glycosylated CA-12582, and other candidate biomarkers, such as Osteopontin, Human Epididymis Protein 4, and so on83. Targeted cell-free DNA methylation analysis had sensitivities for ovarian cancer of 83.1% (95% CI, 72.2–90.3%) across all stages and 50% (95% CI, 23.7–76.3%) in stage I based on the Circulating Cell-free Genome Atlas (CCGA) study84. Whether these new methods can achieve down-staging or reduce ovarian cancer mortality requires further investigation.

Prevention of ovarian cancer

Although ovarian cancer screening has limited efficacy, both non-surgical and surgical options are available to reduce ovarian cancer risk.

Non-surgical prevention is primarily achieved through use of oral contraceptives12. Oral contraceptive use is associated with a 40%–50% reduction in lifetime ovarian cancer risk among women at average risk85. Women carrying BRCA1 or BRCA2 mutations are also advised to consider oral contraceptive use for prevention86. A longer duration of oral contraceptive use provides greater protection for average- and high-risk populations, although the potential risk of thrombosis should be considered87,88. The levonorgestrel intrauterine device (LNG-IUD) has also been shown to reduce ovarian cancer risk in women at average risk for ovarian cancer89,90.

Risk-reducing salpingo-oophorectomy (RRSO) can reduce ovarian cancer risk by 80%–97% in BRCA carriers and reduce the mortality rate. Therefore, for high-risk individuals who have completed childbearing, bilateral RRSO is recommended for pre-menopausal women with a ≥ 4% lifetime risk of ovarian cancer and post-menopausal women with a ≥ 5% lifetime risk of ovarian cancer72,91. BRCA1 mutation carriers are advised to undergo RRSO between 35 and 40 years of age, BRCA2 carriers between 40 and 45 years of age, and RAD51C, RAD51D, PALB2, and BRIP1 carriers ≥ 45 years of age92. The timing of RRSO may be individualized based on family history and personal choices86. HRT is recommended for pre-menopausal women following RRSO if they do not have a personal history of breast cancer93,94.

Risk-reducing salpingectomy with or without delayed oophorectomy (RRSDO) represents an alternative for premenopausal women who wish to retain ovarian function72,86. The multicenter non-randomized controlled TUBA study recruited 577 women with a BRCA1/2 pathogenic variant from the Netherlands and showed that women undergoing RRSDO reported a better menopause-related quality of life than women who underwent RRSO95. The USWISP study used a similar design in 190 women and reported that the RRSO group exhibited worsening menopausal symptoms and greater decision regret96. The long-term safety and prophylactic effect of RRSDO are under evaluation. The ongoing TUBA-WISP II trial aims to determine whether delayed oophorectomy after salpingectomy is non-inferior to immediate RRSO in reducing tubo-ovarian cancer risk. However, follow-up remains too short for conclusions97. To date, RRSO remains the gold standard for risk reduction98.

RRSO should not be undertaken for ovarian cancer prevention in women at average risk. Opportunistic bilateral salpingectomy (OBS) may be offered at the time of benign gynecologic surgery, after childbearing, and following counselling on benefits and risks with informed consent72,91.

Endometriosis management should also be considered with respect to endometriosis-associated ovarian cancer. Medical or surgical treatment should be individualized according to age, reproductive plans, and disease characteristics99.

Therapeutic landscape

Although each subtype of ovarian cancer has various clinical features, molecular characteristics, and different prognoses, the subtypes share a similar principle of treatment. Complete cytoreduction (R0 resection) remains the cornerstone of ovarian cancer treatment across disease stages and settings, including primary, interval, and secondary cytoreductive surgery100,101. Platinum-based combination chemotherapy remains the standard first-line regimen for most histologic subtypes of ovarian cancer. The introduction of targeted therapy has transformed management paradigms. Bevacizumab, a monoclonal antibody targeting VEGF, is recommended in combination with cytotoxic chemotherapy for stage II–IV disease, followed by maintenance therapy (ICON7 and GOG-218)102,103. However, neither trial showed a significant overall survival benefit in the entire population. Further analysis in ICON-7 demonstrated that the high-risk subgroup, defined by stage IV ovarian cancer, inoperable stage III disease, or sub-optimally debulked (> 1 cm) stage III disease, received a significant overall survival benefit from bevacizumab administration with an HR of 0.78 (0.63–0.97). The analysis suggested that residual tumor burden, presumably producing VEGF, is necessary to enable bevacizumab to exert an effect on the tumor microenvironment.

Poly (ADP-ribose) polymerase (PARP) inhibitors represent a major therapeutic advance. Maintenance therapy with olaparib or niraparib, with or without bevacizumab, substantially prolongs progression-free and overall survival in patients with germline BRCA1/2 mutations or homologous recombination deficiency (HRD) 104–106. In contrast, benefits in non-BRCA and HRD-proficient patients remain limited. Results from immunotherapy trials have been largely disappointing107, which can be attributed to tumor heterogeneity as well as inherent or acquired immunotherapy resistance associated with the tumor microenvironment. However, recent combination regimens have shown promise. The DUO-O trial demonstrated that non-BRCA-mutated patients receiving chemotherapy, bevacizumab, durvalumab, and olaparib achieved a median progression-free survival (PFS) of 24.2 months (HR, 0.63 vs. control)108. The KEYLYNK-001 trial reported an improved PFS (22.2 months; HR, 0.71) with chemotherapy plus pembrolizumab followed by olaparib maintenance109. Further biomarker development is required to enable more personalized therapeutic strategies.

Conclusions and future perspectives

The interpretation of global epidemiology is subject to heterogeneity in the methodologies and quality of the underlying cancer registries. Although the incidence of ovarian cancer has declined in some high-income countries, an upward trend persists in other countries. Globally, mortality has decreased, but despite advances in targeted therapy and other modalities, improvements in overall survival remain limited. Further exploration of underlying mechanisms is warranted. Modifying lifestyle and reproductive factors offers a feasible approach to reduce ovarian cancer risk. Women should be educated on the impact of reproductive decisions, physical activity, and diet on their future cancer risk. RRSO remains the most effective preventive measure for high-risk women. Public health policies should focus on promoting awareness, managing modifiable risk factors, and facilitating access to preventive surgery.

Future epidemiologic research should explore novel determinants to refine the understanding of ovarian cancer etiology. Large-scale multicenter cohort studies are needed to evaluate emerging factors, such as metabolomic signatures, gut microbiota composition, and chronic inflammatory states. The next frontier lies in the development of improved early detection strategies, in which AI and molecular biomarkers are potentially poised to play a role.

Conflict of interest statement

No potential conflicts of interest are disclosed.

Author contributions

Conceived and designed the analysis: Hongmei Zeng, Bin Li.

Collected the data: Ruyuan Li, Anqi Zhao, Meicen Liu.

Contributed data or analysis tools: Ruyuan Li, Lingeng Lu, Hongmei Zeng.

Performed the analysis: Ruyuan Li.

Wrote the paper: Ruyuan Li, Anqi Zhao.

Acknowledgments

We thank the epidemiologists, researchers, and clinicians for their efforts to help ovarian cancer patients.

  • Received October 9, 2025.
  • Accepted December 24, 2025.

References

  1. 1.↵
    2. Ferlay J,
    3. Ervik M,
    4. Lam F,
    5. Colombet M,
    6. Mery L,
    7. Piñeros M, et al.
    Global cancer observatory: cancer today. Lyon: International Agency for Research on Cancer; 2024.
  2. 2.↵
    2. Ferlay J,
    3. Laversanne M,
    4. Ervik M,
    5. Lam F,
    6. Colombet M,
    7. Mery L, et al.
    Global cancer observatory: cancer tomorrow. Lyon: International Agency for Research on Cancer; 2024.
  3. 3.↵
    2. Siegel RL,
    3. Kratzer TB,
    4. Giaquinto AN,
    5. Sung H,
    6. Jemal A.
    Cancer statistics, 2025. CA Cancer J Clin. 2025; 75: 10–45.
    OpenUrlCrossRefPubMed
  4. 4.↵
    2. Allemani C,
    3. Matsuda T,
    4. Di Carlo V,
    5. Harewood R,
    6. Matz M,
    7. Nikšić M, et al.
    Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018; 391: 1023–75.
    OpenUrlCrossRefPubMed
  5. 5.↵
    2. Kordowitzki P,
    3. Lange B,
    4. Elias KM,
    5. Haigis MC,
    6. Mechsner S,
    7. Braicu IE, et al.
    Transforming treatment paradigms: focus on personalized medicine for high-grade serous ovarian cancer. CA Cancer J Clin. 2025; 75: 436–60.
    OpenUrlPubMed
  6. 6.↵
    2. Lheureux S,
    3. Braunstein M,
    4. Oza AM.
    Epithelial ovarian cancer: evolution of management in the era of precision medicine. CA Cancer J Clin. 2019; 69: 280–304.
    OpenUrlCrossRefPubMed
  7. 7.↵
    2. Torre LA,
    3. Trabert B,
    4. DeSantis CE,
    5. Miller KD,
    6. Samimi G,
    7. Runowicz CD, et al.
    Ovarian cancer statistics, 2018. CA Cancer J Clin. 2018; 68: 284–96.
    OpenUrlCrossRefPubMed
  8. 8.↵
    2. Slomovitz B,
    3. Gourley C,
    4. Carey MS,
    5. Malpica A,
    6. Shih IM,
    7. Huntsman D, et al.
    Low-grade serous ovarian cancer: state of the science. Gynecol Oncol. 2020; 156: 715–25.
    OpenUrlCrossRefPubMed
  9. 9.↵
    2. Kurman RJ,
    3. Shih Ie-M.
    The dualistic model of ovarian carcinogenesis: revisited, revised, and expanded. Am J Pathol. 2016; 186: 733–47.
    OpenUrlCrossRefPubMed
  10. 10.↵
    2. Kurman RJ.
    Origin and molecular pathogenesis of ovarian high-grade serous carcinoma. Ann Oncol. 2013; 24: x16–21.
    OpenUrlCrossRefPubMed
  11. 11.↵
    2. Li T,
    3. Zhang H,
    4. Lian M,
    5. He Q,
    6. Lv M,
    7. Zhai L, et al.
    Global status and attributable risk factors of breast, cervical, ovarian, and uterine cancers from 1990 to 2021. J Hematol Oncol. 2025; 18: 5.
    OpenUrlPubMed
  12. 12.↵
    2. Webb PM,
    3. Jordan SJ.
    Global epidemiology of epithelial ovarian cancer. Nat Rev Clin Oncol. 2024; 21: 389–400.
    OpenUrlCrossRefPubMed
  13. 13.↵
    2. Cabasag CJ,
    3. Arnold M,
    4. Butler J,
    5. Inoue M,
    6. Trabert B,
    7. Webb PM, et al.
    The influence of birth cohort and calendar period on global trends in ovarian cancer incidence. Int J Cancer. 2020; 146: 749–58.
    OpenUrlCrossRefPubMed
  14. 14.↵
    United Nations, Department of Economic and Social Affairs, Population Division. Trends in contraceptive use worldwide 2015. New York: Department of Economic and Social Affairs, Population Division; 2015.
  15. 15.↵
    2. Han B,
    3. Zheng R,
    4. Zeng H,
    5. Wang S,
    6. Sun K,
    7. Chen R, et al.
    Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024; 4: 47–53.
    OpenUrlCrossRefPubMed
  16. 16.↵
    2. Allemani C,
    3. Weir HK,
    4. Carreira H,
    5. Harewood R,
    6. Spika D,
    7. Wang XS, et al.
    Global surveillance of cancer survival 1995-2009: analysis of individual data for 25,676,887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet. 2015; 385: 977–1010.
    OpenUrlCrossRefPubMed
  17. 17.↵
    2. Zeng H,
    3. Zheng R,
    4. Sun K,
    5. Zhou M,
    6. Wang S,
    7. Li L, et al.
    Cancer survival statistics in China 2019-2021: a multicenter, population-based study. J Natl Cancer Cent. 2024; 4: 203–13.
    OpenUrlPubMed
  18. 18.↵
    Surveillance Research Program, National Cancer Institute. SEER*Stat software (Version 8.4.5). 2025.
  19. 19.↵
    Institute for Health Metrics and Evaluation (IHME). GBD compare data visualization. Seattle, WA: IHME, University of Washington; 2024.
  20. 20.↵
    2. Gaitskell K,
    3. Green J,
    4. Pirie K,
    5. Barnes I,
    6. Hermon C,
    7. Reeves GK, et al.
    Histological subtypes of ovarian cancer associated with parity and breastfeeding in the prospective Million Women Study. Int J Cancer. 2018; 142: 281–9.
    OpenUrlCrossRefPubMed
  21. 21.↵
    2. Khodavandi A,
    3. Alizadeh F,
    4. Razis AFA.
    Association between dietary intake and risk of ovarian cancer: a systematic review and meta-analysis. Eur J Nutr. 2021; 60: 1707–36.
    OpenUrlPubMed
  22. 22.↵
    2. Zhan X,
    3. Wang J,
    4. Pan S,
    5. Lu C.
    Tea consumption and the risk of ovarian cancer: a meta-analysis of epidemiological studies. Oncotarget. 2017; 8: 37796–806.
    OpenUrlCrossRefPubMed
  23. 23.↵
    2. Yang J,
    3. Ma J,
    4. Jin Y,
    5. Cheng S,
    6. Huang S,
    7. Wang Y.
    Dietary inflammatory index and ovarian cancer risk: a meta-analysis. Nutr Cancer. 2022; 74: 796–805.
    OpenUrlPubMed
  24. 24.↵
    2. Olsen CM,
    3. Nagle CM,
    4. Whiteman DC,
    5. Ness R,
    6. Pearce CL,
    7. Pike MC, et al.
    Obesity and risk of ovarian cancer subtypes: evidence from the Ovarian Cancer Association Consortium. Endocr Relat Cancer. 2013; 20: 251–62.
    OpenUrlAbstract/FREE Full Text
  25. 25.↵
    2. Wang L,
    3. Zhong L,
    4. Xu B,
    5. Chen M,
    6. Huang H.
    Diabetes mellitus and the risk of ovarian cancer: a systematic review and meta-analysis of cohort and case-control studies. BMJ Open. 2020; 10: e040137.
  26. 26.↵
    2. Buras AL,
    3. Wang T,
    4. Whiting J,
    5. Townsend MK,
    6. Fridley BL,
    7. Tworoger SS.
    Prospective analyses of sedentary behavior in relation to risk of ovarian cancer. Am J Epidemiol. 2022; 191: 1021–9.
    OpenUrlPubMed
  27. 27.↵
    2. Roberts AL,
    3. Ratanatharathorn A,
    4. Chibnik L,
    5. Kubzansky LD,
    6. Tworoger SS.
    Multiple types of distress are prospectively associated with increased risk of ovarian cancer. Cancer Med. 2023; 12: 15404–13.
    OpenUrlPubMed
  28. 28.↵
    Collaborative Group on Epidemiological Studies of Ovarian Cancer. Ovarian cancer and oral contraceptives: collaborative reanalysis of data from 45 epidemiological studies including 23,257 women with ovarian cancer and 87,303 controls. Lancet. 2008; 371: 303–14.
    OpenUrlCrossRefPubMed
  29. 29.↵
    Collaborative Group On Epidemiological Studies Of Ovarian Cancer. Menopausal hormone use and ovarian cancer risk: individual participant meta-analysis of 52 epidemiological studies. Lancet. 2015; 385: 1835–42.
    OpenUrlCrossRefPubMed
  30. 30.↵
    2. Pearce CL,
    3. Templeman C,
    4. Rossing MA,
    5. Lee A,
    6. Near AM,
    7. Webb PM, et al.
    Association between endometriosis and risk of histological subtypes of ovarian cancer: a pooled analysis of case-control studies. Lancet Oncol. 2012; 13: 385–94.
    OpenUrlCrossRefPubMed
  31. 31.↵
    2. Rasmussen CB,
    3. Faber MT,
    4. Jensen A,
    5. Høgdall E,
    6. Høgdall C,
    7. Blaakær J, et al.
    Pelvic inflammatory disease and risk of invasive ovarian cancer and ovarian borderline tumors. Cancer Causes Control. 2013; 24: 1459–64.
    OpenUrlPubMed
  32. 32.↵
    2. Jonsson S,
    3. Jonsson H,
    4. Lundin E,
    5. Häggström C,
    6. Idahl A.
    Pelvic inflammatory disease and risk of borderline ovarian tumors: a national population-based case-control study in Sweden. Int J Cancer. 2025; 156: 529–37.
    OpenUrlPubMed
  33. 33.↵
    2. Falconer H,
    3. Yin L,
    4. Grönberg H,
    5. Altman D.
    Ovarian cancer risk after salpingectomy: a nationwide population-based study. J Natl Cancer Inst. 2015; 107: dju410.
  34. 34.
    2. Mavaddat N,
    3. Peock S,
    4. Frost D,
    5. Ellis S,
    6. Platte R,
    7. Fineberg E, et al.
    Cancer risks for BRCA1 and BRCA2 mutation carriers: results from prospective analysis of EMBRACE. J Natl Cancer Inst. 2013; 105: 812–22.
    OpenUrlCrossRefPubMed
  35. 35.↵
    2. Watson P,
    3. Vasen HFA,
    4. Mecklin JP,
    5. Bernstein I,
    6. Aarnio M,
    7. Järvinen HJ, et al.
    The risk of extra-colonic, extra-endometrial cancer in the Lynch syndrome. Int J Cancer. 2008; 123: 444–9.
    OpenUrlCrossRefPubMed
  36. 36.↵
    2. Camargo MC,
    3. Stayner LT,
    4. Straif K,
    5. Reina M,
    6. Al-Alem U,
    7. Demers PA, et al.
    Occupational exposure to asbestos and ovarian cancer: a meta-analysis. Environ Health Perspect. 2011; 119: 1211–7.
    OpenUrlCrossRefPubMed
  37. 37.↵
    2. Hinkula M,
    3. Pukkala E,
    4. Kyyrönen P,
    5. Kauppila A.
    Incidence of ovarian cancer of grand multiparous women – a population-based study in Finland. Gynecol Oncol. 2006; 103: 207–11.
    OpenUrlCrossRefPubMed
  38. 38.↵
    2. Husby A,
    3. Wohlfahrt J,
    4. Melbye M.
    Pregnancy duration and ovarian cancer risk: a 50-year nationwide cohort study. Int J Cancer. 2022; 151: 1717–25.
    OpenUrlPubMed
  39. 39.↵
    2. Babic A,
    3. Sasamoto N,
    4. Rosner BA,
    5. Tworoger SS,
    6. Jordan SJ,
    7. Risch HA, et al.
    Association between breastfeeding and ovarian cancer risk. JAMA Oncol. 2020; 6: e200421.
  40. 40.↵
    2. Xie Y,
    3. Liu F,
    4. Wu Y,
    5. Zhu Y,
    6. Jiang Y,
    7. Wu Q, et al.
    Inflammation in cancer: therapeutic opportunities from new insights. Mol Cancer. 2025; 24: 51.
    OpenUrlPubMed
  41. 41.↵
    2. Tang L,
    3. Pham NM,
    4. Lee AH,
    5. Shivappa N,
    6. Hébert JR,
    7. Zhao J, et al.
    Dietary inflammatory index and epithelial ovarian cancer in Southern Chinese women: a case-control study. Cancer Control. 2020; 27: 1073274820977203.
  42. 42.↵
    2. Shivappa N,
    3. Hébert JR,
    4. Rosato V,
    5. Rossi M,
    6. Montella M,
    7. Serraino D, et al.
    Dietary inflammatory index and ovarian cancer risk in a large Italian case-control study. Cancer Causes Control. 2016; 27: 897–906.
    OpenUrlCrossRefPubMed
  43. 43.↵
    2. Esposito G,
    3. Turati F,
    4. Parazzini F,
    5. Augustin LSA,
    6. Serraino D,
    7. Negri E, et al.
    Diabetes risk reduction diet and ovarian cancer risk: an Italian case-control study. Cancer Causes Control. 2023; 34: 769–76.
    OpenUrlPubMed
  44. 44.↵
    2. Han L,
    3. Xu S,
    4. Zhou D,
    5. Chen R,
    6. Ding Y,
    7. Zhang M, et al.
    Unveiling the causal link between metabolic factors and ovarian cancer risk using Mendelian randomization analysis. Front Endocrinol (Lausanne). 2024; 15: 1401648.
  45. 45.↵
    2. Whelan E,
    3. Kalliala I,
    4. Semertzidou A,
    5. Raglan O,
    6. Bowden S,
    7. Kechagias K, et al.
    Risk factors for ovarian cancer: an umbrella review of the literature. Cancers (Basel). 2022; 14: 2708.
    OpenUrlPubMed
  46. 46.↵
    2. Khanlarkhani N,
    3. Azizi E,
    4. Amidi F,
    5. Khodarahmian M,
    6. Salehi E,
    7. Pazhohan A, et al.
    Metabolic risk factors of ovarian cancer: a review. JBRA Assist Reprod. 2022; 26: 335–47.
    OpenUrlPubMed
  47. 47.↵
    2. Avgerinos KI,
    3. Spyrou N,
    4. Mantzoros CS,
    5. Dalamaga M.
    Obesity and cancer risk: emerging biological mechanisms and perspectives. Metabolism. 2019; 92: 121–35.
    OpenUrlCrossRefPubMed
  48. 48.↵
    2. Yarmolinsky J,
    3. Relton CL,
    4. Lophatananon A,
    5. Muir K,
    6. Menon U,
    7. Gentry-Maharaj A, et al.
    Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: a Mendelian randomization analysis. PLoS Med. 2019; 16: e1002893.
  49. 49.↵
    2. Zhang D,
    3. Li N,
    4. Xi Y,
    5. Zhao Y,
    6. Wang T.
    Diabetes mellitus and risk of ovarian cancer. A systematic review and meta-analysis of 15 cohort studies. Diabetes Res Clin Pract. 2017; 130: 43–52.
    OpenUrlPubMed
  50. 50.↵
    2. Biller VS,
    3. Leitzmann MF,
    4. Sedlmeier AM,
    5. Berger FF,
    6. Ortmann O,
    7. Jochem C.
    Sedentary behaviour in relation to ovarian cancer risk: a systematic review and meta-analysis. Eur J Epidemiol. 2021; 36: 769–80.
    OpenUrlCrossRefPubMed
  51. 51.↵
    2. Cannioto RA,
    3. Moysich KB.
    Epithelial ovarian cancer and recreational physical activity: a review of the epidemiological literature and implications for exercise prescription. Gynecol Oncol. 2015; 137: 559–73.
    OpenUrlCrossRefPubMed
  52. 52.↵
    2. Roberts AL,
    3. Huang T,
    4. Koenen KC,
    5. Kim Y,
    6. Kubzansky LD,
    7. Tworoger SS.
    Posttraumatic stress disorder is associated with increased risk of ovarian cancer: a prospective and retrospective longitudinal cohort study. Cancer Res. 2019; 79: 5113–20.
    OpenUrlCrossRefPubMed
  53. 53.↵
    2. McHale MT.
    New insights in endometriosis subtypes and ovarian cancer risk. JAMA. 2024; 332: 460–1.
    OpenUrlPubMed
  54. 54.↵
    2. Lacey JV Jr.,
    3. Mink PJ,
    4. Lubin JH,
    5. Sherman ME,
    6. Troisi R,
    7. Hartge P, et al.
    Menopausal hormone replacement therapy and risk of ovarian cancer. JAMA. 2002; 288: 334–41.
    OpenUrlCrossRefPubMed
  55. 55.↵
    2. Xiang H,
    3. Wang L,
    4. Sun L,
    5. Xu S.
    The risk of ovarian cancer in hormone replacement therapy users: a systematic review and meta-analysis. Front Endocrinol (Lausanne). 2024; 15: 1414968.
  56. 56.↵
    2. Lee AW,
    3. Templeman C,
    4. Stram DA,
    5. Beesley J,
    6. Tyrer J,
    7. Berchuck A, et al.
    Evidence of a genetic link between endometriosis and ovarian cancer. Fertil Steril. 2016; 105: 35–43.e1-10.
    OpenUrlCrossRefPubMed
  57. 57.↵
    2. Werger M,
    3. Ardestani SK,
    4. Sobel M.
    Opportunistic salpingectomy to decrease the risk of ovarian cancer. CMAJ. 2024; 196: E765.
    OpenUrlFREE Full Text
  58. 58.↵
    2. Taylor JA,
    3. Burnell M,
    4. Ryan A,
    5. Karpinskyj C,
    6. Kalsi JK,
    7. Taylor H, et al.
    Association of hysterectomy and invasive epithelial ovarian and tubal cancer: a cohort study within UKCTOCS. BJOG. 2022; 129: 110–8.
    OpenUrlPubMed
  59. 59.↵
    2. Huo X,
    3. Yao L,
    4. Han X,
    5. Li W,
    6. Liu J,
    7. Zhou L, et al.
    Hysterectomy and risk of ovarian cancer: a systematic review and meta-analysis. Arch Gynecol Obstet. 2019; 299: 599–607.
    OpenUrlCrossRefPubMed
  60. 60.↵
    2. Jordan SJ,
    3. Nagle CM,
    4. Coory MD,
    5. Maresco D,
    6. Protani MM,
    7. Pandeya NA, et al.
    Has the association between hysterectomy and ovarian cancer changed over time? A systematic review and meta-analysis. Eur J Cancer. 2013; 49: 3638–47.
    OpenUrlCrossRefPubMed
  61. 61.↵
    2. Lynch HT,
    3. Snyder C,
    4. Casey MJ.
    Hereditary ovarian and breast cancer: what have we learned? Ann Oncol. 2013; 24: viii83–viii95.
    OpenUrlCrossRefPubMed
  62. 62.↵
    2. Schrijver LH,
    3. Antoniou AC,
    4. Olsson H,
    5. Mooij TM,
    6. Roos-Blom MJ,
    7. Azarang L, et al.
    Oral contraceptive use and ovarian cancer risk for BRCA1/2 mutation carriers: an international cohort study. Am J Obstet Gynecol. 2021; 225: 51.e1–51.e17.
    OpenUrlCrossRefPubMed
  63. 63.↵
    2. Shaw PA,
    3. McLaughlin JR,
    4. Zweemer RP,
    5. Narod SA,
    6. Risch H,
    7. Verheijen RH, et al.
    Histopathologic features of genetically determined ovarian cancer. Int J Gynecol Pathol. 2002; 21: 407–11.
    OpenUrlCrossRefPubMed
  64. 64.↵
    2. Vo C,
    3. Carney ME.
    Ovarian cancer hormonal and environmental risk effect. Obstet Gynecol Clin North Am. 2007; 34: 687–700, viii.
    OpenUrlCrossRefPubMed
  65. 65.↵
    2. Narod SA.
    Talc and ovarian cancer. Gynecol Oncol. 2016; 141: 410–2.
    OpenUrlPubMed
  66. 66.
    2. Goodman JE,
    3. Kerper LE,
    4. Prueitt RL,
    5. Marsh CM.
    A critical review of talc and ovarian cancer. J Toxicol Environ Health B Crit Rev. 2020; 23: 183–213.
    OpenUrlPubMed
  67. 67.↵
    2. O’Brien KM,
    3. Tworoger SS,
    4. Harris HR,
    5. Anderson GL,
    6. Weinberg CR,
    7. Trabert B, et al.
    Association of powder use in the genital area with risk of ovarian cancer. JAMA. 2020; 323: 49–59.
    OpenUrlCrossRefPubMed
  68. 68.↵
    2. Mei S,
    3. Chelmow D,
    4. Gecsi K,
    5. Barkley J,
    6. Barrows E,
    7. Brooks R, et al.
    Health disparities in ovarian cancer: report from the ovarian cancer evidence review conference. Obstet Gynecol. 2023; 142: 196–210.
    OpenUrlPubMed
  69. 69.↵
    2. Peterson CE,
    3. Rauscher GH,
    4. Johnson TP,
    5. Kirschner CV,
    6. Barrett RE,
    7. Kim S, et al.
    The association between neighborhood socioeconomic status and ovarian cancer tumor characteristics. Cancer Causes Control. 2014; 25: 633–7.
    OpenUrlCrossRefPubMed
  70. 70.↵
    2. Gardy J,
    3. Dejardin O,
    4. Thobie A,
    5. Eid Y,
    6. Guizard AV,
    7. Launoy G.
    Impact of socioeconomic status on survival in patients with ovarian cancer. Int J Gynecol Cancer. 2019; 29: 792–801.
    OpenUrlAbstract/FREE Full Text
  71. 71.↵
    2. Karanth S,
    3. Fowler ME,
    4. Mao X,
    5. Wilson LE,
    6. Huang B,
    7. Pisu M, et al.
    Race, socioeconomic status, and health-care access disparities in ovarian cancer treatment and mortality: systematic review and meta-analysis. JNCI Cancer Spectr. 2019; 3: pkz084.
  72. 72.↵
    2. Sideris M,
    3. Menon U,
    4. Manchanda R.
    Screening and prevention of ovarian cancer. Med J Aust. 2024; 220: 264–74.
    OpenUrlPubMed
  73. 73.↵
    2. Jervis S,
    3. Song H,
    4. Lee A,
    5. Dicks E,
    6. Tyrer J,
    7. Harrington P, et al.
    Ovarian cancer familial relative risks by tumour subtypes and by known ovarian cancer genetic susceptibility variants. J Med Genet. 2014; 51: 108–13.
    OpenUrlAbstract/FREE Full Text
  74. 74.↵
    2. Buys SS,
    3. Partridge E,
    4. Black A,
    5. Johnson CC,
    6. Lamerato L,
    7. Isaacs C, et al.
    Effect of screening on ovarian cancer mortality: the prostate, lung, colorectal and ovarian (PLCO) cancer screening randomized controlled trial. JAMA. 2011; 305: 2295–303.
    OpenUrlCrossRefPubMed
  75. 75.↵
    2. Menon U,
    3. Gentry-Maharaj A,
    4. Burnell M,
    5. Singh N,
    6. Ryan A,
    7. Karpinskyj C, et al.
    Ovarian cancer population screening and mortality after long-term follow-up in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial. Lancet. 2021; 397: 2182–93.
    OpenUrlCrossRefPubMed
  76. 76.↵
    2. Sessa C,
    3. Balmaña J,
    4. Bober SL,
    5. Cardoso MJ,
    6. Colombo N,
    7. Curigliano G, et al.
    Risk reduction and screening of cancer in hereditary breast-ovarian cancer syndromes: ESMO Clinical Practice Guideline. Ann Oncol. 2023; 34: 33–47.
    OpenUrlCrossRefPubMed
  77. 77.↵
    2. Daly MB,
    3. Pal T,
    4. Berry MP,
    5. Buys SS,
    6. Dickson P,
    7. Domchek SM, et al.
    Genetic/familial high-risk assessment: breast, ovarian, and pancreatic, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2021; 19: 77–102.
    OpenUrlCrossRefPubMed
  78. 78.↵
    2. Cai G,
    3. Huang F,
    4. Gao Y,
    5. Li X,
    6. Chi J,
    7. Xie J, et al.
    Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study. Lancet Digit Health. 2024; 6: e176–e86.
    OpenUrl
  79. 79.↵
    2. Christiansen F,
    3. Konuk E,
    4. Ganeshan AR,
    5. Welch R,
    6. Palés Huix J,
    7. Czekierdowski A, et al.
    International multicenter validation of AI-driven ultrasound detection of ovarian cancer. Nat Med. 2025; 31: 189–96.
    OpenUrlPubMed
  80. 80.↵
    2. Barrett JE,
    3. Jones A,
    4. Evans I,
    5. Reisel D,
    6. Herzog C,
    7. Chindera K, et al.
    The DNA methylome of cervical cells can predict the presence of ovarian cancer. Nat Commun. 2022; 13: 448.
    OpenUrlCrossRefPubMed
  81. 81.↵
    2. Wang Y,
    3. Li L,
    4. Douville C,
    5. Cohen JD,
    6. Yen TT,
    7. Kinde I, et al.
    Evaluation of liquid from the Papanicolaou test and other liquid biopsies for the detection of endometrial and ovarian cancers. Sci Transl Med. 2018; 10: eaap8793.
  82. 82.↵
    2. Chen K,
    3. Gentry-Maharaj A,
    4. Burnell M,
    5. Steentoft C,
    6. Marcos-Silva L,
    7. Mandel U, et al.
    Microarray Glycoprofiling of CA125 improves differential diagnosis of ovarian cancer. J Proteome Res. 2013; 12: 1408–18.
    OpenUrlCrossRefPubMed
  83. 83.↵
    2. Atallah GA,
    3. Abd Aziz NH,
    4. Teik CK,
    5. Shafiee MN,
    6. Kampan NC.
    New predictive biomarkers for ovarian cancer. Diagnostics (Basel). 2021; 11: 465.
    OpenUrlPubMed
  84. 84.↵
    2. Klein EA,
    3. Richards D,
    4. Cohn A,
    5. Tummala M,
    6. Lapham R,
    7. Cosgrove D, et al.
    Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set. Ann Oncol. 2021; 32: 1167–77.
    OpenUrlCrossRefPubMed
  85. 85.↵
    2. Havrilesky LJ,
    3. Moorman PG,
    4. Lowery WJ,
    5. Gierisch JM,
    6. Coeytaux RR,
    7. Urrutia RP, et al.
    Oral contraceptive pills as primary prevention for ovarian cancer: a systematic review and meta-analysis. Obstet Gynecol. 2013; 122: 139–47.
    OpenUrlCrossRefPubMed
  86. 86.↵
    2. Walker JL,
    3. Powell CB,
    4. Chen LM,
    5. Carter J,
    6. Bae Jump VL,
    7. Parker LP, et al.
    Society of Gynecologic Oncology recommendations for the prevention of ovarian cancer. Cancer. 2015; 121: 2108–20.
    OpenUrlPubMed
  87. 87.↵
    2. Cibula D,
    3. Zikan M,
    4. Dusek L,
    5. Majek O.
    Oral contraceptives and risk of ovarian and breast cancers in BRCA mutation carriers: a meta-analysis. Expert Rev Anticancer Ther. 2011; 11: 1197–207.
    OpenUrlCrossRefPubMed
  88. 88.↵
    2. Peragallo Urrutia R,
    3. Coeytaux RR,
    4. McBroom AJ,
    5. Gierisch JM,
    6. Havrilesky LJ,
    7. Moorman PG, et al.
    Risk of acute thromboembolic events with oral contraceptive use: a systematic review and meta-analysis. Obstet Gynecol. 2013; 122: 380–9.
    OpenUrlCrossRefPubMed
  89. 89.↵
    2. Wheeler LJ,
    3. Desanto K,
    4. Teal SB,
    5. Sheeder J,
    6. Guntupalli SR.
    Intrauterine device use and ovarian cancer risk: a systematic review and meta-analysis. Obstet Gynecol. 2019; 134: 791–800.
    OpenUrlCrossRefPubMed
  90. 90.↵
    2. Balayla J,
    3. Gil Y,
    4. Lasry A,
    5. Mitric C.
    Ever-use of the intra-uterine device and the risk of ovarian cancer. J Obstet Gynaecol. 2021; 41: 848–53.
    OpenUrlPubMed
  91. 91.↵
    2. Kahn RM,
    3. Gordhandas S,
    4. Godwin K,
    5. Stone RL,
    6. Worley MJ Jr.,
    7. Lu KH, et al.
    Salpingectomy for the primary prevention of ovarian cancer: a systematic review. JAMA Surg. 2023; 158: 1204–11.
    OpenUrlCrossRefPubMed
  92. 92.↵
    2. Daly MB,
    3. Pal T,
    4. Maxwell KN,
    5. Churpek J,
    6. Kohlmann W,
    7. AlHilli Z, et al.
    NCCN Guidelines® insights: genetic/familial high-risk assessment: breast, ovarian, and pancreatic, Version 2.2024. J Natl Compr Canc Netw. 2023; 21: 1000–10.
    OpenUrlCrossRefPubMed
  93. 93.↵
    The 2022 hormone therapy position statement of The North American Menopause Society. Menopause. 2022; 29: 767–94.
    OpenUrlCrossRefPubMed
  94. 94.↵
    2. Manchanda R,
    3. Gaba F,
    4. Talaulikar V,
    5. Pundir J,
    6. Gessler S,
    7. Davies M, et al.
    Risk-reducing salpingo-oophorectomy and the use of hormone replacement therapy below the age of natural menopause. Scientific Impact Paper No. 66. October 2021. BJOG. 2022; 129: e16–e34.
    OpenUrlPubMed
  95. 95.↵
    2. Steenbeek MP,
    3. Harmsen MG,
    4. Hoogerbrugge N,
    5. de Jong MA,
    6. Maas AHEM,
    7. Prins JB, et al.
    Association of salpingectomy with delayed oophorectomy versus salpingo-oophorectomy with quality of life in BRCA1/2 pathogenic variant carriers: a nonrandomized controlled trial. JAMA Oncol. 2021; 7: 1203–12.
    OpenUrlPubMed
  96. 96.↵
    2. Lu KH,
    3. Nebgen DR,
    4. Norquist B,
    5. Bowen DJ,
    6. Bakkum-Gamez JN,
    7. Romero I, et al.
    WISP: a prospective, multi-center trial of salpingectomy with delayed oophorectomy versus risk reducing salpingo-oophorectomy in women at increased risk for hereditary ovarian cancer. Gynecol Oncol. 2019; 154: 22.
    OpenUrlPubMed
  97. 97.↵
    2. Steenbeek MP,
    3. van Bommel MHD,
    4. intHout J,
    5. Peterson CB,
    6. Simons M,
    7. Roes KCB, et al.
    TUBectomy with delayed oophorectomy as an alternative to risk-reducing salpingo-oophorectomy in high-risk women to assess the safety of prevention: the TUBA-WISP II study protocol. Int J Gynecol Cancer. 2023; 33: 982–7.
    OpenUrlAbstract/FREE Full Text
  98. 98.↵
    2. Walker M,
    3. Jacobson M,
    4. Sobel M.
    Management of ovarian cancer risk in women with BRCA1/2 pathogenic variants. CMAJ. 2019; 191: E886–93.
    OpenUrlFREE Full Text
  99. 99.↵
    2. Nezhat FR,
    3. Apostol R,
    4. Nezhat C,
    5. Pejovic T.
    New insights in the pathophysiology of ovarian cancer and implications for screening and prevention. Am J Obstet Gynecol. 2015; 213: 262–7.
    OpenUrlCrossRefPubMed
  100. 100.↵
    2. Harter P,
    3. Sehouli J,
    4. Vergote I,
    5. Ferron G,
    6. Reuss A,
    7. Meier W, et al.
    Randomized trial of cytoreductive surgery for relapsed ovarian cancer. N Engl J Med. 2021; 385: 2123–31.
    OpenUrlCrossRefPubMed
  101. 101.↵
    2. Jiang R,
    3. Feng Y,
    4. Chen Y,
    5. Cheng X,
    6. Shi T,
    7. Gao W, et al.
    Surgery versus no surgery in platinum-sensitive relapsed ovarian cancer: final overall survival analysis of the SOC-1 randomized phase 3 trial. Nat Med. 2024; 30: 2181–8.
    OpenUrlPubMed
  102. 102.↵
    2. Oza AM,
    3. Cook AD,
    4. Pfisterer J,
    5. Embleton A,
    6. Ledermann JA,
    7. Pujade-Lauraine E, et al.
    Standard chemotherapy with or without bevacizumab for women with newly diagnosed ovarian cancer (ICON7): overall survival results of a phase 3 randomised trial. Lancet Oncol. 2015; 16: 928–36.
    OpenUrlCrossRefPubMed
  103. 103.↵
    2. Tewari KS,
    3. Burger RA,
    4. Enserro D,
    5. Norquist BM,
    6. Swisher EM,
    7. Brady MF, et al.
    Final overall survival of a randomized trial of bevacizumab for primary treatment of ovarian cancer. J Clin Oncol. 2019; 37: 2317–28.
    OpenUrlCrossRefPubMed
  104. 104.↵
    2. DiSilvestro P,
    3. Banerjee S,
    4. Colombo N,
    5. Scambia G,
    6. Kim BG,
    7. Oaknin A, et al.
    Overall survival with maintenance olaparib at a 7-year follow-up in patients with newly diagnosed advanced ovarian cancer and a BRCA mutation: the SOLO1/GOG 3004 trial. J Clin Oncol. 2023; 41: 609–17.
    OpenUrlCrossRefPubMed
  105. 105.
    2. Ray-Coquard I,
    3. Leary A,
    4. Pignata S,
    5. Cropet C,
    6. González-Martín A,
    7. Marth C, et al.
    Olaparib plus bevacizumab first-line maintenance in ovarian cancer: final overall survival results from the PAOLA-1/ENGOT-ov25 trial. Ann Oncol. 2023; 34: 681–92.
    OpenUrlCrossRefPubMed
  106. 106.↵
    2. Hardesty MM,
    3. Krivak TC,
    4. Wright GS,
    5. Hamilton E,
    6. Fleming EL,
    7. Belotte J, et al.
    A phase 2 trial of niraparib plus bevacizumab maintenance therapy following first-line platinum-based chemotherapy with bevacizumab in advanced ovarian cancer: final analysis and overall survival results from OVARIO. Gynecol Oncol. 2025; 199: 96–102.
    OpenUrlPubMed
  107. 107.↵
    2. Ghisoni E,
    3. Morotti M,
    4. Sarivalasis A,
    5. Grimm AJ,
    6. Kandalaft L,
    7. Laniti DD, et al.
    Immunotherapy for ovarian cancer: towards a tailored immunophenotype-based approach. Nat Rev Clin Oncol. 2024; 21: 801–17.
    OpenUrlCrossRefPubMed
  108. 108.↵
    2. Harter P,
    3. Wimberger P,
    4. Okamoto A,
    5. Reuss A,
    6. Lim MC,
    7. Rubio-Pérez MJ, et al.
    Durvalumab plus paclitaxel/carboplatin plus bevacizumab followed by durvalumab, bevacizumab plus olaparib maintenance among patients with newly-diagnosed advanced ovarian cancer without a tumor BRCA1/BRCA2 mutation: updated results from DUO-O/ENGOT-OV46/GOG-3025 trail. Gynecol Oncol. 2024; 190: S65–6.
    OpenUrl
  109. 109.↵
    2. Vergote I,
    3. Sehouli J,
    4. Salutari V,
    5. Zola P,
    6. Madry R,
    7. Wenham RM, et al.
    ENGOT-OV43/KEYLYNK-001: a phase III, randomized, double-blind, active- and placebo-controlled study of pembrolizumab plus chemotherapy with olaparib maintenance for first-line treatment of BRCA-nonmutated advanced epithelial ovarian cancer. J Clin Oncol. 2019; 37: TPS5603.
Back to top

In this issue

Print
Download PDF
Email Article
Citation Tools

Jump to section

Keywords