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Abstract The advancement of precision radiotherapy techniques, such as volumetric modulated arc therapy (VMAT), stereotactic body radiotherapy (SBRT), and particle therapy, highlights the importance of radiotherapy in the treatment of cancer, while also posing challenges for respiratory motion management in thoracic and abdominal tumors. MRI-guided radiotherapy (MRIgRT) stands out as state-of-art real-time respiratory motion management approach owing to the non-ionizing radiation nature and superior soft-tissue contrast characteristic of MR imaging. In clinical practice, MR imaging often operates at a frequency of 4 Hz, resulting in approximately a 300 ms system latency of MRIgRT. This system latency decreases the accuracy of respiratory motion management in MRIgRT. Artificial intelligence (AI)-based respiratory motion prediction has recently emerged as a promising solution to address the system latency issues in MRIgRT, particularly for advanced contour prediction and volumetric prediction. However, implementing AI-based respiratory motion prediction faces several challenges including the collection of training datasets, the selection of prediction methods, and the formulation of complex contour and volumetric prediction problems. This review presents modeling approaches of AI-based respiratory motion prediction in MRIgRT, and provides recommendations for achieving consistent and generalizable results in this field.

This paper describes the design of an innovative linear accelerator image-guided radiosurgery (IGRS) device, which is based on a composite twofold rotary gantry structure. The paper discusses five aspects of the innovative device: its overall composition, the safety net space created by the accelerator radiation head as it rotates around the patient’s longitudinal axis, the non-coplanar spherical coverage in the direction of the incidence angle for quasi-4π delivery, the structural features of the composite twofold rotary gantry, and the processes of treatment planning and implementation. It elaborates on the device’s manufacturing feasibility, safety, effectiveness, accuracy, and efficiency. The conclusion is that this innovative device design holds significant development value and market promotion potential.

Purpose: To assess the planned dose, in vivo dosimetry, acute skin toxicity, pain, and distress using Thermoplastic Elastomer (TPE) bolus for postmastectomy radiotherapy (PMRT). Material and methods: Thirty-two PMRT patients with TPE bolus (17 patients for 25 fractions, 15 patients for the first 20 fractions) were selected for the study. The acute skin toxicity, pain, and psychological distress were assessed from the first treatment week to the fourth week after the end of treatment. At the first treatment, the MOSFET was used in vivo dosimetry measurement. Results: In vivo dosimetry with the bolus, the dose deviation ranged from −6.22% to −1.56% for 5 points. The presence of grade 1 and 2 skin toxicity reached its peak (70.0% and 13.3%) in the sixth week. Two patients (6.6%) with 25 fractions bolus experienced moist desquamation in the fifth and seventh week, with pain score 2 and 3, and interruptions of 3 and 5 days, respectively. The incidence of pain score 1, 2, and 3 peaked in the fifth (33.3%), fourth (33.3%), and seventh (10.0%) week. No patients experienced grade 3 skin toxicity and severe pain. One patient had significant anxiety, and two patients had significant depression. Conclusion: The TPE bolus can accurately fit skin and improve the surface dose to more than 90%. Twenty fractions with TPE bolus had similar skin toxicity and pain to those without bolus and did not increase patients' distress and clinical workload, compared with the literature's data, which is an alternative to the 3D printing bolus for PMRT.

Abstract Background Prostate alignment is subject to interobserver variability in cone-beam CT (CBCT)-based soft-tissue matching. This study aims to analyze the impact of possible interobserver variability in CBCT-based soft-tissue matching for prostate cancer radiotherapy. Methods Retrospective data, consisting of 156 CBCT images from twelve prostate cancer patients with elective nodal irradiation were analyzed in this study. To simulate possible interobserver variability, couch shifts of 2 mm relative to the resulting patient position of prostate alignment were assumed as potential patient positions (27 possibilities). For each CBCT, the doses of the potential patient positions were re-calculated using deformable image registration-based synthetic CT. The impact of the simulated interobserver variability was evaluated using tumor control probabilities (TCPs) and normal tissue complication probabilities (NTCPs). Results No significant differences in TCPs were found between prostate alignment and potential patient positions (0.944 ± 0.003 vs 0.945 ± 0.003, P = 0.117). The average NTCPs of the rectum ranged from 5.16 to 7.29 (%) among the potential patient positions and were highly influenced by the couch shift in the anterior–posterior direction. In contrast, the average NTCPs of the bladder ranged from 0.75 to 1.12 (%) among the potential patient positions and were relatively negligible. Conclusions The NTCPs of the rectum, rather than the TCPs of the target, were highly influenced by the interobserver variability in CBCT-based soft-tissue matching. This study provides a theoretical explanation for daily CBCT-based image guidance and the prostate-rectum interface matching procedure. Trial registration: Not applicable.

Objectives: We identified functional genes and studied the underlying molecular mechanisms of diabetic cardiomyopathy (DCM) using bioinformatics tools., Methods: Original gene expression profiles were obtained from the GSE21610 and GSE112556 data sets. We used GEO2R to screen the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on DEGs. Protein-protein interaction (PPI) networks of DEGs were constructed using STRING and hub genes of signaling pathways were identified using Cytoscape. Aberrant hub gene expression was verified using The Cancer Genome Atlas data set., Results: The DEGs in DCM were mainly enriched in the nuclei and cytoplasm and involved in DCM and chemokine-related signaling pathways. In the PPI network, 32 nodes were chosen as hub nodes and an RNA interaction network was constructed with 517 interactions. The expression of key genes (JPIK3R1, CCR9, XIST, WDFY3.AS2, hsa-miR-144-5p, and hsa-miR-146b-5p) was significantly different between DCM and normal tissues., Conclusions: The identified hub genes could be associated with DCM pathogenesis and could be used for treating DCM., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)