Dataset Information

Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine.

ABSTRACT:

Background

Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload.

Methods

Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance.

Results

For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%.

Conclusions

The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time.

SUBMITTER: Moreau N

PROVIDER: S-EPMC10001389 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine.

Moreau Noémie N Bonnor Laurine L Jaudet Cyril C Lechippey Laetitia L Falzone Nadia N Batalla Alain A Bertaut Cindy C Corroyer-Dulmont Aurélien A

Diagnostics (Basel, Switzerland) 20230302 5

<h4>Background</h4>Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload.<h4>Methods</h4>Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model wa ...[more]

PMID: 36900087

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Project description:BackgroundModern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use complex fluence modulation strategies to achieve optimal patient dose distribution. Ensuring their accuracy necessitates rigorous patient-specific quality assurance (PSQA), traditionally done through pretreatment measurements with detector arrays. While effective, these methods are labor-intensive and time-consuming. Independent calculation-based methods leveraging advanced dose algorithms provide a reduced workload but cannot account for machine performance during delivery.PurposeThis study introduces a novel unified deep-learning (DL) framework to enhance PSQA. The framework can combine the strengths of measurement- and calculation-based approaches.MethodsA comprehensive artificial training dataset, comprising 400,000 samples, was generated based on a rigorous mathematical model that describes the physical processes of radiation transport and interaction within both the medium and detector. This artificial data was used to pretrain the DL models, which were subsequently fine-tuned with a measured dataset of 400 IMRT segments to capture the machine-specific characteristics. Additional measurements of five IMRT plans were used as the unseen test dataset. Within the unified framework, a forward prediction model uses plan parameters to predict the measured dose distributions, while the backward prediction model reconstructs these parameters from actual measurements. The former enables a detailed control point (CP)-wise analysis. At the same time, the latter facilitates the reconstruction of treatment plans from the measurements and, subsequently, dose recalculation in the treatment planning system (TPS), as well as an independent second check software (VERIQA). This method has been tested with an OD 1600 SRS and an OD 1500 detector array with distinct spatial resolution and detector arrangement in combination with a dedicated upsampling model for the latter.ResultsThe final models could deliver highly accurate predictions of the measurements in the forward direction and the actual delivered plan parameters in the backward direction. In the forward direction, the test plans reached median gamma passing rates better than 94% for the OD 1600 SRS measurements. The upsampled OD 1500 measurements show similar performance with similar median gamma passing rates but a slightly higher variability. The 3D gamma passing rates from the comparisons between the original and reconstructed dose distributions in patients lie between 95.4% and 98.2% for the OD 1600 SRS and 94.7% and 98.5% for the interpolated OD 1500 measurements. The dose volume histograms (DVH) of the original and the reconstructed plans, recalculated in both the TPS and VERIQA, were evaluated for the organs at risk and targets based on clinical protocols and showed no clinically relevant deviations.ConclusionsThe flexibility of the implemented model architecture allows its adaptability to other delivery techniques and measurement modalities. Its utilization also reduces the requirements of the measurement devices. The proposed unified framework could play a decisive role in automating QA workflow, especially in the context of real-time adaptive radiation therapy (ART).

Project description:BackgroundSCOPE 1 was the first UK based multi-centre trial involving radiotherapy of the oesophagus. A comprehensive radiotherapy trials quality assurance programme was launched with two main aims: 1. To assist centres, where needed, to adapt their radiotherapy techniques in order to achieve protocol compliance and thereby enable their participation in the trial. 2. To support the trial's clinical outcomes by ensuring the consistent planning and delivery of radiotherapy across all participating centres.MethodsA detailed information package was provided and centres were required to complete a benchmark case in which the delineated target volumes and organs at risk, dose distribution and completion of a plan assessment form were assessed prior to recruiting patients into the trial. Upon recruiting, the quality assurance (QA) programme continued to monitor the outlining and planning of radiotherapy treatments. Completion of a questionnaire was requested in order to gather information about each centre's equipment and techniques relating to their trial participation and to assess the impact of the trial nationally on standard practice for radiotherapy of the oesophagus. During the trial, advice was available for individual planning issues, and was circulated amongst the SCOPE 1 community in response to common areas of concern using bulletins.Results36 centres were supported through QA processes to enable their participation in SCOPE1. We discuss the issues which have arisen throughout this process and present details of the benchmark case solutions, centre questionnaires and on-trial protocol compliance. The range of submitted benchmark case GTV volumes was 29.8-67.8cm3; and PTV volumes 221.9-513.3 cm3. For the dose distributions associated with these volumes, the percentage volume of the lungs receiving 20Gy (V20Gy) ranged from 20.4 to 33.5%. Similarly, heart V40Gy ranged from 16.1 to 33.0%. Incidence of incorrect outlining of OAR volumes increased from 50% of centres at benchmark case, to 64% on trial. Sixty-five percent of centres, who returned the trial questionnaire, stated that their standard practice had changed as a result of their participation in the SCOPE1 trial.ConclusionsThe SCOPE 1 QA programme outcomes lend support to the trial's clinical conclusions. The range of patient planning outcomes for the benchmark case indicated, at the outset of the trial, the significant degree of variation present in UK oesophageal radiotherapy planning outcomes, despite the presence of a protocol. This supports the case for increasingly detailed definition of practice by means of consensus protocols, training and peer review. The incidence of minor inconsistencies of technique highlights the potential for improved QA systems and the need for sufficient resource for this to be addressed within future trials. As indicated in questionnaire responses, the QA exercise as a whole has contributed to greater consistency of oesophageal radiotherapy in the UK via the adoption into standard practice of elements of the protocol.Trial registrationThe SCOPE1 trial is an International Standard Randomized Controlled Trial, ISRCTN47718479 .

Dataset Information

Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine.

Background

Methods

Results

Conclusions

Publications

Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine.

Similar Datasets

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