Study on the ability of 3D gamma analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS)

Bai, Han; Zhu, Sijin; Wu, Xinghan; Liu, Xuhong; Chen, Feihu; Yan, Jingwei

doi:10.1186/s13014-020-01603-6

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…In addition, it is impossible to determine the specific reason for the failure field of gamma analysis [ 25 ]. Although 3D gamma analysis and/or delivery DVHs based on anatomical structure provide more evaluation methods by many 3D verification systems [ 26 ], the retrospective error review cannot be performed on plan delivery. The model in this study provides a novel method for error classification and error magnitude prediction in delivery, and it is expected to eliminate the limitations of the well-known conventional IMRT QA method.…”

Section: Discussionmentioning

confidence: 99%

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

Huang

Ma³

et al. 2023

Strahlenther Onkol

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Objective To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML). Methods In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD). Both distributions were calculated for all beams with portal dose image prediction (PDIP). Besides, 14 image-based features were extracted from RDD and EDD of portal dose predictions to obtain the feature vectors. In addition, a random forest was adopted for the multiclass classification task, and regression prediction for error magnitude. Results The top five features extracted with the highest weight included 1) the relative displacement in the x direction, 2) the ratio of the absolute minimum residual error to the maximal RDD value, 3) the product of the maximum and minimum residuals, 4) the ratio of the absolute maximum residual error to the maximal RDD value, and 5) the ratio of the absolute mean residual value to the maximal RDD value. The relative displacement in the x direction had the highest weight. The overall accuracy of the five-class classification model was 99.85% for the validation set and 99.30% for the testing set. This model could be applied to the classification of the error-free plan, COLL, MU, MLCS, and MLCR with an accuracy of 100%, 98.4%, 99.9%, 98.0%, and 98.3%, respectively. MLCR had the worst performance in error magnitude prediction (70.1–96.6%), while others had better performance in error magnitude prediction (higher than 93%). In the error magnitude prediction, the mean absolute error (MAE) between predicted error magnitude and actual error ranged from 0.03 to 0.33, with the root mean squared error (RMSE) varying from 0.17 to 0.56 for the validation set. The MAE and RMSE ranged from 0.03 to 0.50 and 0.44 to 0.59 for the test set, respectively. Conclusion It could be demonstrated in this study that the image-based features extracted from RDD and EDD can be employed to identify different types of delivery errors and accurately predict error magnitude with the assistance of ML techniques. They can be used to associate traditional gamma analysis with clinically based analysis for error classification and magnitude prediction in patient-specific IMRT quality assurance.

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Section: Discussionmentioning

confidence: 99%

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

Huang

Ma³

et al. 2023

Strahlenther Onkol

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“…After 2015, the number of publications is increased significantly. There are 9 publications in 2016 [118][119][120][121][122][123][124][125][126], 7 publications in 2017 [127][128][129][130][131][132][133], 10 publications in 2018 [134][135][136][137][138][139][140][141][142][143], 9 publications in 2019 [144][145][146][147][148][149][150][151][152] and 8 publications in 2020 [153][154][155][156][157][158][159][160]. Within the subspecialty of radiation therapy, the numbers of publications in the fields of clinical implementation, plan optimization, plan evaluation and quality assurance are 23 papers (37%), 16 papers (26%), 14 papers (23%) and 9 papers (14%), respectively.…”

Section: Scientific Researchmentioning

confidence: 99%

The status of medical physics in radiotherapy in China

Yan

Huang

et al. 2021

Physica Medica

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To present an overview of the status of medical physics in radiotherapy in China, including facilities and devices, occupation, education, research, etc. Materials and methods: The information about medical physics in clinics was obtained from the 9-th nationwide survey conducted by the China Society for Radiation Oncology in 2019. The data of medical physics in education and research was collected from the publications of the official and professional organizations. Results: By 2019, there were 1463 hospitals or institutes registered to practice radiotherapy and the number of accelerators per million population was 1.5. There were 4172 medical physicists working in clinics of radiation oncology. The ratio between the numbers of radiation oncologists and medical physicists is 3.51. Approximately, 95% of medical physicists have an undergraduate or graduate degrees in nuclear physics and biomedical engineering. 86% of medical physicists have certificates issued by the Chinese Society of Medical Physics. There has been a fast growth of publications by authors from mainland of China in the top international medical physics and radiotherapy journals since 2018. Conclusions: Demand for medical physicists in radiotherapy increased quickly in the past decade. The distribution of radiotherapy facilities in China became more balanced. High quality continuing education and training programs for medical physicists are deficient in most areas. The role of medical physicists in the clinic has not been clearly defined and their contributions have not been fully recognized by the community.Prof. Xu Haichao in 1940s at Peking Union Medical College (PUMC). He worked with Prof. Marvin Williomus, an American medical physicist, to treat cancer patients using a 400 kV X-ray unit [7]. The department of radiology in Caner Institute of Chinese Academy of Medical Sciences (CICAMS) started to treat patients with a Cobalt-60 unit in 1958. At the same year, the division of radiation physics in CICAMS was founded. They made the manual multi-leaf collimator and installed it on Cobalt-60 machine in 1965. Later, they made the remote control system for brachytherapy machine using Cobalt-60 source ball in 1965Cobalt-60 source ball in -1967.From 1970, China began to build linear accelerators and on-board imaging devices. As a result, 25 MeV Betatron, Simulator and Cobalt-60 brachytherapy machine were produced by Tianjing therapeutic instrument factory [9]. Meanwhile, Beijing Medical Apparatus and Instruments Institute built the first linear accelerator [10]. Since 1976, the major hospitals started to purchase high-end linear accelerators, CT, Brachytron and planning software from USA and Europe [11]. Following the clinical application of the linear accelerators, text books in Chinese

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Quantified difference of the collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms based on DVH and gamma analysis for cervical cancer radiation therapy

Zhang,

Bai,

Wang

et al. 2024

Applied Radiation and Isotopes

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Study on the ability of 3D gamma analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS)

Cited by 3 publications

References 44 publications

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

The status of medical physics in radiotherapy in China

Quantified difference of the collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms based on DVH and gamma analysis for cervical cancer radiation therapy

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