A robust evaluation of 49 high-dose-rate prostate brachytherapy treatment plans including all major uncertainties.

BACKGROUND: Uncertainties in radiotherapy cause deviation from the planned dose distribution and may result in delivering a treatment that fails to meet clinical objectives. The impact of uncertainties is unique to the patient anatomy and the needle locations in HDR prostate brachytherapy. Evaluating this impact during treatment planning is not common practice, relying on margins around the target or organs-at-risk to account for uncertainties. PURPOSE: A robust evaluation framework for HDR prostate brachytherapy treatment plans was evaluated on 49 patient plans, measuring the range of possible dosimetric outcomes to the patient due to 14 major uncertainties. METHODS: Patient plans were evaluated for their robustness to uncertainties by simulating probable uncertainty scenarios. Five-thousand probabilistic and 1943 worst-case scenarios per patient were simulated by changing the position and size of structures and length of dwell times from their nominal values. For each uncertainty scenario, the prostate D90 and maximum doses to the urethra, D0.01cc , and rectum, D0.1cc , were calculated. RESULTS: The D90 was an average 1.16 ± 0.51% (mean ± SD) below nominal values for the probabilistic scenarios; the D0.01cc metric was 2.24 ± 0.90% higher; and D0.1cc was greater by 0.48 ± 0.30%. The D0.01cc and D90 metrics were more sensitive to uncertainties than D0.1cc , with a median of 79.0% and 84.9% of probabilistic scenarios passing the constraints, compared to 96.5%. The median pass-rate for scenarios that passed all three metrics simultaneously was 63.4%. CONCLUSIONS: Assessing treatment plan robustness improves plan quality assurance, is achievable in less than 1-min, and identifies treatment plans with poor robustness, allowing re-optimization before delivery.

Can optical scanning technologies replace CT for 3D printed medical devices in radiation oncology?

3D printing is being increasingly adopted in radiation oncology for printing highly conformal medical devices for treatment. Optical surface reconstruction technologies have been shown to be useful for 3D printing applications due to their higher spatial resolution, non-ionising radiation imaging and will likely supplement existing radiographic imaging techniques in the future.

DeepWL: Robust EPID based Winston-Lutz analysis using deep learning, synthetic image generation and optical path-tracing.

Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality assurance test is the Winston-Lutz test which localises the radiation isocentre of the linac. In the current work we demonstrate a novel method of analysing EPID based Winston-Lutz QA images using a deep learning model trained only on synthetic image data. In addition, we propose a novel method of generating the synthetic WL images and associated 'ground-truth' masks using an optical path-tracing engine to 'fake' mega-voltage EPID images. The model called DeepWL was trained on 1500 synthetic WL images using data augmentation techniques for 180 epochs. The model was built using Keras with a TensorFlow backend on an Intel Core i5-6500T CPU and trained in approximately 15 h. DeepWL was shown to produce ball bearing and multi-leaf collimator field segmentations with a mean dice coefficient of 0.964 and 0.994 respectively on previously unseen synthetic testing data. When DeepWL was applied to WL data measured on an EPID, the predicted mean displacements were shown to be statistically similar to the Canny Edge detection method. However, the DeepWL predictions for the ball bearing locations were shown to correlate better with manual annotations compared with the Canny edge detection algorithm. DeepWL was demonstrated to analyse Winston-Lutz images with an accuracy suitable for routine linac quality assurance with some statistical evidence that it may outperform Canny Edge detection methods in terms of segmentation robustness and the resultant displacement predictions.