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A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients
In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have de...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094026/ https://www.ncbi.nlm.nih.gov/pubmed/37046969 http://dx.doi.org/10.3390/healthcare11071042 |
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author | Bavaro, Domenica Antonia Fanizzi, Annarita Iacovelli, Serena Bove, Samantha Comes, Maria Colomba Cristofaro, Cristian Cutrignelli, Daniela De Santis, Valerio Nardone, Annalisa Lagattolla, Fulvia Rizzo, Alessandro Ressa, Cosmo Maurizio Massafra, Raffaella |
author_facet | Bavaro, Domenica Antonia Fanizzi, Annarita Iacovelli, Serena Bove, Samantha Comes, Maria Colomba Cristofaro, Cristian Cutrignelli, Daniela De Santis, Valerio Nardone, Annalisa Lagattolla, Fulvia Rizzo, Alessandro Ressa, Cosmo Maurizio Massafra, Raffaella |
author_sort | Bavaro, Domenica Antonia |
collection | PubMed |
description | In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing. |
format | Online Article Text |
id | pubmed-10094026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100940262023-04-13 A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients Bavaro, Domenica Antonia Fanizzi, Annarita Iacovelli, Serena Bove, Samantha Comes, Maria Colomba Cristofaro, Cristian Cutrignelli, Daniela De Santis, Valerio Nardone, Annalisa Lagattolla, Fulvia Rizzo, Alessandro Ressa, Cosmo Maurizio Massafra, Raffaella Healthcare (Basel) Article In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing. MDPI 2023-04-05 /pmc/articles/PMC10094026/ /pubmed/37046969 http://dx.doi.org/10.3390/healthcare11071042 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bavaro, Domenica Antonia Fanizzi, Annarita Iacovelli, Serena Bove, Samantha Comes, Maria Colomba Cristofaro, Cristian Cutrignelli, Daniela De Santis, Valerio Nardone, Annalisa Lagattolla, Fulvia Rizzo, Alessandro Ressa, Cosmo Maurizio Massafra, Raffaella A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients |
title | A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients |
title_full | A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients |
title_fullStr | A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients |
title_full_unstemmed | A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients |
title_short | A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients |
title_sort | machine learning approach for predicting capsular contracture after postmastectomy radiotherapy in breast cancer patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094026/ https://www.ncbi.nlm.nih.gov/pubmed/37046969 http://dx.doi.org/10.3390/healthcare11071042 |
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