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Advanced epithelial ovarian cancer (aEOC), particularly High-Grade Serous Carcinoma (HGSC), is a lethal malignancy with high recurrence rates and chemoresistance. A critical gap exists in understanding how surgical trauma and the malignant ascites microenvironment drive disease progression and treatment failure. This project aims to develop an innovative, AI-driven predictive model by integrating multi-dimensional data to improve patient outcomes. We will use a unique biobank of paired tumor, ascites, and peritoneal samples, collected before and after surgery, for comprehensive multi-omic characterization (genomics, proteomics, scRNA-seq) to identify the molecular impact of surgical stress. The functional impact of ascites on cancer cell behavior will be investigated using advanced 3D in vitro models and a biomimetic "multi-organ on a chip" ex vivo system to model metastasis. Finally, all high-dimensional datasets will be integrated into a novel, multi-modal AI model, creating a powerful predictive tool. This will enable superior patient stratification and guide personalized surgical and therapeutic management, with the goal of improving clinical outcomes for women with aEOC.