An Intelligent Digital Twin Framework with Biomarker-Aware XGBoost for Ovarian Cancer Prediction

Abstract: Ovarian cancer is a major killer in women worldwide, and the rate of cancer deaths is especially low because of the lack of good tools to detect it in the early stages and the late diagnosis. Currently, the most used screening tests are CA-125 and transvaginal ultrasound which are not sufficiently precise to make an early diagnosis, so more intelligent prediction methods are needed. In this study, we have proposed a framework for the Digital Twin and XGBoost machine learning to make the personalized prediction of ovarian cancer risk and generation of biomarkers. The clinical data set consisted of 349 patients with 51 biomarkers; 10 were identified as key predictors by Recursive Feature Elimination, including CA125, HE4, ALB and CEA. The accuracy of the XGBoost model is 91.43%. Clinicians can use the Digital Twin component to estimate a patient’s current cancer risk and simulate the evolution of cancer risk over time, with the understanding of changes in biomarkers, to obtain interpretable clinical insights. The proposed framework performs well in terms of prediction and is a dynamic and personalized clinical decision support tool for ovarian cancer. .

Keywords: Ovarian cancer, XGBoost, Bio markers, Digital Twin.