4.3 Article

Elevated serum progesterone levels in postmenopausal women with mucinous ovarian tumors

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Publisher

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/GME.0000000000000587

Keywords

Estrogen; Menopause; Mucinous ovarian tumor; Progesterone

Funding

  1. Grants-in-Aid for Scientific Research [26462475] Funding Source: KAKEN

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Objective: The aim of the study was to evaluate the association between tumor histology and serum sex hormone levels in postmenopausal women with ovarian tumors. Methods: We preoperatively measured serum levels of gonadotropins and sex steroids, including estradiol, progesterone, and testosterone, in 69 postmenopausal women who underwent surgical resection for ovarian tumors. Tumors were classified as surface epithelial-stromal tumors, sex cord-stromal tumors, germ cell tumors, and metastatic tumors. Surface epithelial-stromal tumors were divided into mucinous, serous, clear cell, and endometrioid tumor subgroups. Patients were divided into two groups depending on tumor type: mucinous and non-mucinous, and any association between these tumor types and serum sex hormone levels were evaluated. Results: Univariate analyses revealed that serum sex steroid levels were significantly higher in women with mucinous ovarian tumors compared with women with other tumor types. Serum gonadotropin levels, age, body mass index, tumor size, and tumor malignancy status did not affect the sex steroid levels. Multivariate analysis evaluating sex steroid levels and tumor histology revealed that high serum progesterone levels were significantly and independently associated with mucinous ovarian tumors. A serum progesterone cut-off level of at least 1.3 nmol/L was the most accurate for differentiating mucinous tumors from other tumor types (area under the curve, 0.81; sensitivity, 75%; and specificity, 86%). Conclusions: Serum progesterone levels were significantly elevated in postmenopausal women with mucinous ovarian tumors. In these women, serum progesterone levels may thus represent a useful biomarker for predicting tumor histology preoperatively, which would aid treatment planning.

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