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Quantitation of Intra-peritoneal Ovarian Cancer Metastasis
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Risk of Ovarian Malignancy Algorithm and Pelvic Mass Score for the prediction of malignant ovarian tumors: a
Mohamed A Ali1, Mohamed S Sweed1, Eman A NasrElDin2
1Department of Obstetrics and Gynecology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Aim:
Ovarian cancer is the seventh most common female cancer worldwide. Nevertheless, there is no available universal screening method for malignant ovarian masses. This study compares the value of the Risk of Ovarian Malignancy Algorithm (ROMA) and Pelvic Mass Score (PMS) scoring systems in the diagnosis of malignant ovarian masses.
Material And Methods:
This prospective comparative study was conducted from March 2021 until April 2022. A total of 258 women diagnosed with ovarian mass and eligible for surgical intervention according to institutional guidelines were enrolled in the study. Ultrasound was performed for the assessment of masses, ascites and metastases, also color flow Doppler was done to measure the resistance index of the mass vasculature. Preoperative venous blood samples were collected to measure CA 125 and HE4. PMS and ROMA scoring systems were calculated for each patient. All women were subjected to a surgical intervention (according to applicable institutional guidelines), using either open or laparoscopic techniques. Histopathological examination of the removed specimens was done, and in line with the recognized gold standard, the results were compared with the pre-operative diagnosis of both scoring systems.
Results:
Both PMS and ROMA showed a high predictive probability for ovarian malignancies (AUC = 0.93, sensitivity = 83.3%, specificity = 90.37%; AUC = 0.91, sensitivity = 84.4%, specificity = 95.56%, respectively), yet no statistical significant difference was found between the two scoring systems (p = 0.353, 95% CI -0.025 to 0.070).
Conclusions:
Both PMS and ROMA seem to be promising scoring systems for discriminating benign from malignant ovarian masses, but more research is needed to determine the optimum diagnostic pathway, especially one yielding the least false-negative results.
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