SU-E-T-103: Development and Implementation of Web Based Quality Control Software

Studinski, Ryan; Taylor, Robert; Angers, Crystal; Russa, D La; Clark, Brett W.

doi:10.1118/1.4888433

Cited by 1 publication

(2 citation statements)

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“…All plans were delivered with a nominal beam energy of 6 MV and were developed using the same beam model in the Monaco 5.11 TPS (Elekta, Sweden). All measurements were stored in, and retrieved from, a database via the QATrack+ application (v0.29) (Studinski et al 2014). Each patient-specific QA measurement was associated with the treatment plan, the treatment unit used to perform the measurement, and the date the measurement was performed.…”

Section: Model Targets: Patient-specific Qa Resultsmentioning

confidence: 99%

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Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics

et al. 2019

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The use of treatment plan characteristics to predict patient-specific quality assurance (QA) measurement results has recently been reported as a strategy to help facilitate automated pre-treatment verification workflows or to provide a virtual assessment of delivery quality. The goal of this work is to investigate the potential of using treatment plan characteristics and linac performance metrics (i.e. quality control test results) in combination with machine learning techniques to predict the results of VMAT patient-specific QA measurements. Using features that describe treatment plan complexity and linac performance metrics, we trained a linear support vector classifier (SVC) to classify the results of VMAT patient-specific QA measurements. The ‘targets’ in this model were simple classes representing median dose difference between measured and expected dose distributions—‘hot’ if the median dose deviation was >1%, ‘cold’ if it was <−1%, and ‘normal’ if it was within ±1%. A total of 1620 unique patient-specific QA measurements were available for model development and testing. 75% of the data were used to develop and cross-validate the model, and the remaining 25% were used for an independent assessment of model performance. For the model development phase, a recursive feature elimination (RFE) cross-validation technique was used to eliminate unimportant features. Model performance was assessed using receiver operator characteristic (ROC) curve metrics. Of the ten features found to be most predictive of patient-specific QA measurement results, half were derived from treatment plan characteristics and half from quality control (QC) metrics characterizing linac performance. The model achieved a micro-averaged area under the ROC curve of 0.93, and a macro-averaged area under the ROC curve of 0.88. This work demonstrates the potential of using both treatment plan characteristics and routine linac QC results in the development of machine learning models for VMAT patient-specific QA measurements.

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Section: Model Targets: Patient-specific Qa Resultsmentioning

confidence: 99%

“…Our clinic uses a QATrack+ (Studinski et al 2014) database to archive the results of all routine linac QC measurements. For each patient-specific QA measurement result, we extracted the most recent preceding routine QC results for the linac on which the measurement was performed.…”

Section: Model Features: Linac Performance Featuresmentioning

confidence: 99%