دورية أكاديمية

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification

التفاصيل البيبلوغرافية
العنوان: A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
المؤلفون: Massafra, Raffaella, Comes, Maria Colomba, Bove, Samantha, Didonna, Vittorio, Diotaiuti, Sergio, Giotta, Francesco, Latorre, Agnese, La Forgia, Daniele, Nardone, Annalisa, Pomarico, Domenico, Ressa, Cosmo Maurizio, Rizzo, Alessandro, Tamborra, Pasquale, Zito, Alfredo, Lorusso, Vito, Fanizzi, Annarita
المساهمون: Srinivasan, Kathiravan, Italian Ministry of Health
المصدر: PLOS ONE ; volume 17, issue 9, page e0274691 ; ISSN 1932-6203
بيانات النشر: Public Library of Science (PLoS)
سنة النشر: 2022
المجموعة: PLOS Publications (via CrossRef)
الوصف: Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure.
نوع الوثيقة: article in journal/newspaper
اللغة: English
DOI: 10.1371/journal.pone.0274691
الإتاحة: https://doi.org/10.1371/journal.pone.0274691Test
حقوق: http://creativecommons.org/licenses/by/4.0Test/
رقم الانضمام: edsbas.90DA0A05
قاعدة البيانات: BASE