Can the Combination of Magnetic Resonance Imaging, Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio Predict the Mass Origin in Ovarian Masses?

磁共振成像、中性粒细胞与淋巴细胞比率和血小板与淋巴细胞比率的组合能否预测卵巢肿块的来源?

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Abstract

OBJECTIVE: Evaluate the effectiveness of magnetic resonance imaging (MRI), blood parameters, and tumor markers to determine the role of objective criteria in distinguishing malignant, borderline, and benign masses and to minimize unnecessary surgical interventions by reducing interpretation differences. METHODS: The histopathological and clinical-laboratory results of the patients who underwent surgery for the initial diagnosis and whose ovarian masses were confirmed were retrospectively reviewed. Between groups, age, cancer antigen 125, mean platelet volume (MPV), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), the presence of ascites, the ovarian-adnexal reporting and data system MRI scores, mass characteristics, and lymphocyte count were compared. RESULTS: The study comprised a total of 191 patients. These patients were categorized into three groups: Benign (n=113), borderline (n=26), and malignant (n=52). No noteworthy correlation was detected between the unilocular or multilocular nature of solid, cystic, or mixed masses and the rates of NLR, PLR, or MPV. However, a notable correlation was identified between NLR and the presence of acidity (p=0.003). In ovarian cancer patients, there was no significant difference in NLR and MPV between malignant epithelial and malignant sex cord-stromal types (p>0.05), whereas a significant difference emerged in the PLR ratio (p=0.013). CONCLUSION: In ovarian masses with malignant potential, laboratory parameters such as NLR and PLR can guide the diagnosis process. In the future, various studies such as the development of different tests, markers, and imaging methods, the use of blood tests such as NLR, PLR, and MPV in cancer diagnosis will be possible. The results of these studies may contribute to the development of new methods for the diagnosis of ovarian cancer and the improvement of treatment protocols.

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