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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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    Authors
    Aldraimli, M.
    Osman, S.
    Grishchuck, D.
    Ingram, Samuel
    Lyon, R.
    Mistry, A.
    Oliveira, J.
    Samuel, R.
    Shelley, L. E. A.
    Soria, D.
    Dwek, M. V.
    Aguado-Barrera, M. E.
    Azria, D.
    Chang-Claude, J.
    Dunning, A.
    Giraldo, A.
    Green, S.
    Gutiérrez-Enríquez, S.
    Herskind, C.
    van Hulle, H.
    Lambrecht, M.
    Lozza, L.
    Rancati, T.
    Reyes, V.
    Rosenstein, B. S.
    de Ruysscher, D.
    de Santis, M. C.
    Seibold, P.
    Sperk, E.
    Symonds, R. P.
    Stobart, H.
    Taboada-Valadares, B.
    Talbot, C. J.
    Vakaet, V. J. L.
    Vega, A.
    Veldeman, L.
    Veldwijk, M. R.
    Webb, A.
    Weltens, C.
    West, Catharine M L
    Chaussalet, T. J.
    Rattay, T.
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    Affiliation
    Health Innovation Ecosystem, University of Westminster, London
    Issue Date
    2022
    
    Metadata
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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 naïve 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.
    Citation
    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. Vol. 7, Advances in Radiation Oncology. Elsevier BV; 2022. p. 100890.
    Journal
    Advances in Radiation Oncology
    URI
    http://hdl.handle.net/10541/625351
    DOI
    10.1016/j.adro.2021.100890
    PubMed ID
    35647396
    Additional Links
    https://dx.doi.org/10.1016/j.adro.2021.100890
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.adro.2021.100890
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