Work

The Compatibility of Pathological Findings of Ovarian Cysts with Preoperative Ultrasound Findings

Year: 2025

Type: article

Abstract: Background & Objective: Ovarian cysts are common findings in gynecological practice and are often detected incidentally through imaging, particularly ultrasound. This study was conducted with aim to evaluate the compatibility between preoperative ultrasound findings and final pathological diagnoses of ovarian.Materials & Methods: This study was conducted on the medical records of 100 patients who underwent surgical intervention for ovarian cysts in 2020 at Forghani Hospital, Qom, Iran. Variables included patient demographics, cyst characteristics (size, morphology, solid components, septations, Doppler vascularity), tumor markers (AFP, LDH, CA125, CEA, HE4, CA19.9), ultrasound impressions (benign vs. suspicious), and histopathological diagnoses. Statistical analysis was performed using SPSS software (version 26), and agreement between ultrasound and histopathology was assessed via the Kappa statistic and categorical variables compared using the Chi-square test. P<0.05 was considered significant. Results: Pathological diagnoses revealed 94% of cysts as benign lesions (28% functional cysts, 30% serous cysts, 18% dermoids, 18% endometriomas) and 6% malignant lesions. Ultrasound classified 45% of cysts as "benign" and 55% as "suspicious." A significant correlation was found between ultrasound impressions and pathology diagnosis (P<0.001). For malignancy detection, ultrasound demonstrated moderate sensitivity (50%), low specificity (44.7%), low positive predictive value (PPV: 5.5%), and high negative predictive value (NPV: 93.3%). Tumor markers showed elevated CA125 (42%) and HE4 (39%) in benign cases, highlighting limited specificity. Conclusion: Ultrasound effectively excludes benign ovarian cysts (high NPV) but poorly detects malignancy (low PPV). Integrated diagnostic approaches combining standardized systems, tumor markers, and advanced imaging are essential to improve risk assessment and avoid overtreatment.

Source: Journal of Obstetrics Gynecology and Cancer Research

DOI: 10.24200/jogcr.11.2.84

Authors: Maryam Yousefi, Zahra Kamelian, Mohammad Hosein Arjmandnia, Enayatollah Noori, MohammadReza PourMahjoor

Institutions: Qom University of Medical Science and Health Services, Sharif University of Technology

Language: English

Topic: Ovarian cancer diagnosis and treatment

Subfield: Reproductive Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W7119009592