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Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography

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Abstract

The prediction and detection of radiation-related caries (RRC) are crucial to manage the side effects of the head and the neck cancer (HNC) radiotherapy (RT). Despite the demands for the prediction of RRC, no study proposes and evaluates a prediction method. This study introduces a method based on artificial intelligence neural network to predict and detect either regular caries or RRC in HNC patients under RT using features extracted from panoramic radiograph. We selected fifteen HNC patients (13 men and 2 women) to analyze, retrospectively, their panoramic dental images, including 420 teeth. Two dentists manually labeled the teeth to separate healthy and teeth with either type caries. They also labeled the teeth by resistant and vulnerable, as predictive labels telling about RT aftermath caries. We extracted 105 statistical/morphological image features of the teeth using PyRadiomics. Then, we used an artificial neural network classifier (ANN), firstly, to select the best features (using maximum weights) and then label the teeth: in caries and non-caries while detecting RRC, and resistant and vulnerable while predicting RRC. To evaluate the method, we calculated the confusion matrix, receiver operating characteristic (ROC), and area under curve (AUC), as well as a comparison with recent methods. The proposed method showed a sensibility to detect RRC of 98.8% (AUC = 0.9869) and to predict RRC achieved 99.2% (AUC = 0.9886). The proposed method to predict and detect RRC using neural network and PyRadiomics features showed a reliable accuracy able to perform before starting RT to decrease the side effects on susceptible teeth.

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Funding

This study was funded by the Brazilian Governmental Agencies (CAPES: grand number 001) and FAPESP (grant number 2017/20598–9).

Author information

Author notes

  1. Vanessa De Araujo Faria and Mehran Azimbagirad contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Faculty of Medicine, University of São Paulo, Ribeirao Preto, Sao Paulo, Brazil

    Vanessa De Araujo Faria & Gustavo Viani Arruda

  2. Department of Medical Science and Health, University of Western Brittany, 12 Av Foch, IBRBS LATIM Lab, 29200, Brest, France

    Mehran Azimbagirad

  3. Department of Physics, Faculty of Philosophy, Science and Languages, University of São Paulo, Ribeirao Preto, Sao Paulo, Brazil

    Juliana Fernandes Pavoni

  4. Department of Computing and Mathematics, Faculty of Philosophy, Science and Languages, University of São Paulo, Ribeirao Preto, Sao Paulo, Brazil

    Joaquim Cezar Felipe & Luiz Otavio Murta Junior

  5. Medical & Diagnostic Laboratory, 3S Diagnóstico Por Imagem, Leme, SP, Brazil

    Elza Maria Carneiro Mendes Ferreira dos Santos

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  1. Vanessa De Araujo Faria
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Correspondence to Mehran Azimbagirad.

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Appendix

Appendix

Table 5 List of all 105 features by category, name and label extracted using PyRadiomics. More details about each feature definition can be found in PyRadiomics documentary (https://pyradiomics.readthedocs.io/en/latest/features.html)
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Table 6 Results of the neural network classifier performance on 15 subjects including 420 teeth. NHL number of hidden layers, NSF number of selected features, \(u\) upper bound used to remove the features by maximum weights less than this
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Table 7 Results of the NNC performance on 15 subjects including 119 teeth including 75 vulnerable and 44 resistant teeth. NHL number of hidden layers, NSF number of selected features, \(\alpha\) parameter used in feature selection by maximum weights approach
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De Araujo Faria, V., Azimbagirad, M., Viani Arruda, G. et al. Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography. J Digit Imaging 34, 1237–1248 (2021). https://doi.org/10.1007/s10278-021-00487-6

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