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Abstract

Ovarian cancer remains one of the most lethal gynecological malignancies, largely due to its asymptomatic nature in early stages and the lack of effective early diagnostic tools. This study provides a comprehensive overview of the current diagnostic modalities for ovarian cancer, emphasizing the integration of novel biomarkers, imaging techniques, and computational models to enhance early detection and improve patient outcomes. Traditional diagnostic approaches, such as pelvic examinations, ultrasound, and serum CA-125 levels, are discussed, highlighting their limitations and the need for more sensitive and specific methods.

Emerging biomarkers, including HE4, mesothelin, and various genetic markers, offer promising avenues for early detection. Advances in imaging techniques, such as transvaginal ultrasound, magnetic resonance imaging (MRI), and positron emission tomography (PET), are also reviewed, focusing on their potential to detect earlystage ovarian tumors with greater accuracy. The role of computational models and machine learning algorithms in analyzing complex datasets to predict disease risk and diagnose ovarian cancer is explored, showcasing the potential of artificial intelligence in revolutionizing cancer diagnostics.

The study also examines the challenges and future directions in ovarian cancer diagnosis, including the need for large-scale validation of novel biomarkers, the integration of multi-omics data, and the development of costeffective, non-invasive diagnostic tests. By addressing these challenges, the field can move closer to achieving reliable early detection of ovarian cancer, ultimately improving survival rates and patient quality of life.

Ovarian cancer remains one of the most lethal gynecological malignancies, primarily due to its asymptomatic nature in the early stages and the lack of effective early screening methods. This paper provides a comprehensive overview of the current methodologies for diagnosing ovarian cancer, highlighting the advancements in imaging techniques, biomarker discovery, and genetic profiling. We discuss traditional diagnostic approaches, including transvaginal ultrasound and serum CA-125 levels, alongside emerging technologies such as liquid biopsies and machine learning algorithms. The integration of multi-omics data and artificial intelligence (AI) has shown promise in enhancing diagnostic accuracy and early detection rates. Despite these advancements, challenges such as high false-positive rates, the need for standardized protocols, and accessibility to advanced diagnostic tools persist. This review underscores the importance of continued research and collaboration among clinicians, researchers, and technologists to develop more reliable, cost-effective, and non-invasive diagnostic methods for ovarian cancer, ultimately improving patient outcomes and survival rates.