Artificial intelligence for prediction of measurement-based patient-specific quality assurance is ready for prime time

Pretreatment patient-specific quality assurance prediction based on 1D complexity metrics and 3D planning dose: classification, gamma passing rates, and DVH metrics

PURPOSE

Highly modulated radiotherapy plans aim to achieve target conformality and spare organs at risk, but the high complexity of the plan may increase the uncertainty of treatment. Thus, patient-specific quality assurance (PSQA) plays a crucial role in ensuring treatment accuracy and providing clinical guidance. This study aims to propose a prediction model based on complexity metrics and patient planning dose for PSQA results.

MATERIALS AND METHODS

Planning dose, measurement-based reconstructed dose and plan complexity metrics of the 687 radiotherapy plans of patients treated in our institution were collected for model establishing. Global gamma passing rate (GPR, 3%/2mm,10% threshold) of 90% was used as QA criterion. Neural architecture models based on Swin-transformer were adapted to process 3D dose and incorporate 1D metrics to predict QA results. The dataset was divided into training (447), validation (90), and testing (150) sets. Evaluation of predictions was performed using mean absolute error (MAE) for GPR, planning target volume (PTV) HI and PTV CI, mean absolute percentage error (MAPE) for PTV D95, PTV D2 and PTV Dmean, and the area under the receiver operating characteristic (ROC) curve (AUC) for classification. Furthermore, we also compare the prediction results with other models based on either only 1D or 3D inputs.

RESULTS

In this dataset, 72.8% (500/687) plans passed the pretreatment QA under the criterion. On the testing set, our model achieves the highest performance, with the 1D model slightly surpassing the 3D model. The performance results are as follows (combine, 1D, and 3D transformer): The AUCs are 0.92, 0.88 and 0.86 for QA classification. The MAEs of prediction are 0.039, 0.046, and 0.040 for 3D GPR, 0.018, 0.021, and 0.019 for PTV HI, and 0.075, 0.078, and 0.084 for PTV CI. Specifically, for cases with 3D GPRs greater than 90%, the MAE could achieve 0.020 (combine). The MAPE of prediction is 1.23%, 1.52%, and 1.66% for PTV D95, 2.36%, 2.67%, and 2.45% for PTV D2, and 1.46%, 1.70%, and 1.71% for PTV Dmean.

CONCLUSION

The model based on 1D complexity metrics and 3D planning dose could predict pretreatment PSQA results with high accuracy and the complexity metrics play a leading role in the model. Furthermore, dose-volume metric deviations of PTV could be predicted and more clinically valuable information could be provided.

Do we need patient-specific QA for adaptively generated plans? Retrospective evaluation of delivered online adaptive treatment plans on Varian Ethos

BACKGROUND

The clinical introduction of dedicated treatment units for online adaptive radiation therapy (OART) has led to widespread adoption of daily adaptive radiotherapy. OART allows for rapid generation of treatment plans using daily patient anatomy, potentially leading to reduction of treatment margins and increased normal tissue sparing. However, the OART workflow does not allow for measurement of patient-specific quality assurance (PSQA) during treatment delivery sessions and instead relies on secondary dose calculations for verification of adapted plans. It remains unknown if independent dose verification is a sufficient surrogate for PSQA measurements.

PURPOSE

To evaluate the plan quality of previously treated adaptive plans through multiple standard PSQA measurements.

METHODS

This IRB-approved retrospective study included sixteen patients previously treated with OART at our institution. PSQA measurements were performed for each patient's scheduled and adaptive plans: five adaptive plans were randomly selected to perform ion chamber measurements and two adaptive plans were randomly selected for ArcCHECK measurements. The same ArcCHECK 3D dose distribution was also sent to Mobius3D to evaluate the second-check dosimetry system.

RESULTS

All (n = 96) ion chamber measurements agreed with the planned dose within 3% with a mean of 1.4% (± 0.7%). All (n = 48) plans passed ArcCHECK measurements using a 95% gamma passing threshold and 3%/2 mm criteria with a mean of 99.1% (± 0.7%). All (n = 48) plans passed Mobius3D second-check performed with 95% gamma passing threshold and 5%/3 mm criteria with a mean of 99.0% (± 0.2%).

CONCLUSION

Plan measurement for PSQA may not be necessary for every online-adaptive treatment verification. We recommend the establishment of a periodic PSQA check to better understand trends in passing rates for delivered adaptive treatments.

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

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 D_mean 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 D_max 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.

Prospective clinical validation of virtual patient-specific quality assurance of VMAT radiation therapy plans

PURPOSE

Performing measurement-based patient-specific quality assurance (PSQA) is recognized as a resource-intensive and time inefficient task in the radiotherapy treatment workflow. Paired with technological refinements in modern radiotherapy, research towards measurement-free PSQA has seen increased interest over the last five years. However, these efforts have not been clinically implemented or prospectively validated in the U.S. We propose a virtual QA (VQA) system and workflow to assess the safety and workload reduction of measurement-free PSQA.

METHODS

An XGBoost machine learning model was designed to predict PSQA outcomes of VMAT plans, represented as percent differences between the measured ion chamber point dose in a phantom and the corresponding planned dose. The final model was deployed within a web application to predict PSQA outcomes of clinical plans within an existing clinical workflow. The application also displays relevant feature importance and plan-specific distribution analyses relative to database plans for documentation and to aid physicist interpretation and evaluation. VQA predictions were prospectively validated over three months of measurements at our clinic to assess safety and efficiency gains.

RESULTS

Over three months, VQA predictions for 445 VMAT plans were prospectively validated at our institution. VQA predictions for these plans had a mean absolute error of 1.08 +/- 0.77%, with a maximum absolute error of 2.98%. Employing a 1% prediction threshold (i.e. plans predicted to have an absolute error of less than 1% would not require a measurement) would yield a 69.2% reduction in QA workload - saving 32.5 hours per month on average - with 81.5%/72.4%/0.81 sensitivity/specificity/AUC at a 3% clinical threshold and 100%/70%/0.93 sensitivity/specificity/AUC at a 4% clinical threshold.

CONCLUSION

This is the first prospective clinical implementation and validation of VQA in the U.S., which we observed to be efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for PSQA, leading to more effective allocation of clinical resources.