No abstract

Xerostomia induced by radiotherapy is a common toxicity for head and neck carcinoma patients. In this study, the deformable image registration of planning computed tomography (CT) and weekly cone‐beam CT (CBCT) was used to override the Hounsfield unit value of CBCT, and the modified CBCT was introduced to estimate the radiation dose delivered during the course of treatment. Herein, the beams from each patient's treatment plan were applied to the modified CBCT to construct the weekly delivered dose. Then, weekly doses were summed together to obtain the accumulated dose. A total of 42 parotid glands (PGs) of 21 nasopharyngeal carcinoma patients were analyzed. Doses delivered to the parotid glands significantly increased compared with the planning doses. V20, V30, V40, Dmean, and D50 increased by 11.3%, 28.6%, 44.4%, 9.5%, and 8.4% respectively. Of the 21 patients included in the study, eight developed xerostomia and the remaining 13 did not. Both planning and delivered PG Dmean for all patients exceeded tolerance (26 Gy). Among the 21 patients, the planning dose and delivered dose of Dmean were 30.6 Gy and 33.6 Gy, respectively, for patients with xerostomia, and 26.3 Gy and 28.0 Gy, respectively, for patients without xerostomia. The D50 of the planning and delivered dose for patients was below tolerance (30 Gy). The results demonstrated that the p‐value of V20, V30, D50, and Dmean difference of the delivery dose between patients with xerostomia and patients without xerostomia was less than 0.05. However, for the planning dose, the significant dosimetric difference between the two groups only existed in D50 and Dmean. Xerostomia is closely related to V20, V30, D50, and Dmean.

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Purpose One of the promising options for motion management in radiation therapy (RT) is the use of LINAC‐compatible robotic‐arm‐mounted ultrasound imaging system due to its high soft tissue contrast, real‐time capability, absence of ionizing radiation, and low cost. The purpose of this work is to develop a novel deep learning‐based real‐time motion tracking strategy for ultrasound image‐guided RT. Methods The proposed tracker combined the attention‐aware fully convolutional neural network (FCNN) and the convolutional long short‐term memory network (CLSTM) that is end‐to‐end trainable. The glimpse sensor module was built inside the attention‐aware FCNN to discard majority of background by focusing on a region containing the object of interest. FCNN extracted discriminating spatial features of glimpse to facilitate temporal modeling for CLSTM. The saliency mask computed from CLSTM refined the features particular to the tracked landmarks. Moreover, the multitask loss strategy including bounding box loss, localization loss, saliency loss, and adaptive loss weighting term was utilized to facilitate training convergence and avoid over/underfitting. The tracker was tested on the databases provided by MICCAI 2015 challenges, and the ground truth data were obtained with the help of brute force‐based template matching technology. Results The mean tracking error of 0.97 ± 0.52 mm and maximum tracking error of 1.94 mm were observed for 85 point landmarks across 39 ultrasound cases compared to the ground truth annotations. The tracking speed per frame per landmark with the GPU implementation ranged from 66 and 101 frames per second, which largely exceeded the ultrasound imaging rate. Conclusion The results demonstrated the robustness and accuracy of the proposed deep learning‐based motion estimation, despite the existence of some known shortcomings of ultrasound imaging such as speckle noise. The tracking speed of the system was found to be remarkable, sufficiently fast for real‐time applications in RT environment. The approach provides a valuable tool to guide RT treatment with beam gating or multileaf collimator (MLC) tracking in real time.

To explore the mechanism of drug release and depot formation of in situ forming implants (ISFIs), osthole-loaded ISFIs were prepared by dissolving polylactide, poly(lactide-co-glycolide), polycaprolactone, or poly(trimethylene carbonate) in different organic solvents, including N-methyl-2-pyrrolidone (NMP), dimethyl sulfoxide (DMSO), and triacetin (TA). Drug release, polymer degradation, solvent removal rate and depot microstructure were examined. The burst release effect could be reduced by using solvents exhibit slow forming phase inversion and less permeable polymers. Both the drug burst release and polymer depot microstructure were closely related to the removal rate of organic solvent. Polymers with higher permeability often displayed faster drug and solvent diffusion rates. Due to high polymer-solvent affinity, some of the organic solvent remained in the depot even after the implant was completely formed. The residual of organic solvent could be predicted by solubility parameters. The ISFI showed a lower initial release in vivo than that in vitro. In summary, the effects of different polymers and solvents on drug release and depot formation in ISFI systems were extensively investigated and discussed in this article. The two main factors, polymer permeability and solvent removal rate, were involved in different stages of drug release and depot formation in ISFI systems.