The impact of leaf width and plan complexity on DMLC tracking of prostate intensity modulated arc therapy

Purpose: Intensity modulated arc therapy (IMAT) is commonly used to treat prostate cancer. The purpose of this study was to evaluate the impact of leaf width and plan complexity on dynamic multileaf collimator (DMLC) tracking for prostate motion management during IMAT treatments. Methods: Prostate IMAT plans were delivered with either a high-definition MLC (HDMLC) or a Millennium MLC (M-MLC) (0.25 and 0.50 cm central leaf width, respectively), with and without DMLC tracking, to a dosimetric phantom that reproduced four prostate motion traces. The plan complexity was varied by applying leaf position constraints during plan optimization. A subset of the M-MLC plans was converted for delivery with the HDMLC, isolating the effect of the different leaf widths. The gamma index was used for evaluation. Tracking errors caused by target localization, leaf fitting, and leaf adjustment were analyzed. Results: The gamma pass rate was significantly improved with DMLC tracking compared to no tracking (p < 0.001). With DMLC tracking, the average gamma index pass rate was 98.6% (range 94.8%–100%) with the HDMLC and 98.1% (range 95.4%–99.7%) with the M-MLC, using 3%, 3 mm criteria and the planned dose as reference. The corresponding pass rates without tracking were 87.6% (range 76.2%–94.7%) and 91.1% (range 81.4%–97.6%), respectively. Decreased plan complexity improved the pass rate when static target measurements were used as reference, but not with the planned dose as reference. The main cause of tracking errors was leaf fitting errors, which were decreased by 42% by halving the leaf width. Conclusions: DMLC tracking successfully compensated for the prostate motion. The finer leaf width of the HDMLC improved the tracking accuracy compared to the M-MLC. The tracking improvement with limited plan complexity was small and not discernible when using the planned dose as reference

Dosimetric benefit of DMLC tracking for conventional and sub-volume boosted prostate intensity-modulated arc radiotherapy

This study investigated the dosimetric impact of uncompensated motion and motion compensation with dynamic multileaf collimator (DMLC) tracking for prostate intensity modulated arc therapy. Two treatment approaches were investigated; a conventional approach with a uniform radiation dose to the target volume and an intraprostatic lesion (IPL) boosted approach with an increased dose to a subvolume of the prostate. The impact on plan quality of optimizations with a leaf position constraint, which limited the distance between neighbouring adjacent MLC leaves, was also investigated. Deliveries were done with and without DMLC tracking on a linear acceleration with a high-resolution MLC. A cylindrical phantom containing two orthogonal diode arrays was used for dosimetry. A motion platform reproduced six patient-derived prostate motion traces, with the average displacement ranging from 1.0 to 8.9 mm during the first 75 seconds. A research DMLC tracking system was used for real-time motion compensation with optical monitoring for position input. The gamma index was used for evaluation, with measurements with a static phantom or the planned dose as reference, using 2% and 2 mm gamma criteria. The average pass rate with DMLC tracking was 99.9% (range 98.7–100%, measurement as reference), whereas the pass rate for untracked deliveries decreased distinctly as the average displacement increased, with an average pass rate of 61.3% (range 32.7–99.3%). Dose-volume histograms showed that DMLC tracking maintained the planned dose distributions in the presence of motion whereas traces with > 3 mm average displacement caused clear plan degradation for untracked deliveries. The dose to the rectum and bladder had an evident dependence on the motion direction and amplitude for untracked deliveries, and the dose to the rectum was slightly increased for IPL boosted plans compared to conventional plans for anterior motion with large amplitude. In conclusion, optimization using a leaf position constraint had minimal dosimetric effect, DMLC tracking improved the target and normal tissue dose distributions compared to no tracking for target motion >3 mm, with the DMLC tracking distributions showing generally good agreement between the planned and delivered doses

Pelvic U-Net : multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network

Background: Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. Methods: A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1–4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. Results: Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. Conclusions: Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients