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Development and optimization of a machine-learning prediction model for acute desquamation after breast radiation therapy in the multicenter REQUITE cohort

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Abstract
Purpose: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials: Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naive Bayes models were taken forward to cost-sensitive learning optimisation. Results: One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions: ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. (c) 2022 The Authors. Published by Elsevier Inc. on behalf of American Society for Radiation Oncology.
Keywords
Radiology, Nuclear Medicine and imaging, Oncology, ACUTE SKIN TOXICITY, NORMAL TISSUE, RISK-FACTOR, RADIOTHERAPY, BIOMARKERS

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MLA
Aldraimli, Mahmoud, et al. “Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation after Breast Radiation Therapy in the Multicenter REQUITE Cohort.” ADVANCES IN RADIATION ONCOLOGY, vol. 7, no. 3, 2022, doi:10.1016/j.adro.2021.100890.
APA
Aldraimli, M., Osman, S., Grishchuck, D., Ingram, S., Lyon, R., Mistry, A., … Rattay, T. (2022). Development and optimization of a machine-learning prediction model for acute desquamation after breast radiation therapy in the multicenter REQUITE cohort. ADVANCES IN RADIATION ONCOLOGY, 7(3). https://doi.org/10.1016/j.adro.2021.100890
Chicago author-date
Aldraimli, Mahmoud, Sarah Osman, Diana Grishchuck, Samuel Ingram, Robert Lyon, Anil Mistry, Jorge Oliveira, et al. 2022. “Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation after Breast Radiation Therapy in the Multicenter REQUITE Cohort.” ADVANCES IN RADIATION ONCOLOGY 7 (3). https://doi.org/10.1016/j.adro.2021.100890.
Chicago author-date (all authors)
Aldraimli, Mahmoud, Sarah Osman, Diana Grishchuck, Samuel Ingram, Robert Lyon, Anil Mistry, Jorge Oliveira, Robert Samuel, Leila E.A. Shelley, Daniele Soria, Miriam V. Dwek, Miguel E. Aguado-Barrera, David Azria, Jenny Chang-Claude, Alison Dunning, Alexandra Giraldo, Sheryl Green, Sara Gutiérrez-Enríquez, Carsten Herskind, Hans Van Hulle, Maarten Lambrecht, Laura Lozza, Tiziana Rancati, Victoria Reyes, Barry S. Rosenstein, Dirk de Ruysscher, Maria C. de Santis, Petra Seibold, Elena Sperk, R. Paul Symonds, Hilary Stobart, Begoña Taboada-Valadares, Christopher J. Talbot, Vincent Vakaet, Ana Vega, Liv Veldeman, Marlon R. Veldwijk, Adam Webb, Caroline Weltens, Catharine M. West, Thierry J. Chaussalet, and Tim Rattay. 2022. “Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation after Breast Radiation Therapy in the Multicenter REQUITE Cohort.” ADVANCES IN RADIATION ONCOLOGY 7 (3). doi:10.1016/j.adro.2021.100890.
Vancouver
1.
Aldraimli M, Osman S, Grishchuck D, Ingram S, Lyon R, Mistry A, et al. Development and optimization of a machine-learning prediction model for acute desquamation after breast radiation therapy in the multicenter REQUITE cohort. ADVANCES IN RADIATION ONCOLOGY. 2022;7(3).
IEEE
[1]
M. Aldraimli et al., “Development and optimization of a machine-learning prediction model for acute desquamation after breast radiation therapy in the multicenter REQUITE cohort,” ADVANCES IN RADIATION ONCOLOGY, vol. 7, no. 3, 2022.
@article{8758719,
  abstract     = {{Purpose: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study.

Methods and Materials: Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naive Bayes models were taken forward to cost-sensitive learning optimisation.

Results: One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort.

Conclusions: ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. (c) 2022 The Authors. Published by Elsevier Inc. on behalf of American Society for Radiation Oncology.}},
  articleno    = {{100890}},
  author       = {{Aldraimli, Mahmoud and Osman, Sarah and Grishchuck, Diana and Ingram, Samuel and Lyon, Robert and Mistry, Anil and Oliveira, Jorge and Samuel, Robert and Shelley, Leila E.A. and Soria, Daniele and Dwek, Miriam V. and Aguado-Barrera, Miguel E. and Azria, David and Chang-Claude, Jenny and Dunning, Alison and Giraldo, Alexandra and Green, Sheryl and Gutiérrez-Enríquez, Sara and Herskind, Carsten and Van Hulle, Hans and Lambrecht, Maarten and Lozza, Laura and Rancati, Tiziana and Reyes, Victoria and Rosenstein, Barry S. and de Ruysscher, Dirk and de Santis, Maria C. and Seibold, Petra and Sperk, Elena and Symonds, R. Paul and Stobart, Hilary and Taboada-Valadares, Begoña and Talbot, Christopher J. and Vakaet, Vincent and Vega, Ana and Veldeman, Liv and Veldwijk, Marlon R. and Webb, Adam and Weltens, Caroline and West, Catharine M. and Chaussalet, Thierry J. and Rattay, Tim}},
  issn         = {{2452-1094}},
  journal      = {{ADVANCES IN RADIATION ONCOLOGY}},
  keywords     = {{Radiology,Nuclear Medicine and imaging,Oncology,ACUTE SKIN TOXICITY,NORMAL TISSUE,RISK-FACTOR,RADIOTHERAPY,BIOMARKERS}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{13}},
  title        = {{Development and optimization of a machine-learning prediction model for acute desquamation after breast radiation therapy in the multicenter REQUITE cohort}},
  url          = {{http://doi.org/10.1016/j.adro.2021.100890}},
  volume       = {{7}},
  year         = {{2022}},
}

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