SURVIVAL OUTCOMES AND CLINICAL PROGNOSTIC FACTORS IN OVARIAN CANCER: A CONTEMPORARY REVIEW AND DATA-DRIVEN ANALYSIS
Abstract
Ovarian cancer continues to be among the most aggressive types of gynaecological cancers because of late diagnosis, high recurrence rate, and wide disparities in patient survival outcomes. With improvements in biomedical analytics and computational oncology, there are emerging possibilities for better prognostic assessment and cancer treatment tailored to individual patients. This paper seeks to assess the survival outcomes and key clinical prognostic indicators of ovarian cancer through an extensive literature review and analytical study. A retrospective analysis approach was adopted using
clinical data of ovarian cancer freely available in public databases to analyse survival patterns and clinical prognostic indicators. Statistical survival analysis methods such as Kaplan-Meier survival analysis and Cox proportional hazard regression models were employed to estimate the impact of clinical features on patients' survival outcomes. Results from the analysis showed that tumour staging, age, performance status, and treatment factors were significantly correlated with survival rates amongst ovarian cancer patients. Late-stage cancer and poor performance status were predictors of
high mortality rates and low chances of survival. Data analysis techniques enhanced the ability to detect predictive trends and enabled more reliable survival predictions. Additionally, recent developments in computational oncology and machine learning have helped advance the process of predicting the outcome of a cancer case. In conclusion, the application of survival analysis alongside biomedical analytics and computational oncology models holds considerable promise in enhancing the prediction of ovarian cancer outcomes.
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