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Diisononyl phthalate (DINP) is a synthetic material that has been widely used as a substitute for other plasticizers prohibited due to reproductive toxicity in consumer products. Some phthalates have been associated with testicular dysgenesis syndrome in male fetus when female pregnant dams were exposed to them. The present study investigated effects of DINP on fetal Leydig cell function and testis development. Female pregnant Sprague Dawley rats received control vehicle (corn oil) or DINP (10, 100, 500, and 1000 mg/kg) by oral gavage from gestational day (GD) 12 to 21. At GD 21.5, testicular testosterone production, fetal Leydig cell numbers and distribution, testicular gene and protein expression levels were examined. DINP showed dose-dependent increase of fetal Leydig cell aggregation with the low observed adverse-effect level (LOAEL) of 10 mg/kg and multinucleated gonocyte with LOAEL of 100 mg/kg. At 10 mg/kg, DINP also significantly increased fetal Leydig cell size, but inhibited insulin-like 3 and 3β-hydroxysteroid dehydrogenase gene expression and protein levels. DINP inhibited testicular testosterone levels at 1000 mg/kg. The results indicate that in utero exposure to DINP affects the expression levels of some fetal Leydig cell steroidogenic genes, gonocyte multinucleation and Leydig cell aggregation. [ABSTRACT FROM AUTHOR]

Purpose: The current deep learning-based dose prediction methods only predict one dose distribution. If the predicted dose is inaccurate, no additional options can be selected. To overcome this limitation, we propose a novel dose prediction method called "dose-band prediction," which provides a spectrum of predicted dose distributions for planning and quality assurance (QA) purposes., Material and Methods: We utilized Upper/Lower-band losses in 3D neural networks to establish the Upper/Lower-band models (UBM/LBM). The maximum/minimum rational dose predicted in UBM/LBM defined the ideal dose spectrum for each voxel. We enrolled 104 nasopharyngeal carcinoma cases with tomotherapy (dataset 1), 54 cervical carcinoma cases with IMRT (dataset 2), and 37 cervical carcinoma cases with VMAT (dataset 3) in the study. Moreover, a dose band-based auto planning (Auto-plan dose-band ) attempt was carried out in dataset 3, compared with the MSE model (Auto-plan MSE )., Results: The UBM/LBM doses tend to be higher/lower than the clinical dose, forming a predicted dose spectrum. The Middle-line dose represents the average of the Upper/Lower-band, which was consistent with the clinical dose. The mean differences of the planning target volumes (PTVs) and organs at risk (OARs) for the Upper-band, Middle-line, and Lower-band in Dataset 1 were 3.66 %, -0.40 %, and -4.48 % in Dataset 2, they were 2.40 %, -1.62 %, and -5.57 %; in Dataset 3, they were 2.18 %, -0.59 %, and -3.31 %. When PTVs meet prescription, the mean difference between Auto-plan dose-band and Auto-plan MSE in OARs was -2.67 %., Conclusion: The dose-band prediction successfully predicted a spectrum of doses, making auto-planning and QA flexible and high quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: Accurate beam modelling is essential for dose calculation in stereotactic radiation therapy (SRT), such as CyberKnife treatment. However, the present deep learning methods only involve patient anatomical images and delineated masks for training. These studies generally focus on traditional intensity-modulated radiation therapy (RT) plans. Nevertheless, this paper aims to develop a deep CNN-based method for CyberKnife plan dose prediction about brain cancer patients. It utilized modelled beam information, target delineation, and patient anatomical information., Methods: This study proposes a method that adds beam information to predict the dose distribution of CyberKnife in brain cases. A retrospective dataset of 88 brain and abdominal cancer patients treated with the Ray-tracing algorithm was performed. The datasets include patients' anatomical information (planning CT), binary masks for organs at risk (OARs) and targets, and clinical plans (containing beam information). The datasets were randomly split into 68, 6, and 14 brain cases for training, validation, and testing, respectively., Results: Our proposed method performs well in SRT dose prediction. First, for the gamma passing rates in brain cancer cases, with the 2 mm/2% criteria, we got 96.7% ± 2.9% for the body, 98.3% ± 3.0% for the planning target volume, and 100.0% ± 0.0% for the OARs with small volumes referring to the clinical plan dose. Secondly, the model predictions matched the clinical plan's dose-volume histograms reasonably well for those cases. The differences in key metrics at the target area were generally below 1.0 Gy (approximately a 3% difference relative to the prescription dose)., Conclusions: The preliminary results for selected 14 brain cancer cases suggest that accurate 3-dimensional dose prediction for brain cancer in CyberKnife can be accomplished based on accurate beam modelling for homogeneous tumour tissue. More patients and other cancer sites are needed in a further study to validate the proposed method fully., Advances in Knowledge: With accurate beam modelling, the deep learning model can quickly generate the dose distribution for CyberKnife cases. This method accelerates the RT planning process, significantly improves its operational efficiency, and optimizes it., Competing Interests: This is the first submission of this manuscript and no parts of this manuscript are being considered for publication elsewhere. All authors have approved this manuscript. No author has financial or other contractual agreements that might cause conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

To study an automatic plan(AP) method for radiotherapy after breast-conserving surgery based on TiGRT system and and compare with manual plan (MP). The dosimetry parameters of 10 patients and the evaluation of scoring table were analyzed, it was found that the targets dose of AP were better than that of MP, but there was no statistical difference except for CI, The V5, V20 and V30 of affected lungs and whole lungs in AP were lower than all that in MP, the Dmean of hearts was slightly higher than that of MP, but the difference was not statistically significant, the MU of AP was increase by 16.1% compared with MP, the score of AP evaluation was increase by 6.1% compared with MP. So the AP could be programmed and automated while ensuring the quality of the plan, and can be used to design the plans for radiotherapy after breast-conserving surgery.

Image-guided radiation therapy using magnetic resonance imaging (MRI) is a new technology that has been widely studied and developed in recent years. The technology combines the advantages of MRI imaging, and can offer online real-time tracking of tumor and adjacent organs at risk, as well as real-time optimization of radiotherapy plan. In order to provide a comprehensive understanding of this technology, and to grasp the international development and trends in this field, this paper reviews and summarizes related researches, so as to make the researchers and clinical personnel in this field to understand recent status of this technology, and carry out corresponding researches. This paper summarizes the advantages of MRI and the research progress of MRI linear accelerator (MR-Linac), online guidance, adaptive optimization, and dosimetry-related research. Possible development direction of these technologies in the future is also discussed. It is expected that this review can provide a certain reference value for clinician and related researchers to understand the research progress in the field.

Flash radiotherapy is a kind of radiotherapy method using ultra-high dose rate radiation. Compared with the traditional dose rate radiotherapy, it has unique radiobiological advantages. In this paper, the principle of flash radiotherapy, the process and results of biological experiments are summarized. At the same time, the advantages and challenges of flash radiotherapy are analyzed, and the future clinical application is prospected.