Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms
Metaplastic breast cancer (MpBC) is a rare and aggressive subtype of breast cancer, with data emerging on prognostic factors and survival prediction. This study aimed to develop machine learning models to predict breast cancer-specific survival (BCSS) in MpBC patients, utilizing a dataset of 160 pat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353923001438 |
| _version_ | 1827861393059086336 |
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| author | Yufan Feng Natasha McGuire Alexandra Walton Stephen Fox Antonella Papa Sunil R. Lakhani Amy E. McCart Reed |
| author_facet | Yufan Feng Natasha McGuire Alexandra Walton Stephen Fox Antonella Papa Sunil R. Lakhani Amy E. McCart Reed |
| author_sort | Yufan Feng |
| collection | DOAJ |
| description | Metaplastic breast cancer (MpBC) is a rare and aggressive subtype of breast cancer, with data emerging on prognostic factors and survival prediction. This study aimed to develop machine learning models to predict breast cancer-specific survival (BCSS) in MpBC patients, utilizing a dataset of 160 patients with clinical, pathological, and biological variables. An in-depth variable selection process was carried out using gain ratio and correlation-based methods, resulting in 10 variables for model estimation. Five models (decision tree with bagging; logistic regression; multilayer perceptron; naïve Bayes; and, random forest algorithms) were evaluated using 10-fold cross-validation. Despite the constraints posed by the absence of therapeutic information, the random forest model exhibited the highest performance in predicting BCSS, with an ROC area of 0.808. This study emphasizes the potential of machine learning algorithms in predicting prognosis for complex and heterogeneous cancer subtypes using clinical datasets, and their potential to contribute to patient management. Further research that incorporates additional variables, such as treatment response, and more advanced machine learning techniques will likely enhance the predictive power of MpBC prognostic models. |
| first_indexed | 2024-03-12T13:36:14Z |
| format | Article |
| id | doaj.art-4793b35aa0984c9796d003d29718e6ad |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-03-12T13:36:14Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-4793b35aa0984c9796d003d29718e6ad2023-08-24T04:35:03ZengElsevierJournal of Pathology Informatics2153-35392023-01-0114100329Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithmsYufan Feng0Natasha McGuire1Alexandra Walton2Stephen Fox3Antonella Papa4Sunil R. Lakhani5Amy E. McCart Reed6UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane 4029, AustraliaUQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane 4029, AustraliaUQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane 4029, Australia; Pathology Queensland, The Royal Brisbane and Women’s Hospital, Brisbane 4029, AustraliaPeter MacCallum Cancer Centre and University of Melbourne, Melbourne 3000, AustraliaMonash Biomedicine Discovery Institute, Monash University, Melbourne 3800, AustraliaUQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane 4029, Australia; Pathology Queensland, The Royal Brisbane and Women’s Hospital, Brisbane 4029, AustraliaUQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane 4029, Australia; Corresponding author.Metaplastic breast cancer (MpBC) is a rare and aggressive subtype of breast cancer, with data emerging on prognostic factors and survival prediction. This study aimed to develop machine learning models to predict breast cancer-specific survival (BCSS) in MpBC patients, utilizing a dataset of 160 patients with clinical, pathological, and biological variables. An in-depth variable selection process was carried out using gain ratio and correlation-based methods, resulting in 10 variables for model estimation. Five models (decision tree with bagging; logistic regression; multilayer perceptron; naïve Bayes; and, random forest algorithms) were evaluated using 10-fold cross-validation. Despite the constraints posed by the absence of therapeutic information, the random forest model exhibited the highest performance in predicting BCSS, with an ROC area of 0.808. This study emphasizes the potential of machine learning algorithms in predicting prognosis for complex and heterogeneous cancer subtypes using clinical datasets, and their potential to contribute to patient management. Further research that incorporates additional variables, such as treatment response, and more advanced machine learning techniques will likely enhance the predictive power of MpBC prognostic models.http://www.sciencedirect.com/science/article/pii/S2153353923001438Breast cancerMetaplastic breast cancer (MpBC)Machine learning algorithmsBreast cancer-specific survival (BCSS)Predictive modelsRandom forest |
| spellingShingle | Yufan Feng Natasha McGuire Alexandra Walton Stephen Fox Antonella Papa Sunil R. Lakhani Amy E. McCart Reed Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms Journal of Pathology Informatics Breast cancer Metaplastic breast cancer (MpBC) Machine learning algorithms Breast cancer-specific survival (BCSS) Predictive models Random forest |
| title | Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms |
| title_full | Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms |
| title_fullStr | Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms |
| title_full_unstemmed | Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms |
| title_short | Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms |
| title_sort | predicting breast cancer specific survival in metaplastic breast cancer patients using machine learning algorithms |
| topic | Breast cancer Metaplastic breast cancer (MpBC) Machine learning algorithms Breast cancer-specific survival (BCSS) Predictive models Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S2153353923001438 |
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