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Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning

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Abstract

This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston–Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.

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Funding

No specific grant was given to this research by funding organizations in the public, private, or not-for-profit sectors.

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Authors and Affiliations

  1. Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Sana Salahuddin & Saeed Ahmad Buzdar

  2. Medical Physics Department Shaukat Khanum Memorial Cancer Hospital & Research Center, Lahore, Pakistan

    Khalid Iqbal

  3. Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy

    Muhammad Adeel Azam

  4. Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genoa, Genoa, Italy

    Muhammad Adeel Azam

  5. Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy

    Sana Salahuddin & Lidia Strigari

Authors
  1. Sana Salahuddin
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  2. Saeed Ahmad Buzdar
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  3. Khalid Iqbal
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  4. Muhammad Adeel Azam
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  5. Lidia Strigari
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Contributions

Conceptualization, SS, SAB and LS; methodology, SS, KI; software, MAA.; formal analysis, SS, MAA.; investigation, SS.; resources, LS; data curation, KI; writing—original draft preparation, SS, MAA.; writing—review and editing, SS, LS, MAA. supervision, SAB, LS; visualization, LS. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Sana Salahuddin.

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There are no animal or human subjects used in any of the experiments in this article.

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Salahuddin, S., Buzdar, S.A., Iqbal, K. et al. Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning. Radiol Phys Technol 17, 219–229 (2024). https://doi.org/10.1007/s12194-023-00768-5

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  • DOI: https://doi.org/10.1007/s12194-023-00768-5

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