Evidence map›Paper›PMID 40491759›Full record

ArticlePhysics and imaging in radiation oncology2025

Detection of the failed-tolerance causes of electronic-portal-imaging-device-based

Nipon Saiyo, Hironori Kojima, Kimiya Noto, Naoki Isomura, Kosuke Tsukamoto, Shotaro Yamaguchi, Yuto Segawa, Junya Kohigashi, Akihiro Takemura

Abstract read
In one paragraph

Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Nipon SaiyoDepartment of Quantum Medical Technology, Graduate Course of Medical Sciences and Technology, Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Ishikawa, Japan.
Hironori KojimaDepartment of Radiology, Kanazawa University Hospital, Kanazawa University, Ishikawa, Japan.
Kimiya NotoDepartment of Radiology, Kanazawa University Hospital, Kanazawa University, Ishikawa, Japan.
Naoki IsomuraDepartment of Radiology, Kanazawa University Hospital, Kanazawa University, Ishikawa, Japan.
Kosuke TsukamotoDepartment of Radiology, Kanazawa University Hospital, Kanazawa University, Ishikawa, Japan.
Shotaro YamaguchiDepartment of Quantum Medical Technology, Graduate Course of Medical Sciences and Technology, Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Ishikawa, Japan.
Yuto SegawaDepartment of Quantum Medical Technology, Graduate Course of Medical Sciences and Technology, Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Ishikawa, Japan.
Junya KohigashiDepartment of Quantum Medical Technology, Graduate Course of Medical Sciences and Technology, Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Ishikawa, Japan.
Akihiro TakemuraFaculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Purpose: When electronic-portal-imaging-device (EPID)-based 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.

Indexed as

EPID-based in vivo dosimetryFailed-tolerance detectionMachine-learning model

Identifiers

PMID40491759
PMCPMC12148416

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.