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Abstract

Uterine cancer, also known as endometrial cancer, is a prevalent malignancy affecting women worldwide. Timely and accurate diagnosis is crucial for effective management and improved patient outcomes. This abstract provides an overview of current diagnostic approaches and emerging trends in uterine cancer diagnosis. Traditional methods such as transvaginal ultrasound, endometrial biopsy, and dilation and curettage remain cornerstone techniques for initial evaluation. However, advancements in imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT), offer enhanced sensitivity and specificity in detecting uterine malignancies and assessing disease extent. Molecular profiling and biomarker analysis have also emerged as promising tools for risk stratification and personalized treatment strategies. Additionally, the integration of artificial intelligence (AI) algorithms into diagnostic algorithms holds potential for improving accuracy and efficiency in uterine cancer diagnosis. Despite these advancements, challenges persist, including the need for standardized protocols, access to advanced technologies, and addressing healthcare disparities. This abstract underscores the importance of multidisciplinary collaboration and ongoing research efforts to optimize uterine cancer diagnosis and ultimately improve patient outcomes.

Uterine cancer, predominantly endometrial carcinoma, represents a significant health concern among women worldwide. Early diagnosis is paramount for effective treatment and improved prognosis. This paper aims to provide an overview of current diagnostic methods for uterine cancer, highlighting their advantages, limitations, and future prospects. From traditional approaches such as transvaginal ultrasound and endometrial biopsy to advanced imaging techniques like magnetic resonance imaging (MRI) and novel biomarkers, a comprehensive evaluation of diagnostic modalities is presented. Additionally, emerging technologies such as liquid biopsy and artificial intelligence (AI) applications in image analysis are explored for their potential to enhance early detection and personalized treatment strategies. Understanding the landscape of uterine cancer diagnosis is crucial for clinicians to optimize patient care and outcomes.