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, D_mean, 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 D_mean for all patients exceeded tolerance (26 Gy). Among the 21 patients, the planning dose and delivered dose of D_mean 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 D_mean 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 D_mean. Xerostomia is closely related to V₂₀, V₃₀, D₅₀, and D_mean.

Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.

The purpose of this work is to benchmark RapidPlan against clinical plans for liver Intensity-modulated radiotherapy (IMRT) treatment of patients with special anatomical characteristics, and to investigate the prediction capability of the general model (Model-G) versus our specific model (Model-S). A library consisting of 60 liver cancer patients with IMRT planning was used to set up two models (Model-S, Model-G), using the RapidPlan knowledge-based planning system. Model-S consisted of 30 patients with special anatomical characteristics where the distance from planning target volume (PTV) to the right kidney was less than three centimeters and Model-G was configurated using all 60 patients in this library. Knowledge-based IMRT plans were created for the evaluation group formed of 13 patients similar to those included in Model-S by Model-G, Model-S and manually (M), named RPG-plans, RPS-plans and M-plans, respectively. The differences in the dose-volume histograms (DVHs) were compared, not only between RP-plans and their respective M-plans, but also between RPG-plans and RPS-plans. For all 13 patients, RapidPlan could automatically produce clinically acceptable plans. Comparing RP-plans to M-plans, RP-plans improved V95% of PTV and had greater dose sparing in the right kidney. For the normal liver, RPG-plans delivered similar doses, while RPS-plans delivered a higher dose than M-plans. With respect to RapidPlan models, RPS-plans had better conformity index (CI) values and delivered lower doses to the right kidney V20Gy and maximizing point doses to spinal cord, while delivering higher doses to the normal liver. The study shows that RapidPlan can create high-quality plans, and our specific model can improve the CI of PTV, resulting in more sparing of OAR in IMRT for individual liver cancer patients.

For cervical carcinoma cases, this study aimed to evaluate the quality of intensity-modulated radiation therapy (IMRT) plans optimized by biological constraints. Furthermore, a new integrated strategy in biological planning module was proposed and verified. Twenty patients of advanced stage cervical carcinoma were enrolled in this study. For each patient, dose volume optimization (DVO), biological model optimization (BMO) and integrated strategy optimization (ISO) plans were created using same treatment parameters. Different biological models were also used for organ at risk (OAR) in BMO plans, which include the LKB and Poisson models. Next, BMO plans were compared with their corresponding DVO plans, in order to evaluate BMO plan quality. ISO plans were also compared with DVO and BMO plans, in order to verify the performance of the integrated strategy. BMO plans produced slightly inhomogeneity and less coverage of planning target volume (PTV) (V95=96.79, HI = 0.10: p < 0.01). However, the tumor control probability (TCP) value, both from DVO and BMO plans, were comparable. For the OARs, BMO plans produced lower normal tissue complication probability (NTCP) of rectum (NTCP = 0.11) and bladder (NTCP = 0.14) than in the corresponding DVO plans (NTCP = 0.19 and 0.18 for rectum and bladder; p < 0.01 for rectum and p = 0.03 for bladder). V95, D98, CI and HI values that were produced by ISO plans (V95 = 98.31, D98 = 54.18Gy, CI = 0.76, HI = 0.09) were greatly better than BMO plans (V95 = 96.79, D98 = 53.42Gy, CI = 0.71, HI = 0.10) with significant differences. Furthermore, ISO plans produced lower NTCP values of rectum (NTCP = 0.14) and bladder (NTCP = 0.16) than DVO plans (NTCP = 0.19 and 0.18 for rectum and bladder, respectively) with significant differences. BMO plans produced lower NTCP values of OARs compared to DVO plans for cervical carcinoma cases, and resulted in slightly less target coverage and homogeneity. The integrated strategy, proposed in this study, could improve the coverage, conformity and homogeneity of PTV greater than the BMO plans, as well as reduce the NTCP values of OARs greater than the DVO plans.

Glioma refers to a highly prevalent type of brain tumor that is strongly associated with a high mortality rate. During the treatment process of the disease, it is particularly important to accurately perform segmentation of the glioma from Magnetic Resonance Imaging (MRI). However, existing methods used for glioma segmentation usually rely solely on either local or global features and perform poorly in terms of capturing and exploiting critical information from tumor volume features. Herein, we propose a local and global dual transformer with an attentional supervision U-shape network called DTASUnet, which is purposed for glioma segmentation. First, we built a pyramid hierarchical encoder based on 3D shift local and global transformers to effectively extract the features and relationships of different tumor regions. We also designed a 3D channel and spatial attention supervision module to guide the network, allowing it to capture key information in volumetric features more accurately during the training process. In the BraTS 2018 validation set, the average Dice scores of DTASUnet for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions were 0.845, 0.905, and 0.808, respectively. These results demonstrate that DTASUnet has utility in assisting clinicians with determining the location of gliomas to facilitate more efficient and accurate brain surgery and diagnosis.