Stepwise imaging clinical model for patients with ovarian cancer

Abstract

This article is the result of research conducted as part of a Master's degree in Comprehensive Care for Cancer Patients, given that ovarian cancer is a major global health problem, representing the seventh most common cancer and the eighth leading cause of cancer death in women worldwide. The objective is to describe the design of a stepwise clinical-imaging model for the diagnosis and staging of ovarian cancer at the Dr. Ernesto Guevara de la Serna General Teaching Hospital. Theoretical methods were used: theoretical systematization and modeling, and empirical methods: observation and documentary analysis. The main results of the model's application are aimed at achieving indicators of validity and reliability of the algorithm for predicting malignant disease and optimal resectability, evaluating the clinical viability of the model in real practice, and reducing diagnostic costs by 30%. In conclusion, the model provides a validated, cost-effective, and exportable tool for improving the management of ovarian cancer in similar settings and provides a clear, consensus-based, and contextualized protocol that reduces uncertainty in initial decision-making, as well as providing patients with more accurate staging and, consequently, initial treatment that is more appropriate to their condition, which could potentially impact prognosis.