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Objective: Peritoneal metastases (PM) are a challenge in gynecological cancers, but its appearance has never been described in a population-based study. Therefore, we describe the incidence of PM and identify predictors that increase the probability of peritoneal spread. Methods: All ovarian, endometrial and cervical cancer patients diagnosed in the Netherlands between 1989 and 2015 were identified from the Netherlands Cancer Registry and stratified for PM. Crude and age-adjusted incidence over time was calculated. Independent predictors for PM were identified using uni-and multivariable analyses. Results: The 94,981 patients were diagnosed with ovarian, endometrial or cervical cancer and respectively 61%, 2% and 1% presented with PM. Predictors for PM in ovarian cancer were: age between 50 and 74 years (odds ratio [OR]=1.19; 95% confidence interval [CI]=1.08-1.32), other distant metastases (OR=1.25; 95% CI=1.10-1.41), poor differentiation grade (OR=2.00; 95% CI=1.73-2.32) and serous histology. Predictors in endometrial cancer were lymph node metastases (OR=2.32; 95% CI=1.65-3.26), other distant metastases (OR=1.38; 95% CI=1.08-1.77), high-grade tumors (OR=1.95; 95% CI=1.38-2.76) and clear cell (OR=1.49; 95% CI=1.04-2.13) or serous histology (OR=2.71; 95% CI=2.15-3.42). In cervical cancer, the risk is higher in adenocarcinoma than in squamous cell carcinoma (OR=4.92; 95% CI=3.11-7.79). Conclusion: PM are frequently seen in patients with ovarian cancer. In endometrial and cervical cancer PM are rare. Histological subtype was the strongest predictive factor for PM in all 3 cancers. Better understanding of predictive factors for PM and thus the biological behavior is of paramount importance.

Risk of cervical, oesophageal and colon cancer in migrants mainly reflects the risks in their countries of origin. Almost similar cancer survival rates in migrants and native Dutch individuals points towards successful and comprehensive health care in The Netherlands. Primary cancer prevention should target high-risk groups and involve migration-sensitive approaches.

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