التفاصيل البيبلوغرافية
| العنوان: |
Analyzing breast cancer invasive disease event classification through explainable artificial intelligence |
| المؤلفون: |
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 |
| المساهمون: |
Ministry of Health |
| المصدر: |
Frontiers in Medicine ; volume 10 ; ISSN 2296-858X |
| بيانات النشر: |
Frontiers Media SA |
| سنة النشر: |
2023 |
| المجموعة: |
Frontiers (Publisher - via CrossRef) |
| الوصف: |
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 |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| DOI: |
10.3389/fmed.2023.1116354 |
| DOI: |
10.3389/fmed.2023.1116354/full |
| الإتاحة: |
https://doi.org/10.3389/fmed.2023.1116354Test |
| حقوق: |
https://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.9C9C0B9F |
| قاعدة البيانات: |
BASE |
