Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning

Kalet, Alan M.; Luk, Samuel; Phillips, Mark H.

doi:10.1002/mp.13445

Cited by 54 publications

(50 citation statements)

References 80 publications

(134 reference statements)

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“…Several publications have demonstrated promising results in correlating IMRT QA passing rates with beam complexity scores . Machine learning and deep learning have recently been used to predict IMRT QA passing rates . Among these reports Valdes et al .…”

Section: Introductionmentioning

confidence: 99%

“…7,[9][10][11] Machine learning and deep learning have recently been used to predict IMRT QA passing rates. [12][13][14][15][16][17][18] Among these reports Valdes et al have developed a machine learning (ML) method (Poisson regression with Lasso regularization) that is able to predict IMRT QA results with an error <3%. 12 Despite great success, there are several limitations in their work.…”

Section: Introductionmentioning

confidence: 99%

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Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning

et al. 2019

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Purpose Intensity‐modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning‐based approach to predict portal dosimetry based IMRT QA gamma passing rates. Methods 182 IMRT plans for various treatment sites were planned and delivered with portal dosimetry on two TrueBeam and two Trilogy LINACs. A total of 1497 beams were collected and analyzed using gamma criteria of 2%/2 mm with a 5% threshold. The datasets for building the machine learning models consisted of 1269 beams. Ten‐fold cross‐validation was utilized to tune the model and prevent “overfitting.” A separate test set with the remaining 228 beams was used to evaluate model performance. Each beam was characterized by a set of 31 features including both plan complexity metrics and machine characteristics. Three tree‐based machine learning algorithms (AdaBoost, Random Forest, and XGBoost) were used to train the models and predict gamma passing rates. Results Both AdaBoost and Random Forest had 98% of predictions within 3% of the measured 2%/2 mm gamma passing rates with a maximum error less than 4% and a mean absolute error < 1%. XGBoost showed a slightly worse prediction accuracy with 95% of the predictions within 3% of the measured gamma passing rates and a maximum error of 4.5%. The three models identified the same nine features in the top 10 most important ones that are related to plan complexity and maximum aperture displacement from the central axis or the maximum jaw size in a beam. Conclusion We have demonstrated that portal dosimetry IMRT QA gamma passing rates can be accurately predicted using tree‐based ensemble learning models. The machine learning based approach allows physicists to better identify the failures of IMRT QA measurements and to develop proactive QA approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning

et al. 2019

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“…However, this measurement‐based QA still requires either setup or beam delivery times and cannot predict unacceptable‐quality plans . Recently, prediction of dosimetric accuracy has been developed as a more efficient patient‐specific QA method than measurement‐based QA . In fact, prediction does not require setup or beam delivery times, leading to increased adoption of IMRT and VMAT in clinical facilities.…”

Section: Introductionmentioning

confidence: 99%

“…4 Recently, prediction of dosimetric accuracy has been developed as a more efficient patientspecific QA method than measurement-based QA. [5][6][7][8][9][10] In fact, prediction does not require setup or beam delivery times, leading to increased adoption of IMRT and VMAT in clinical facilities. In addition, prediction allows to determine a decay in QA during treatment planning.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning

et al. 2019

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Purpose The dosimetric accuracies of volumetric modulated arc therapy (VMAT) plans were predicted using plan complexity parameters via machine learning. Methods The dataset consisted of 600 cases of clinical VMAT plans from a single institution. The predictor variables (n = 28) for each plan included complexity parameters, machine type, and photon beam energy. Dosimetric measurements were performed using a helical diode array (ArcCHECK), and the dosimetric accuracy of the passing rates for a 5% dose difference (DD5%) and gamma index of 3%/3 mm (γ3%/3 mm) were predicted using three machine learning models: regression tree analysis (RTA), multiple regression analysis (MRA), and neural networks (NNs). First, the prediction models were applied to 500 cases of the VMAT plans. Then, the dosimetric accuracy was predicted using each model for the remaining 100 cases (evaluation dataset). The error between the predicted and measured passing rates was evaluated. Results For the 600 cases, the mean ± standard deviation of the measured passing rates was 92.3% ± 9.1% and 96.8% ± 3.1% for DD5% and γ3%/3 mm, respectively. For the evaluation dataset, the mean ± standard deviation of the prediction errors for DD5% and γ3%/3 mm was 0.5% ± 3.0% and 0.6% ± 2.4% for RTA, 0.0% ± 2.9% and 0.5% ± 2.4% for MRA, and –0.2% ± 2.7% and –0.2% ± 2.1% for NN, respectively. Conclusions NNs performed slightly better than RTA and MRA in terms of prediction error. These findings may contribute to increasing the efficiency of patient‐specific quality‐assurance procedures.

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“…However, no difference in terms of input standard was observed between old and new datasets as no new standard was established in our institution in recent years. Another factor that could contribute to the degradation of older clinical data is the introduction of an institutional SBRT program in 2010-2013 and an increase in 800 cGy palliative treatments between 2010 and 2014, 34 both of which become common clinical practices afterward. The BN model also demonstrated the ability to adapt clinical practice changes easily by updating the training dataset and retraining the network.…”

Section: Discussionmentioning

confidence: 99%

Characterization of a Bayesian network‐based radiotherapy plan verification model

et al. 2019

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Purpose The current process for radiotherapy treatment plan quality assurance relies on human inspection of treatment plans, which is time‐consuming, error prone and oft reliant on inconsistently applied professional judgments. A previous proof‐of‐principle paper describes the use of a Bayesian network (BN) to aid in this process. This work studied how such a BN could be expanded and trained to better represent clinical practice. Methods We obtained 51 540 unique radiotherapy cases including diagnostic, prescription, plan/beam, and therapy setup factors from a de‐identified Elekta oncology information system from the years 2010–2017 from a single institution. Using a knowledge base derived from clinical experience, factors were coordinated into a 29‐node, 40‐edge BN representing dependencies among the variables. Conditional probabilities were machine learned using expectation maximization module using all data except a subset of 500 patient cases withheld for testing. Different classes of errors that were obtained from incident learning systems were introduced to the testing set of cases which were withheld from the dataset used for building the BN. Different sizes of datasets were used to train the network. In addition, the BN was trained using data from different length epochs as well as different eras. Its performance under these different conditions was evaluated by means of Areas Under the receiver operating characteristic Curve (AUC). Results Our performance analysis found AUCs of 0.82, 0.85, 0.89, and 0.88 in networks trained with 2‐yr, 3‐yr 4‐yr and 5‐yr windows. With a 4‐yr sliding window, we found AUC reduction of 3% per year when moving the window back in time in 1‐yr steps. Compared to the 4‐yr window moved back by 4 yrs (2010–2013 vs 2014–2017), the largest component of overall reduction in AUC over time was from the loss of detection performance in plan/beam error types. Conclusions The expanded BN method demonstrates the ability to detect classes of errors commonly encountered in radiotherapy planning. The results suggest that a 4‐yr training dataset optimizes the performance of the network in this institutional dataset, and that yearly updates are sufficient to capture the evolution of clinical practice and maintain fidelity.

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Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning

Cited by 54 publications

References 80 publications

Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning

Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning

Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning

Characterization of a Bayesian network‐based radiotherapy plan verification model

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