دورية أكاديمية
Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer
| العنوان: | Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer |
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| المؤلفون: | Angela Lombardi, Nicola Amoroso, Loredana Bellantuono, Samantha Bove, Maria Colomba Comes, Annarita Fanizzi, Daniele La Forgia, Vito Lorusso, Alfonso Monaco, Sabina Tangaro, Francesco Alfredo Zito, Roberto Bellotti, Raffaella Massafra |
| المصدر: | Applied Sciences, Vol 12, Iss 14, p 7227 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | sentinel lymph node, imbalanced dataset, data augmentation, breast cancer, machine learning, interpretability, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2076-3417 |
| العلاقة: | https://www.mdpi.com/2076-3417/12/14/7227Test; https://doaj.org/toc/2076-3417Test |
| DOI: | 10.3390/app12147227 |
| الوصول الحر: | https://doaj.org/article/aaf36b281ca24345804fe579e8cb3e10Test |
| رقم الانضمام: | edsdoj.f36b281ca24345804fe579e8cb3e10 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20763417 |
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| DOI: | 10.3390/app12147227 |