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Purpose: To explore the potential of quantitative parameters of the hydrogel spacer distribution as predictors for separating the rectum from the planning target volume (PTV) in linear‐accelerator‐based stereotactic body radiotherapy (SBRT) for prostate cancer. Methods: Fifty‐five patients underwent insertion of a hydrogel spacer and were divided into groups 1 and 2 of the PTV separated from and overlapping with the rectum, respectively. Prescribed doses of 36.25–45 Gy in five fractions were delivered to the PTV. The spacer cover ratio (SCR) and hydrogel–implant quality score (HIQS) were calculated. Results: Dosimetric and quantitative parameters of the hydrogel spacer distribution were compared between the two groups. For PTV, D99% in group 1 (n = 29) was significantly higher than that in group 2 (n = 26), and Dmax, D0.03cc, D1cc, and D10% for the rectum were significantly lower in group 1 than in group 2. The SCR for prostate (89.5 ± 12.2%) in group 1 was significantly higher (p < 0.05) than that in group 2 (74.7 ± 10.3%). In contrast, the HIQS values did not show a significant difference between the groups. An area under the curve of 0.822 (95% confidence interval, 0.708–0.936) for the SCR was obtained with a cutoff of 93.6%, sensitivity of 62.1%, and specificity of 100%. Conclusions: The SCR seems promising to predict the separation of the rectum from the PTV in linear‐accelerator‐based SBRT for prostate cancer. [ABSTRACT FROM AUTHOR]

Purpose: In recent years, deep learning–based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep learning–based methods heavily relies on the amount of training data; the high cost of acquiring a large data set is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. Methods: The proposed method is based on cycle‐consistency generative adversarial network (CycleGAN) with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several data sets acquired from patients with head and neck cancer. The size of the data sets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. Results: The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring. Conclusions: We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?" [ABSTRACT FROM AUTHOR]

We recently published a phantom validation of our diaphragm tracking system, DiaTrak, on an Elekta linear accelerator with an integrated cone-beam computed tomography (CBCT) unit for multiple breath-hold volumetric modulated arc therapy of abdominal tumors, where the diaphragm position was compared between digitally reconstructed radiography (DRR) and kilovolt (kV) projection streaming images by template matching. In the present report, the visual feedback of the diaphragm position was added to the reported system. DICOM-RT diaphragm contour data were additionally exported from a treatment planning system to the DiaTrak PC. Following phantom localization by registering the CBCT to the planning CT images, a projected diaphragm contour was overlaid on each DRR image, whereas another two projected diaphragm contours were superimposed on each kV projection cine image every 180 ms after shifting ±5 mm (set as breath-hold tolerance) in the craniocaudal direction during gantry rotation. It was visually confirmed that the projected diaphragm surface was observed within the two contour lines on the kV cine window. The diaphragm registration errors of the localized phantom were also calculated based on image cross-correlation between the DRR and the projection cine images every 180 ms. It was found that the mean diaphragm registration error was -0.29 mm with a standard deviation of 0.32 mm during the gantry rotation. In conclusion, a new interface for the 5 mm tolerance check was proposed to provide direct visual feedback, thereby giving a sense of assurance to the attending radiotherapy technologists. The calculated diaphragm registration errors were relatively small compared to the tolerance of 5 mm, and therefore it is considered clinically acceptable., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Nozawa et al.)

We herein report a 75-year-old woman who presented with dyspnea and purpura. She was diagnosed with eosinophilic granulomatosis with polyangiitis (EGPA) based on axonal damage observed in the left tibial nerve and skin and lung pathologies. Lung pathology showed IgG4-positive plasma cells, considered a complication of IgG4-related disease (IgG4-RD). Computed tomography revealed thickening of the abdominal aorta and a poor contrast area in the left kidney, which was indicative of IgG4-RD. Steroid administration improved the IgG4-RD. However, the EGPA resisted treatment; therefore, immunosuppressive drugs and mepolizumab were administered. Refractory EGPA complicated with IgG4-RD showed different treatment responses for each organ.