Urachal carcinoma (UC) is a rare and aggressive malignancy that arises from the urachus, a vestigial structure connecting the bladder to the umbilicus. Due to its infrequency and nonspecific symptoms, UC is often diagnosed at an advanced stage, leading to a poor prognosis. Prognostic models are essential in predicting patient outcomes, guiding treatment decisions, and optimizing clinical management. However, given the rarity of UC, there is a lack of consensus on standardized prognostic models.

This review aims to explore the existing prognostic models for UC, evaluate key prognostic factors, and discuss the potential role of molecular markers and advanced imaging techniques in refining these models.

A variety of prognostic factors have been identified in the literature, which include tumor size, stage at diagnosis, lymph node involvement, surgical margin status, and the presence of distant metastases. Early-stage disease with complete surgical resection is typically associated with better outcomes, while advanced disease with local invasion or metastasis significantly worsens prognosis. The role of histopathological factors, such as histological subtype (adenocarcinoma being the most common) and differentiation, also impacts survival rates. Despite these factors, the models remain fragmented, with no universally accepted system for prognosis. Emerging research indicates the potential of molecular markers, such as genetic mutations and protein expression profiles, in enhancing prognostic accuracy. Furthermore, imaging techniques such as PET and MRI could contribute to better staging and detection of micrometastases, ultimately influencing clinical decision-making.

The results highlight that while several clinical factors help assess prognosis, there's still no standardized model in use. New approaches involving molecular and imaging technologies may bridge this gap, leading to more personalized and effective care for UC patients.

Prognostic models for urachal carcinoma remain an area of ongoing research due to the rarity of the disease and the complexity of its clinical presentation. While several prognostic factors have been identified, no single model has proven universally effective in predicting outcomes. The incorporation of molecular markers and advanced imaging techniques holds promise for improving prognostication and tailoring individualized treatment strategies. Further studies and the development of large-scale, multicenter datasets are essential to establish reliable, standardized prognostic models for UC. As personalized medicine continues to evolve, these models will play a crucial role in enhancing survival rates and quality of life for UC patients.