Quantitative thermal imaging biomarkers to detect acute skin toxicity from breast radiation therapy using supervised machine learning

SAEDNIA, Khadijeh, TABBARAH, Sami, LAGREE, Andrew, WU, Tina, KLEIN, Jonathan, GARCIA, Eduardo, HALL, Michael, CHOW, Edward, RAKOVITCH, Eileen, CHILDS, Charmaine, SADEGHI-NAINI, Ali and TRAN, William (2020). Quantitative thermal imaging biomarkers to detect acute skin toxicity from breast radiation therapy using supervised machine learning. International Journal of Radiation Oncology*Biology*Physics, 106 (5), 1071-1083.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.ijrobp.2019.12.032
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    Abstract

    Purpose Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Current methods to evaluate breast skin toxicity include clinical examination, visual inspection, and patient-reported symptoms. Physiological changes associated with radiation-induced dermatitis, such as inflammation, may also increase body-surface temperature, which can be detected by thermal imaging. Quantitative thermal imaging markers were identified and used in supervised machine learning to develop a predictive model for radiation dermatitis. Methods and Materials Ninety patients treated for adjuvant whole-breast RT (4250 cGy/fx = 16) were recruited for the study. Thermal images of the treated breast were taken at 4 intervals: before RT, then weekly at fx = 5, fx = 10, and fx = 15. Parametric thermograms were analyzed and yielded 26 thermal-based features that included surface temperature (°C) and texture parameters obtained from (1) gray-level co-occurrence matrix, (2) gray-level run-length matrix, and (3) neighborhood gray-tone difference matrix. Skin toxicity was evaluated at the end of RT using the Common Terminology Criteria for Adverse Events (CTCAE) guidelines (Ver.5). Binary group classes were labeled according to a CTCAE cut-off score of ≥2, and thermal features obtained at fx = 5 were used for supervised machine learning to predict skin toxicity. The data set was partitioned for model training, independent testing, and validation. Fifteen patients (∼17% of the whole data set) were randomly selected as an unseen test data set, and 75 patients (∼83% of the whole data set) were used for training and validation of the model. A random forest classifier with leave-1-patient-out cross-validation was employed for modeling single and hybrid parameters. The model performance was reported using receiver operating characteristic analysis on patients from an independent test set. Results Thirty-seven patients presented with adverse skin effects, denoted by a CTCAE score ≥2, and had significantly higher local increases in skin temperature, reaching 36.06°C at fx = 10 (P = .029). However, machine-learning models demonstrated early thermal signals associated with skin toxicity after the fifth RT fraction. The cross-validated model showed high prediction accuracy on the independent test data (test accuracy = 0.87) at fx = 5 for predicting skin toxicity at the end of RT. Conclusions Early thermal markers after 5 fractions of RT are predictive of radiation-induced skin toxicity in breast RT.

    Item Type: Article
    Identification Number: https://doi.org/10.1016/j.ijrobp.2019.12.032
    Page Range: 1071-1083
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 27 Jan 2020 09:51
    Last Modified: 07 Jul 2020 09:31
    URI: http://shura.shu.ac.uk/id/eprint/25736

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