AI for Ovarian Cancer Imaging

Programme

Scientific & Technological Cooperation (S&T Cooperation)

Project lead

Danube Private University (DPU, AT); Univ.-Prof. Priv.-Doz. Dr. Ramona Woitek, PhD

Project Partners

Western Balkans University, Tirana (WBU, AL)

Researchers involved at DPU 

• Univ.-Prof. Priv.-Doz. Dr. Julia Furtner, PhD, MBA
• Asst. Prof. Olgica Zaric, PhD
• Ali Haider, PhD
• Laura Atenea Villazan Garcia, BSc

Abstract

Ovarian cancer responds to neoadjuvant chemotherapy (NACT) in only about 50% of cases, and to date, no well-established non-invasive biomarkers exist to predict treatment response. Developing such biomarkers could significantly support clinical decision-making and enable more individualized treatment strategies.

The Research Center MIAAI at the DPU and the Western Balkans University (WBU) will jointly develop AI-based models to predict response to NACT using pre-treatment computed tomography (CT) and other clinical data. These models will integrate radiomic features extracted from imaging with clinical, demographic, and histopathological variables.

External validation is essential to ensure the generalizability and robustness of AI algorithms. Therefore, this collaborative project specifically aims to validate predictive AI models in cooperation with WBU, using external datasets that have not been involved in model training or testing.

A multicentric database on female cancers will be established, incorporating imaging data from academic and tertiary care centers affiliated with DPU, including the University of Cambridge (UK), the Fondazione Policlinico Universitario Gemelli in Rome (Italy), the Uzhgorod National University (Ukraine) and WBU in Albania. Patients treated with NACT for ovarian cancer will be identified at WBU-associated hospitals, and their demographic, clinical, histopathological, and radiological data will be collected.

WBU and DPU will jointly develop and externally validate AI prediction models for aggressive breast and ovarian cancers, contributing to the advancement of personalized cancer care for women.