دورية أكاديمية
Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study
| العنوان: | Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study |
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| المؤلفون: | Julia L. Fleck, Daniëlle Hooijenga, Raksmey Phan, Xiaolan Xie, Vincent Augusto, Pierre-Etienne Heudel |
| المصدر: | PLoS ONE, Vol 18, Iss 8 (2023) |
| بيانات النشر: | Public Library of Science (PLoS), 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Guidelines for the management of elderly patients with early breast cancer are scarce. Additional adjuvant systemic treatment to surgery for early breast cancer in elderly populations is challenged by increasing comorbidities with age. In non-metastatic settings, treatment decisions are often made under considerable uncertainty; this commonly leads to undertreatment and, consequently, poorer outcomes. This study aimed to develop a decision support tool that can help to identify candidate adjuvant post-surgery treatment schemes for elderly breast cancer patients based on tumor and patient characteristics. Our approach was to generate predictions of patient outcomes for different courses of action; these predictions can, in turn, be used to inform clinical decisions for new patients. We used a cohort of elderly patients (≥ 70 years) who underwent surgery with curative intent for early breast cancer to train the models. We tested seven classification algorithms using 5-fold cross-validation, with 80% of the data being randomly selected for training and the remaining 20% for testing. We assessed model performance using accuracy, precision, recall, F1-score, and AUC score. We used an autoencoder to perform dimensionality reduction prior to classification. We observed consistently better performance using logistic regression and linear discriminant analysis models when compared to the other models we tested. Classification performance generally improved when an autoencoder was used, except for when we predicted the need for adjuvant treatment. We obtained overall best results using a logistic regression model without autoencoding to predict the need for adjuvant treatment (F1-score = 0.869). |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1932-6203 |
| Relation: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449163/?tool=EBI; https://doaj.org/toc/1932-6203 |
| URL الوصول: | https://doaj.org/article/601992b76ae442218a4d8e03da200f12 |
| رقم الأكسشن: | edsdoj.601992b76ae442218a4d8e03da200f12 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19326203 |
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