Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction
Background:. Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reco...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer
2024-02-01
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| Series: | Plastic and Reconstructive Surgery, Global Open |
| Online Access: | http://journals.lww.com/prsgo/fulltext/10.1097/GOX.0000000000005599 |
| _version_ | 1797292742666092544 |
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| author | Yi-Fu Chen, BSc, MSc Sahil Chawla, HBSc Dorsa Mousa-Doust, BSc, MD Alan Nichol, MD, FRCPC, CCFP Raymond Ng, HBSc, PhD Kathryn V. Isaac, MD, MPH, FRCSC |
| author_facet | Yi-Fu Chen, BSc, MSc Sahil Chawla, HBSc Dorsa Mousa-Doust, BSc, MD Alan Nichol, MD, FRCPC, CCFP Raymond Ng, HBSc, PhD Kathryn V. Isaac, MD, MPH, FRCSC |
| author_sort | Yi-Fu Chen, BSc, MSc |
| collection | DOAJ |
| description | Background:. Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reconstruction (IBR).
Methods:. In this retrospective study, breast cancer patients who underwent IBR from January 2017 to December 2020 were reviewed and data were collected on 81 preoperative characteristics. Primary outcome was recommendation for PMRT. Four algorithms were compared to maximize performance and clinical utility: logistic regression, elastic net (EN), logistic lasso, and random forest (RF). The cohort was split into a development dataset (75% of cohort for training-validation) and 25% used for the test set. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration plots.
Results:. In a total of 800 patients, 325 (40.6%) patients were recommended to undergo PMRT. With the training-validation dataset (n = 600), model performance was logistic regression 0.73 AUC [95% confidence interval (CI) 0.65–0.80]; RF 0.77 AUC (95% CI, 0.74–0.81); EN 0.77 AUC (95% CI, 0.73–0.81); logistic lasso 0.76 AUC (95% CI, 0.72–0.80). Without significantly sacrificing performance, 81 predictive factors were reduced to 12 for prediction with the EN method. With the test dataset (n = 200), performance of the EN prediction model was confirmed [0.794 AUC (95% CI, 0.730–0.858)].
Conclusion:. A parsimonious accurate machine learning model for predicting PMRT after IBR was developed, tested, and translated into a clinically applicable online calculator for providers and patients. |
| first_indexed | 2024-03-07T20:01:53Z |
| format | Article |
| id | doaj.art-2db69e70c7804c1889de967dfbf8290f |
| institution | Directory Open Access Journal |
| issn | 2169-7574 |
| language | English |
| last_indexed | 2024-03-07T20:01:53Z |
| publishDate | 2024-02-01 |
| publisher | Wolters Kluwer |
| record_format | Article |
| series | Plastic and Reconstructive Surgery, Global Open |
| spelling | doaj.art-2db69e70c7804c1889de967dfbf8290f2024-02-28T06:48:00ZengWolters KluwerPlastic and Reconstructive Surgery, Global Open2169-75742024-02-01122e559910.1097/GOX.0000000000005599202402000-00026Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast ReconstructionYi-Fu Chen, BSc, MSc0Sahil Chawla, HBSc1Dorsa Mousa-Doust, BSc, MD2Alan Nichol, MD, FRCPC, CCFP3Raymond Ng, HBSc, PhD4Kathryn V. Isaac, MD, MPH, FRCSC5From the * Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada† Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada† Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada‡ Department of Radiation Oncology, BC Cancer, Vancouver, British Columbia, Canada.From the * Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada† Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, CanadaBackground:. Post mastectomy radiotherapy (PMRT) is an independent predictor of reconstructive complications. PMRT may alter the timing and type of reconstruction recommended. This study aimed to create a machine learning model to predict the probability of requiring PMRT after immediate breast reconstruction (IBR). Methods:. In this retrospective study, breast cancer patients who underwent IBR from January 2017 to December 2020 were reviewed and data were collected on 81 preoperative characteristics. Primary outcome was recommendation for PMRT. Four algorithms were compared to maximize performance and clinical utility: logistic regression, elastic net (EN), logistic lasso, and random forest (RF). The cohort was split into a development dataset (75% of cohort for training-validation) and 25% used for the test set. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), precision-recall curves, and calibration plots. Results:. In a total of 800 patients, 325 (40.6%) patients were recommended to undergo PMRT. With the training-validation dataset (n = 600), model performance was logistic regression 0.73 AUC [95% confidence interval (CI) 0.65–0.80]; RF 0.77 AUC (95% CI, 0.74–0.81); EN 0.77 AUC (95% CI, 0.73–0.81); logistic lasso 0.76 AUC (95% CI, 0.72–0.80). Without significantly sacrificing performance, 81 predictive factors were reduced to 12 for prediction with the EN method. With the test dataset (n = 200), performance of the EN prediction model was confirmed [0.794 AUC (95% CI, 0.730–0.858)]. Conclusion:. A parsimonious accurate machine learning model for predicting PMRT after IBR was developed, tested, and translated into a clinically applicable online calculator for providers and patients.http://journals.lww.com/prsgo/fulltext/10.1097/GOX.0000000000005599 |
| spellingShingle | Yi-Fu Chen, BSc, MSc Sahil Chawla, HBSc Dorsa Mousa-Doust, BSc, MD Alan Nichol, MD, FRCPC, CCFP Raymond Ng, HBSc, PhD Kathryn V. Isaac, MD, MPH, FRCSC Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction Plastic and Reconstructive Surgery, Global Open |
| title | Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction |
| title_full | Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction |
| title_fullStr | Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction |
| title_full_unstemmed | Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction |
| title_short | Machine Learning to Predict the Need for Postmastectomy Radiotherapy after Immediate Breast Reconstruction |
| title_sort | machine learning to predict the need for postmastectomy radiotherapy after immediate breast reconstruction |
| url | http://journals.lww.com/prsgo/fulltext/10.1097/GOX.0000000000005599 |
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