Various therapeutic approaches have been studied for the treatment of Duchenne muscular dystrophy (DMD), but none of these approaches have led to significant long-term effects in patients. One reason for this observed inefficacy may be the use of inappropriate animal models for the testing of therapeutic agents. The mdx mouse is the most widely used murine model of DMD, yet it does not model the fibrotic progression observed in patients. Other murine models of DMD are available that lack one or both alleles of utrophin, a functional analog of dystrophin. The aim of this study was to compare fibrosis and myofiber damage in the mdx, mdx/utrn+/- and double knockout (dko) mouse models. We used Masson’s trichrome stain and percentage of centrally-nucleated myofibers as indicators of fibrosis and myofiber regeneration, respectively, to assess disease progression in diaphragm and gastrocnemius muscles harvested from young and aged wild-type, mdx, mdx/utrn+/- and dko mice. Our results indicated that eight week-old gastrocnemius muscles of both mdx/utrn+/- and dko hind limb developed fibrosis whereas age-matched mdx gastrocnemius muscle did not (p = 0.002). The amount of collagen found in the mdx/utrn+/- diaphragm was significantly higher than that found in the corresponding diaphragm muscles of wild-type animals, but not of mdx animals (p = 0.0003). Aged mdx/utrn+/- mice developed fibrosis in both diaphragm and gastrocnemius muscles compared to wild-type controls (p = 0.003). Mdx diaphragm was fibrotic in aged mice as well (p = 0.0235), whereas the gastrocnemius muscle in these animals was not fibrotic. We did not measure a significant difference in collagen staining between wild-type and mdx gastrocnemius muscles. The results of this study support previous reports that the moderately-affected mdx/utrn+/- mouse is a better model of DMD, and we show here that this difference is apparent by 2 months of age.

A set of computer-controlled motorized jaws for a micro-CT∕RT system were constructed with position reliably better than a tenth of a millimeter. The hardware system is ready for image-guided conformal radiotherapy for small animals with capability of respiratory-gated delivery.

Previous work has indicated HDR-BT needles may be manually segmented using SR3D images with insertion depth errors ≤3 mm and ≤5 mm for 83% and 92% of needles, respectively. The algorithm shows promise for reducing the time required for the segmentation of straight HDR-BT needles, and future work involves improving needle tip localization performance through improved image quality and modeling curvilinear trajectories.

Purpose To develop and evaluate a volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) approach based on deep‐Q reinforcement learning (RL) capable of finding an optimal machine control policy using previous prostate cancer patient CT scans and contours, and applying the policy to new cases to rapidly produce deliverable VMAT plans in a simplified beam model. Methods A convolutional deep‐Q network was employed to control the dose rate and multileaf collimator of a C‐arm linear accelerator model using the current dose distribution and machine parameter state as input. A Q‐value was defined as the discounted cumulative cost based on dose objectives, and experience‐replay RL was performed to determine a policy to minimize the Q‐value. A two‐dimensional network design was employed which optimized each opposing leaf pair independently while monitoring the corresponding dose plane blocked by those leaves. This RL approach was applied to CT and contours from 40 retrospective prostate cancer patients. The dataset was split into training (15 patients) and validation (5 patients) groups to optimize the network, and its performance was tested in an independent cohort of 20 patients by comparing RL‐based dose distributions to conformal arcs and clinical intensity modulated radiotherapy (IMRT) delivering a prescription dose of 78 Gy in 40 fractions. Results Mean ± SD execution time of the RL VMAT optimization was 1.5 ± 0.2 s per slice. In the test cohort, mean ± SD (P‐value) planning target volume (PTV), bladder, and rectum dose were 80.5 ± 2.0 Gy (P < 0.001), 44.2 ± 14.6 Gy (P < 0.001), and 43.7 ± 11.1 Gy (P < 0.001) for RL VMAT compared to 81.6 ± 1.1 Gy, 51.6 ± 12.9 Gy, and 36.0 ± 12.3 Gy for clinical IMRT. Conclusions RL was applied to VMAT MPO using clinical patient contours without independently optimized treatment plans for training and achieved comparable target and normal tissue dose to clinical plans despite the application of a relatively simple network design originally developed for video‐game control. These results suggest that extending a RL approach to a full three‐dimensional beam model could enable rapid artificial intelligence‐based optimization of deliverable treatment plans, reducing the time required for radiotherapy planning without requiring previous plans for training.

No abstract