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Predicting the Need for Adaptive Radiotherapy in Head and Neck Patients from CT-Based Radiomics and Pre-treatment Data

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12953))

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Abstract

Although adaptive radiotherapy can reduce the negative dosimetric and clinical impacts of anatomical changes during head and neck treatments, evidence shows that it is not equally beneficial for all patients. This makes it important to electively schedule adaptation ahead of time to optimize clinical resources and patient benefit. The purpose of this study is to assess the feasibility of using both pre-treatment patient features and radiomic features extracted from a pre-treatment contrast enhanced computed tomography scan to predict the need for adaptive radiotherapy. Seventy-two patients were included in the analysis, of which 36 required adaptation. 36 pre-treatment semantic features as well as 351 radiomic features extracted from the gross target volume were considered. Three support vector machine models were developed: 1) considering only semantic features; 2) considering only radiomic features; 3) using a combination of features from 1 and 2. A robustness analysis of the selected radiomic features was also conducted. The best classification results were obtained considering 6 features (4 semantic and 2 radiomic) with median accuracy and area under the receiver operating characteristic curve of 0.821 and 0.843, respectively.

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Author information

Authors and Affiliations

  1. Faculty of Science and Technology, Physics Department, University of Coimbra, Coimbra, Portugal

    Natália Alves

  2. Institute of Computer and Systems Engineering, Coimbra, Portugal

    Joana Dias & Maria do Carmo Lopes

  3. Faculty of Economics, University of Coimbra, Coimbra, Portugal

    Joana Dias

  4. Medical Physics Department, Instituto Português de Oncologia de Coimbra Francisco Gentil, EPE, Coimbra, Portugal

    Tiago Ventura, Josefina Mateus, Miguel Capela & Maria do Carmo Lopes

  5. EPE, Radiotherapy Department, Instituto Português de Oncologia de Coimbra Francisco Gentil, Coimbra, Portugal

    Leila Khouri

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  1. Natália Alves
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  4. Josefina Mateus
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  5. Miguel Capela
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  7. Maria do Carmo Lopes
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Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Alves, N. et al. (2021). Predicting the Need for Adaptive Radiotherapy in Head and Neck Patients from CT-Based Radiomics and Pre-treatment Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12953. Springer, Cham. https://doi.org/10.1007/978-3-030-86976-2_29

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  • DOI: https://doi.org/10.1007/978-3-030-86976-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86975-5

  • Online ISBN: 978-3-030-86976-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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