AUTHOR=Massafra Raffaella , Fanizzi Annarita , Amoroso Nicola , Bove Samantha , Comes Maria Colomba , Pomarico Domenico , Didonna Vittorio , Diotaiuti Sergio , Galati Luisa , Giotta Francesco , La Forgia Daniele , Latorre Agnese , Lombardi Angela , Nardone Annalisa , Pastena Maria Irene , Ressa Cosmo Maurizio , Rinaldi Lucia , Tamborra Pasquale , Zito Alfredo , Paradiso Angelo Virgilio , Bellotti Roberto , Lorusso Vito TITLE=Analyzing breast cancer invasive disease event classification through explainable artificial intelligence JOURNAL=Frontiers in Medicine VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1116354 DOI=10.3389/fmed.2023.1116354 ISSN=2296-858X ABSTRACT=Introduction

Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.

Methods

Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.

Results

Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.

Discussion

Thus, our framework aims at shortening the distance between AI and clinical practice