iSTARS  Team Receives FCT Funding to Develop Clinical Decision Support Tool for Oligometastatic Breast Cancer

iSTARS team has been awarded funding under the FCT (Portuguese Foundation for Science and Technology) “Artificial Intelligence, Data Science and Cybersecurity of relevance to Public Administration” call. This call supports the implementation of advanced cybersecurity, artificial intelligence (AI), and data science systems in public administration to accelerate digital transformation while improving the quality and efficiency of public services across Portugal.

The PredictMet project has been funded under this initiative and will be led by iSTARS ERA Chair Holder João Guimarães in collaboration with Dr. Luís Costa, Dr. Marta Martins, Dr. Marina Pavanello from the Gulbenkian Institute for Molecular Medicine, and Dr. Rita Sousa from Santa Maria Hospital. The project focuses on developing an advanced clinical decision-making support tool designed to predict disease progression in oligometastatic breast cancer patients, using a dataset containing clinical and genomic information from hundreds of patients.

Breast cancer remains the most common cancer among women worldwide, with approximately 30% of cases progressing to metastatic disease. Metastatic breast cancer, characterized by the spread of cancer cells to other parts of the body, is associated with a poorer  prognosis, with a five-year survival rate of just 31%, compared to 86–89% for localized breast cancer. Within this category, metastatic breast cancer is further divided into two subtypes: oligometastatic and polymetastatic disease.

Oligometastatic breast cancer is regarded as an intermediate state in which a limited number of metastases (up to five lesions) occur in one or two distant organs. Patients with this subtype have a potential for successful curative treatments or long-term disease control, achieved through individualized, multidisciplinary management strategies. In contrast, polymetastatic breast cancer involves widespread metastatic spread, significantly reducing treatment options and survival outcomes.

Despite its clinical importance, predicting whether oligometastatic patients will remain progression-free or transition to polymetastatic disease remains a significant challenge. Such predictions are critical, as they influence treatment strategies, patient monitoring, and overall care management.

PredictMet aims to address these challenges by using AI and genomic  biomarkers to identify patients at higher risk of disease progression. The tool will help oncologists make more informed and effective decisions regarding treatment and surveillance, leading to improved patient outcomes and potentially reducing healthcare costs.