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A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance
Seiji Tomori1,2, Noriyuki Kadoya2, Yoshiki Takayama2
1Department of Radiology, National Hospital Organization Sendai Medical Center, Sendai, Miyagi, 983-8520, Japan.
Medical Physics
|August 2, 2018
Summary
A deep learning model using convolutional neural networks (CNN) can predict gamma evaluation for patient-specific quality assurance (QA) in prostate cancer radiation therapy. This AI approach offers a faster alternative to traditional measurements, reducing workload for medical physicists.
Area of Science:
- Medical Physics
- Radiotherapy
- Artificial Intelligence
Background:
- Patient-specific quality assurance (QA) is crucial for accurate dose delivery in radiation therapy but is time-consuming.
- Current QA methods create a significant workload for medical physicists and technologists.
- Developing efficient QA methods is essential for improving radiotherapy workflows.
Purpose of the Study:
- To develop and evaluate a deep learning-based prediction model for gamma evaluation in patient-specific QA.
- To assess the practicality of the proposed model using a dataset of prostate cancer treatment plans.
- To reduce the time and workload associated with traditional QA measurements.
Main Methods:
- Sixty intensity-modulated radiation therapy (IMRT) plans for prostate cancer patients were analyzed.
- A 15-layer convolutional neural network (CNN) was trained on sagittal planar dose distributions.
- The model used planning target volume (PTV), rectum volume, overlapping region, and monitor units as input.
- Gamma passing rates (GPR) were measured using EBT3 film and predicted by the CNN at four criteria (2%/2mm, 3%/2mm, 2%/3mm, 3%/3mm).
- Fivefold cross-validation was employed for model validation.
Main Results:
- A strong to moderate correlation was observed between predicted and measured GPR values across all four criteria.
- Spearman rank correlation coefficients in the validation set ranged from 0.65 to 0.74 (P < 0.01).
- Spearman rank correlation coefficients in the test set ranged from 0.32 to 0.62, with most showing statistical significance (P < 0.05).
Conclusions:
- A CNN-based prediction model for patient-specific QA of dose distribution in prostate cancer treatment was successfully developed.
- The findings suggest that deep learning can serve as a valuable tool for gamma evaluation in radiotherapy QA.
- This AI approach has the potential to streamline QA processes and improve efficiency in radiation oncology.
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