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A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy
Neoadjuvant chemotherapy (NAC) may increase the resection rate of breast cancer and shows promising effects on patient prognosis. It has become a necessary treatment choice and is widely used in the clinical setting. Benefitting from the clinical information obtained during NAC treatment, computatio...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813988/ https://www.ncbi.nlm.nih.gov/pubmed/33469514 http://dx.doi.org/10.3389/fonc.2020.592556 |
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author | Song, Dong Man, Xiaxia Jin, Meng Li, Qian Wang, Han Du, Ye |
author_facet | Song, Dong Man, Xiaxia Jin, Meng Li, Qian Wang, Han Du, Ye |
author_sort | Song, Dong |
collection | PubMed |
description | Neoadjuvant chemotherapy (NAC) may increase the resection rate of breast cancer and shows promising effects on patient prognosis. It has become a necessary treatment choice and is widely used in the clinical setting. Benefitting from the clinical information obtained during NAC treatment, computational methods can improve decision-making by evaluating and predicting treatment responses using a multidisciplinary approach, as there are no uniformly accepted protocols for all institutions for adopting different treatment regiments. In this study, 166 Chinese breast cancer cases were collected from patients who received NAC treatment at the First Bethune Hospital of Jilin University. The Miller–Payne grading system was used to evaluate the treatment response. Four machine learning multiple classifiers were constructed to predict the treatment response against the 26 features extracted from the patients’ clinical data, including Random Forest (RF) model, Convolution Neural Network (CNN) model, Support Vector Machine (SVM) model, and Logistic Regression (LR) model, where the RF model achieved the best performance using our data. To allow a more general application, the models were reconstructed using only six selected features, and the RF model achieved the highest performance with 54.26% accuracy. This work can efficiently guide optimal treatment planning for breast cancer patients. |
format | Online Article Text |
id | pubmed-7813988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78139882021-01-18 A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy Song, Dong Man, Xiaxia Jin, Meng Li, Qian Wang, Han Du, Ye Front Oncol Oncology Neoadjuvant chemotherapy (NAC) may increase the resection rate of breast cancer and shows promising effects on patient prognosis. It has become a necessary treatment choice and is widely used in the clinical setting. Benefitting from the clinical information obtained during NAC treatment, computational methods can improve decision-making by evaluating and predicting treatment responses using a multidisciplinary approach, as there are no uniformly accepted protocols for all institutions for adopting different treatment regiments. In this study, 166 Chinese breast cancer cases were collected from patients who received NAC treatment at the First Bethune Hospital of Jilin University. The Miller–Payne grading system was used to evaluate the treatment response. Four machine learning multiple classifiers were constructed to predict the treatment response against the 26 features extracted from the patients’ clinical data, including Random Forest (RF) model, Convolution Neural Network (CNN) model, Support Vector Machine (SVM) model, and Logistic Regression (LR) model, where the RF model achieved the best performance using our data. To allow a more general application, the models were reconstructed using only six selected features, and the RF model achieved the highest performance with 54.26% accuracy. This work can efficiently guide optimal treatment planning for breast cancer patients. Frontiers Media S.A. 2021-01-05 /pmc/articles/PMC7813988/ /pubmed/33469514 http://dx.doi.org/10.3389/fonc.2020.592556 Text en Copyright © 2021 Song, Man, Jin, Li, Wang and Du http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Song, Dong Man, Xiaxia Jin, Meng Li, Qian Wang, Han Du, Ye A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy |
title | A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy |
title_full | A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy |
title_fullStr | A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy |
title_full_unstemmed | A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy |
title_short | A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy |
title_sort | decision-making supporting prediction method for breast cancer neoadjuvant chemotherapy |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813988/ https://www.ncbi.nlm.nih.gov/pubmed/33469514 http://dx.doi.org/10.3389/fonc.2020.592556 |
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