Applications of machine and deep learning to patient‐specific IMRT/VMAT quality assurance

Osman, Alexander F. I.; Maalej, Nabil

doi:10.1002/acm2.13375

Cited by 39 publications

(36 citation statements)

References 58 publications

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“…Despite the rapid changes in maternal conditions, there are often signs before sudden changes in conditions, which are important for reducing maternal deaths and improving adverse outcomes if they are recognized early and treated in a timely manner [ 2 – 4 ]. Safe and effective emergency prescreening triage criteria can accurately identify patients with acute and critical conditions, ensure patient safety, and improve the efficiency of emergency care [ 5 , 6 ]. The emergency medical professional committee of the Chinese nursing association, in collaboration with the quality control center of emergency medicine of Zhejiang Province, has conducted evidence-based research, nationwide surveys, retrospective big data analysis, and retrospective data analysis.…”

Section: Introductionmentioning

confidence: 99%

Clinical Application of Early Warning Scoring Based on BiLSTM-Attention in Emergency Obstetric Preexamination and Triage

Xue

Guo

et al. 2022

Journal of Healthcare Engineering

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Maternity is a special category of population and the criteria for emergency prescreening cannot be directly applied to adults. Therefore, a set of criteria for grading maternal conditions should be established. In this paper, we have combined the semantic analysis technique of BiLSTM-Attention neural network and fuzzy defect risk assessment method, to develop a hybrid approach, to preprocess the text of emergency obstetric prescreening information. Furthermore, we have used word2vec to characterize the word embedding vector and highlight the features related to the degree of defects of emergency obstetric prescreening information through the attention mechanism and obtain the semantic feature vector of the warning information. BiLSTM-Attention neural network has the dual advantages of extracting bidirectional semantic information and giving weight to important judgment information which has effectively improved the semantic understanding accuracy. Experimental tests and application analysis show that the judgment model which is based on proposed method has accurately classified and graded the defects of emergency obstetric prescreening alerts. Additionally, the accuracy and microaverage value are used as evaluation indexes.

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Section: Introductionmentioning

confidence: 99%

Clinical Application of Early Warning Scoring Based on BiLSTM-Attention in Emergency Obstetric Preexamination and Triage

Xue

Guo

et al. 2022

Journal of Healthcare Engineering

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“…These potential errors were eventually projected back into TPS to increase the accuracy of GPRs of IMRT/VMAT plans. GPRs could be predicted within 3% accuracy, and potential errors associated with failure QA could be identified accurately with DL/ML models for IMRT/VMAT PSQA, which makes these models promising for clinical application 32 . However, as mentioned previously, GPRs are weakly associated with clinical dosimetric differences for IMRT/VMAT plans.…”

Section: Introductionmentioning

confidence: 68%

“…GPRs could be predicted within 3% accuracy, and potential errors associated with failure QA could be identified accurately with DL/ML models for IMRT/VMAT PSQA, which makes these models promising for clinical application. 32 However, as mentioned previously, GPRs are weakly associated with clinical dosimetric differences for IMRT/VMAT plans. This renders these PSQA DL/ML models based on GPRs prediction intrinsically incapable of predicting clinically relevant dosimetric errors (DEs).…”

Section: Introductionmentioning

confidence: 83%

Efficient dose–volume histogram–based pretreatment patient‐specific quality assurance methodology with combined deep learning and machine learning models for volumetric modulated arc radiotherapy

et al. 2022

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Background Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose–volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient‐specific quality assurance (PSQA). However, it is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly. Purpose To develop a DVH‐based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH‐based PSQA. Methods A DL model with a three‐dimensional squeeze‐and‐excitation residual blocks incorporated into a modified U‐net was developed to predict the measured PSQA DVHs of 208 head‐and‐neck (H&N) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation, and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training. Results The prediction model was able to achieve a good agreement between predicted, measured, and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for Dmean of PTV6900 (p = 0.001), D50 of PTV6000 (p = 0.014), D2 of PTV5400 (p = 0.009), D50 of left parotid (p = 0.015), and Dmax of left inner ear (p = 0.007). The XGBoost model achieved an area under curves, accuracy, sensitivity, and specificity of 0.89 versus 0.88, 0.89 versus 0.86, 0. 71 versus 0.71, and 0.95 versus 0.91 with measured and predicted PSQA doses, respectively. The agreement between domain experts and the classification model was 86% for 28 test cases. Conclusions The successful prediction of PSQA doses and classification of PoF for H&N VMAT PSQA indicating that this DVH‐based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery.

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“…The feature data of different treatment sites has a very important influence on the classification performance of GPR prediction model. The prediction model for a specific site is helpful to improve the performance of the model [7][8][9][10]. However, there is a lack of studies about pelvic by now.…”

Section: Introductionmentioning

confidence: 99%

Prediction of radiomics-based machine learning for specific dosimetric verification of pelvic intensity modulated radiotherapy

Chen

Zhu

et al. 2023

Preprint

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Background: Machine learning (ML) and deep learning(DL) technology has been used widely in the quality assurance. Due to the complexity of intensity modulated radiotherapy(IMRT)technology, the implementation of patient-specific quality assurance (PSQA) before the treatment has become an essential part in the IMRT. Therefore, this paper is aim to establish the different machine learning classification predict models of gamma pass rates for specific dosimetric verification of pelvic IMRT plan which based on the radiomic features and to explore the best prediction model. Methods: Retrospective analysis of the 3D dosimetric verification results based on measurements with gamma pass rate criteria of 3%/2 mm and 10% dose threshold of 196 pelvic intensity-modulated radiotherapy plans was carried. Prediction models were established by extracting radiomic features data. Four machine learning algorithms, random forest, support vector machine, adaptive boosting and gradient boosting decision trees, were used to calculate the AUC value, sensitivity and specificity respectively. The classification performance of the four prediction models were evaluated. Results: The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision trees models were 0.93,0.85,0.93,0.96, and 0.38,0.69,0.46, and 0.46, respectively. The AUC values for the random forest model and the adaptive boosting model were 0.81 and 0.82, respectively, and the AUC values for the support vector machine and gradient boosting decision tree models were 0.87. Conclusions: Machine learning methods based on radiomics can be used to establish a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity modulated radiotherapy. The classification performance of support vector machine model and gradient boosting decision trees model is better than that of random forest model and adaptive boosting model.The prediction model for a specific site is helpful to improve the performance of the model.

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Applications of machine and deep learning to patient‐specific IMRT/VMAT quality assurance

Cited by 39 publications

References 58 publications

Clinical Application of Early Warning Scoring Based on BiLSTM-Attention in Emergency Obstetric Preexamination and Triage

Clinical Application of Early Warning Scoring Based on BiLSTM-Attention in Emergency Obstetric Preexamination and Triage

Efficient dose–volume histogram–based pretreatment patient‐specific quality assurance methodology with combined deep learning and machine learning models for volumetric modulated arc radiotherapy

Prediction of radiomics-based machine learning for specific dosimetric verification of pelvic intensity modulated radiotherapy

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