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. 2025 May 17:34:100785.
doi: 10.1016/j.phro.2025.100785. eCollection 2025 Apr.

Detection of the failed-tolerance causes of electronic-portal-imaging-device-based in vivo dosimetry using machine learning for volumetric-modulated arc therapy: A feasibility study

Nipon Saiyo  1   2 , Hironori Kojima  3 , Kimiya Noto  3 , Naoki Isomura  3 , Kosuke Tsukamoto  3 , Shotaro Yamaguchi  1 , Yuto Segawa  1 , Junya Kohigashi  1 , Akihiro Takemura  4
Affiliations

Detection of the failed-tolerance causes of electronic-portal-imaging-device-based in vivo dosimetry using machine learning for volumetric-modulated arc therapy: A feasibility study

Nipon Saiyo et al. Phys Imaging Radiat Oncol. .

Abstract

Background and purpose: When electronic-portal-imaging-device (EPID)-based in vivo dosimetry (IVD) identifies dose tolerance failures, the cause of the failures should be evaluated. This study aimed to develop a machine-learning (ML) model to classify the cause of EPID-based IVD failures in volumetric-modulated arc therapy (VMAT) treatment.

Materials and methods: Twenty-three prostate VMAT plans were used to recalculate the dose distribution in homogeneous phantom images as no-error (NE) plans. Errors in the randomized multileaf collimator (RMLC) position, monitor unit (MU) variation, lateral position, pitch rotation, and roll rotation were simulated. The IVD results of the NE plans and introduced errors were obtained using EPIgray software. Support vector machines (SVMs) were used to develop ML models for each error. The accuracy percentage, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate models' performances. The models were verified using five additional plans with an Alderson Rando phantom.

Results: The models obtained accuracies of over 90% and F1-scores of 0.9 for the RMLC position and MU variation. For lateral position, pitch rotation, and roll rotation errors, the accuracies were 66.1%, 65.2%, and 66.8%, and the F1-scores were 0.66, 0.65, and 0.67, respectively. The AUCs for all the errors were over 0.7. Additionally, the model verification results consistently classified EPIgray data for all the error types.

Conclusion: The developed ML models classified the causes of the failed tolerance of the EPID-based IVD.

Keywords: EPID-based in vivo dosimetry; Failed-tolerance detection; Machine-learning model.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The process for analyzing EPIgray data.
Fig. 2
Fig. 2
Workflow of the development of the five ML models to identify the causes of failed tolerance based on EPIgray results.
See this image and copyright information in PMC

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