Imatinib­induced gynecomastia: A case report.

Chronic myeloid leukemia is a myeloproliferative neoplasm characterized by the unregulated and abnormal proliferation of both mature and immature granulocytes, which results in the proliferation of peripheral blood leukocytes. Imatinib, a tyrosine kinase inhibitor, is the first-line treatment for patients diagnosed with chronic myeloid leukemia. However, despite its favorable safety profile, imatinib use is associated with a number of side effects. Gynecomastia is a rare adverse effect of imatinib treatment and may be associated with an imbalance in sex hormones. The present study reports the case of a patient with chronic myeloid leukemia diagnosed with gynecomastia after imatinib treatment. The aim of the present report was to highlight to clinicians this adverse reaction to imatinib treatment and investigate a treatment strategy with fewer side effects.

Ferulic acid suppresses the inflammation and apoptosis in Kawasaki disease through activating the AMPK/mTOR/NF-κB pathway.

Background: Kawasaki disease (KD) is a self-limiting and acute systemic vasculitis of unknown etiology, mainly affecting children. Ferulic acid (FA), a natural phenolic substance, has multiple pharmacological properties, including anti-inflammatory, anti-apoptosis, and anti-fibrosis, and so on. So far, the protective effects of FA on KD have not been explored. Methods: In this study, we established Candida albicans water soluble fraction (CAWS)-induced mouse coronary artery vasculitis of KD model and the tumor necrosis factor α (TNF-α)-induced human umbilical vein endothelial cells (HUVECs) injury model to investigate the anti-inflammatory and anti-apoptosis effects of FA on KD, and try to elucidate the underlying mechanism. Results: Our in vivo results demonstrated that FA exerted anti-inflammatory effects on KD by inhibiting the infiltration of CD45-positive leukocytes and fibrosis around the coronary artery. Additionally, FA downregulated the levels of inflammatory and chemotactic cytokines, alleviated splenomegaly, and exhibited anti-apoptotic effects on KD by reducing TUNEL-positive cells, downregulating BAX expression, and upregulating BCL-2 expression. In addition, Our in vitro findings showed that FA could effectively inhibit TNF-α-induced HUVEC inflammation like NF-κB inhibitor QNZ by downregulating the expression of pro-inflammatory cytokines as well as attenuated TNF-α-induced HUVEC apoptosis by reducing apoptotic cell numbers and the BAX/BCL-2 ratio, which could be reversed by the AMPK inhibitor compound c (CC). The further mechanistic study demonstrated that FA could restrain vascular endothelial cell inflammation and apoptosis in KD through activating the AMPK/mTOR/NF-κB pathway. However, FA alone is hard to completely restore KD into normal condition. Conclusion: In conclusion, FA has potential protective effects on KD, suggesting its promising role as an adjuvant for KD therapy in the future.

Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN.

Objective. Auto-segmentation in mouse micro-CT enhances the efficiency and consistency of preclinical experiments but often struggles with low-native-contrast and morphologically complex organs, such as the spleen, resulting in poor segmentation performance. While CT contrast agents can improve organ conspicuity, their use complicates experimental protocols and reduces feasibility. We developed a 3D Cycle Generative Adversarial Network (CycleGAN) incorporating anatomy-constrained U-Net models to leverage contrast-enhanced CT (CECT) insights to improve unenhanced native CT (NACT) segmentation.Approach.We employed a standard CycleGAN with an anatomical loss function to synthesize virtual CECT images from unpaired NACT scans at two different resolutions. Prior to training, two U-Nets were trained to automatically segment six major organs in NACT and CECT datasets, respectively. These pretrained 3D U-Nets were integrated during the CycleGAN training, segmenting synthetic images, and comparing them against ground truth annotations. The compound loss within the CycleGAN maintained anatomical fidelity. Full image processing was achieved for low-resolution datasets, while high-resolution datasets employed a patch-based method due to GPU memory constraints. Automated segmentation was applied to original NACT and synthetic CECT scans to evaluate CycleGAN performance using the Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95p).Main results.High-resolution scans showed improved auto-segmentation, with an average DSC increase from 0.728 to 0.773 and a reduced HD95p from 1.19 mm to 0.94 mm. Low-resolution scans benefited more from synthetic contrast, showing a DSC increase from 0.586 to 0.682 and an HD95preduction from 3.46 mm to 1.24 mm.Significance.Implementing CycleGAN to synthesize CECT scans substantially improved the visibility of the mouse spleen, leading to more precise auto-segmentation. This approach shows the potential in preclinical imaging studies where contrast agent use is impractical.

Quantification of Dosimetry Improvement With or Without Patient Surface Guidance.

Purpose: Noncoplanar beams and arcs are routinely used to improve dosimetry for intracranial cases, but their application for extracranial cases has been hampered by the risk of collision. This has led to conservative beam selection whose impact on plan dosimetry has not been previously studied. Methods and Materials: A full-body 3-dimensional patient surface was acquired using optical cameras for a single lung patient at the time of computed tomography simulation. Eight stereotactic body radiation therapy (SBRT) plans were created for the patient, with varying degrees of noncoplanarity and deliverability. The plans included volumetric modulated arc therapy and intensity modulated radiation therapy (IMRT) plans ranging from simple, coplanar arcs to multiple noncoplanar arcs and IMRT beams. A total of 70 fields were created across the 8 plans, of which 21 fields were undeliverable with a 5-cm buffer. Organs-at-risk (OARs) metrics including R50, Dmax 2 cm from the PTV, lung V20, and chest wall V30 were evaluated. Five expert SBRT dosimetrists from 5 institutions evaluated field deliverability, with or without the guidance of the clearance map. Results: In the dosimetry evaluation, a clear trend in increasing dosimetric compactness and OAR sparing is observed with increasing plan noncoplanarity. R50, Dmax 2 cm, lung V20, and chest wall V30 decreased 41%, 39%, 43%, and 57%, respectively, from plan 1 (2 coplanar partial arcs) to plan 8 (19 noncoplanar IMRT beams). In the observer tests, the expert dosimetrists' ability to accurately discern beam deliverability because of collision significantly increases with the clearance map. The errors in predicting colliding fields were eliminated using the whole-body surface and clearance map, and the user was able to select fields based on plan quality and patient comfort instead of being overly conservative. Conclusion: The study shows that incorporating a personalized, whole-body clearance map in the treatment planning workflow can facilitate the adoption of noncoplanar beams or arcs that benefit the SBRT plan dosimetry.

Robust localization of poorly visible tumor in fiducial free stereotactic body radiation therapy.

BACKGROUND AND PURPOSE: Effective respiratory motion management reduces healthy tissue toxicity and ensures sufficient dose delivery to lung cancer cells in pulmonary stereotactic body radiation therapy (SBRT) with high fractional doses. An articulated robotic arm paired with an X-ray imaging system is designed for real-time motion-tracking (RTMT) dose delivery. However, small tumors (<15 mm) or tumors at challenging locations may not be visible in the X-ray images, disqualifying patients with such tumors from RTMT dose delivery unless fiducials are implanted via an invasive procedure. To track these practically invisible lung tumors in SBRT, we hereby develop a deep learning-enabled template-free tracking framework, SAFE Track. METHODS: SAFE Track is a fully supervised framework that trains a generalizable prior for template-free target localization. Two sub-stages are incorporated in SAFE Track, including the initial pretraining on two large-scale medical image datasets (DeepLesion and Node21) followed by fine-tuning on our in-house dataset. A two-stage detector, Faster R-CNN, with a backbone of ResNet50, was selected as our detection network. 94 patients (415 fractions; 40,348 total frames) with low tumor visibility who thus had implanted fiducials were included. The cohort is categorized by the longest dimension of the tumor (<10 mm, 10-15 mm and > 15 mm). The patients were split into training (n = 66) and testing (n = 28) sets. We simulated fiducial-free tumors by removing the fiducials from the X-ray images. We classified the patients into two groups - fiducial implanted inside tumors and implanted outside tumors. To ensure the rigor of our experiment design, we only conducted fiducial removal simulation in training patients and utilized patients with fiducial implanted outside of the tumors for testing. Commercial Xsight Lung Tracking (XLT) and a Deep Match were included for comparison. RESULTS: SAFE Track achieves promising outcomes to as accurate as 1.23±1.32 mm 3D distance in testing patients with tumor size > 15 mm where Deep Match is at 4.75±1.67 mm and XLT is at 12.23±4.58 mm 3D distance. Even for the most challenging tumor size (<10 mm), SAFE Track maintains its robustness at 1.82 plus or minus 1.67 mm 3D distance, where Deep Match is at 5.32 plus or minus 2.32 mm, and XLT is at 24.83±12.95 mm 3D distance. Moreover, SAFE Track can detect some considerably challenging cases where the tumor is almost invisible or overlapped with dense anatomies. CONCLUSION: SAFE Track is a robust, clinically compatible, fiducial-free, and template-free tracking framework that is applicable to patients with small tumors or tumors obscured by overlapped anatomies in SBRT.

Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction.

Objective.We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction.Approach.57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence. The recurrences were manually annotated on the T1ce. The high-risk recurrence region was first determined. Then, a sparse multi-modal feature fusion U-Net was developed. The 50 patients from 3 databases were divided into 70% training, 10% validation, and 20% testing. 7 patients from the 4th institution were used as external testing with transfer learning. Model performance was evaluated by recall, precision, F1-score, and Hausdorff Distance at the 95% percentile (HD95). The proposed MFFE U-Net was compared to the support vector machine (SVM) model and two state-of-the-art neural networks. An ablation study was performed.Main results.The MFFE U-Net achieved a precision of 0.79 ± 0.08, a recall of 0.85 ± 0.11, and an F1-score of 0.82 ± 0.09. Statistically significant improvement was observed when comparing MFFE U-Net with proximity estimation couple SVM (SVMPE), mU-Net, and Deeplabv3. The HD95 was 2.75 ± 0.44 mm and 3.91 ± 0.83 mm for the 10 patients used in the model construction and 7 patients used for external testing, respectively. The ablation test showed that all five MR sequences contributed to the performance of the final model, with T1ce contributing the most. Convergence analysis, time efficiency analysis, and visualization of the intermediate results further discovered the characteristics of the proposed method.Significance. We present an advanced MFFE learning framework, MFFE U-Net, for effective voxel-wise GBM recurrence prediction. MFFE U-Net performs significantly better than the state-of-the-art networks and can potentially guide early RT intervention of the disease recurrence.

Paired conditional generative adversarial network for highly accelerated liver 4D MRI.

Purpose.4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisition with sparse samples is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) to shorten the 4D MRI reconstruction time while maintaining the reconstruction quality.Methods.Patients who underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3×, 6× and 10×) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer (RST), were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D +t) as temporal slices (2D +t). A total of 48 patients with 12 332 temporal slices were split into training (37 patients with 10 721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results.Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15, 0.16, and 120 s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3× Re-Con-GAN 80.98%; 3× CS 80.74%; 3× UNet 79.88%) of unprocessed under-sampled images (3× 69.61%).Conclusion.A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.

Exploring Automated Contouring Across Institutional Boundaries: A Deep Learning Approach with Mouse Micro-CT Datasets.

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt Transformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task, using a hierarchical Swin Transformer encoder to extract features at 5 resolution levels, and connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. Results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of average dice similarity coefficient (DSC) and Hausdorff distance (HD95p), except in two mice of intestine contouring. This superior performance is especially evident in the external dataset, confirming the model's robustness to variations in imaging conditions, including noise and quality, thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows.

Intra-fractional geometric and dose/volume metric variations of magnetic resonance imaging-guided stereotactic radiotherapy of prostate bed after radical prostatectomy.

Background and purpose: Magnetic Resonance Imaging (MRI)-guided Stereotactic body radiotherapy (SBRT) treatment to prostate bed after radical prostatectomy has garnered growing interests. The aim of this study is to evaluate intra-fractional anatomic and dose/volume metric variations for patients receiving this treatment. Materials and methods: Nineteen patients who received 30-34 Gy in 5 fractions on a 0.35T MR-Linac were included. Pre- and post-treatment MRIs were acquired for each fraction (total of 75 fractions). The Clinical Target Volume (CTV), bladder, rectum, and rectal wall were contoured on all images. Volumetric changes, Hausdorff distance, Mean Distance to Agreement (MDA), and Dice similarity coefficient (DSC) for each structure were calculated. Median value and Interquartile range (IQR) were recorded. Changes in target coverage and Organ at Risk (OAR) constraints were compared and evaluated using Wilcoxon rank sum tests at a significant level of 0.05. Results: Bladder had the largest volumetric changes, with a median volume increase of 48.9 % (IQR 28.9-76.8 %) and a median MDA of 5.1 mm (IQR 3.4-7.1 mm). Intra-fractional CTV volume remained stable with a median volume change of 1.2 % (0.0-4.8 %). DSC was 0.97 (IQR 0.94-0.99). For the dose/volume metrics, there were no statistically significant changes observed except for an increase in bladder hotspot and a decrease of bladder V32.5 Gy and mean dose. The CTV V95% changed from 99.9 % (IQR 98.8-100 %) to 99.6 % (IQR 93.9-100 %). Conclusion: Despite intra-fractional variations of OARs, CTV coverage remained stable during MRI-guided SBRT treatments for the prostate bed.

Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge.

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.

Robust treatment planning for small animal radio-neuromodulation using focused kV x-ray beams.

BACKGROUND: In preclinical radio-neuromodulation research, small animal experiments are pivotal for unraveling radiobiological mechanism, investigating prescription and planning techniques, and assessing treatment effects and toxicities. However, the target size inside a rat brain is typically in the order of sub-millimeters. The small target inside the visual cortex neural region in rat brain with a diameter of around 1 mm was focused in this work to observe the physiological change of this region. Delivering uniform doses to the small target while sparing health tissues is challenging. Focused kV x-ray technique based on modern x-ray polycapillary focusing lens is a promising modality for small animal radio-neuromodulation. PURPOSE: The current manual planning method could lead to sub-optimal plans, and the positioning uncertainties due to mechanical accuracy limitations, animal immobilization, and robotic arm motion are not considered. This work aims to design a robust inverse planning method to optimize the intensities of focused kV x-ray beams located in beam trajectories to irradiate small mm-sized targets in rat brains for radio-neuromodulation. METHODS: Focused kV x-ray beams were generated through polycapillary x-ray focusing lenses on achieving small (≤0.3 mm) focus perpendicular to the beam. The beam trajectories were manually designed in 3D space in scanning-while-rotating mode. Geant4 Monte Carlo (MC) simulation generated a dose calculation matrix for each focused kV x-ray beam located in beam trajectories. In the proposed robust inverse planning method, an objective function combining a voxel-wise stochastic programming approach and L1 norm regularization was established to overcome the positioning uncertainties and obtain a high-quality plan. The fast iterative shrinkage thresholding algorithm (FISTA) was utilized to solve the objective function and obtain the optimal intensities. Four cases were employed to validate the feasibility and effectiveness of the proposed method. The manual and non-robust inverse planning methods were also implemented for comparison. RESULTS: The proposed robust inverse planning method achieved superior dose homogeneity and higher robustness against positioning uncertainties. On average, the clinical target volume (CTV) homogeneity index (HI) of robust inverse plan improved to 13.3 from 22.9 in non-robust inverse plan and 53.8 in manual plan if positioning uncertainties were also present. The average bandwidth at D90 was reduced by 6.5 Gy in the robust inverse plan, compared to 9.6 Gy in non-robust inverse plan and 12.5 Gy in manual plan. The average bandwidth at D80 was reduced by 3.4 Gy in robust inverse plan, compared to 5.5 Gy in non-robust inverse plan and 8.5 Gy in manual plan. Moreover, the dose delivery time of manual plan was reduced by an average reduction of 54.7% with robust inverse plan and 29.0% with non-robust inverse plan. CONCLUSION: Compared to manual and non-robust inverse planning methods, the robust inverse planning method improved the dose homogeneity and delivery efficiency and was resistant to the uncertainties, which are crucial for radio-neuromodulation utilizing focused kV x-rays.

Hypoxia-informed RBE-weighted beam orientation optimization for intensity modulated proton therapy.

BACKGROUND: Variable relative biological effectiveness (RBE) models in treatment planning have been proposed to optimize the therapeutic ratio of proton therapy. It has been reported that proton RBE decreases with increasing tumor oxygen level, offering an opportunity to address hypoxia-related radioresistance with RBE-weighted optimization. PURPOSE: Here, we obtain a voxel-level estimation of partial oxygen pressure to weigh RBE values in a single biologically informed beam orientation optimization (BOO) algorithm. METHODS: Three glioblastoma patients with [18 F]-fluoromisonidazole (FMISO)-PET/CT images were selected from the institutional database. Oxygen values were derived from tracer uptake using a nonlinear least squares curve fitting. McNamara RBE, calculated from proton dose, was then weighed using oxygen enhancement ratios (OER) for each voxel and incorporated into the dose fidelity term of the BOO algorithm. The nonlinear optimization problem was solved using a split-Bregman approach, with FISTA as the solver. The proposed hypoxia informed RBE-weighted method (HypRBE) was compared to dose fidelity terms using the constant RBE of 1.1 (cRBE) and the normoxic McNamara RBE model (RegRBE). Tumor homogeneity index (HI), maximum biological dose (Dmax), and D95%, as well as OAR therapeutic index (TI = gEUDCTV /gEUDOAR ) were evaluated along with worst-case statistics after normalization to normal tissue isotoxicity. RESULTS: Compared to [cRBE, RegRBE], HypRBE increased tumor HI, Dmax, and D95% across all plans by on average [31.3%, 31.8%], [48.6%, 27.1%], and [50.4%, 23.8%], respectively. In the worst-case scenario, the parameters increase on average by [12.5%, 14.7%], [7.3%,-8.9%], and [22.3%, 2.1%]. Despite increased OAR Dmean and Dmax by [8.0%, 3.0%] and [13.1%, -0.1%], HypRBE increased average TI by [22.0%, 21.1%]. Worst-case OAR Dmean, Dmax, and TI worsened by [17.9%, 4.3%], [24.5%, -1.2%], and [9.6%, 10.5%], but in the best cases, HypRBE escalates tumor coverage significantly without compromising OAR dose, increasing the therapeutic ratio. CONCLUSIONS: We have developed an optimization algorithm whose dose fidelity term accounts for hypoxia-informed RBE values. We have shown that HypRBE selects bE:\Alok\aaeams better suited to deliver high physical dose to low RBE, hypoxic tumor regions while sparing the radiosensitive normal tissue.

Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.

BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available - fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm - Deep Match. METHOD: Deep Match consists of four self-definable stages - training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10-15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping). RESULTS: On X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients. CONCLUSION: Deep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy.

Minimum reporting standards should be expected for preclinical radiobiology irradiators and dosimetry in the published literature.

The cornerstones of science advancement are rigor in performing scientific research, reproducibility of research findings and unbiased reporting of design and results of the experiments. For radiation research, this requires rigor in describing experimental details as well as the irradiation protocols for accurate, precise and reproducible dosimetry. Most institutions conducting radiation biology research in in vitro or animal models do not have describe experimental irradiation protocols in sufficient details to allow for balanced review of their publication nor for other investigators to replicate published experiments. The need to increase and improve dosimetry standards, traceability to National Institute of Standards and Technology (NIST) standard beamlines, and to provide dosimetry harmonization within the radiation biology community has been noted for over a decade both within the United States and France. To address this requirement subject matter experts have outlined minimum reporting standards that should be included in published literature for preclinical irradiators and dosimetry.

Dosimetric predictors for genitourinary toxicity in MR-guided stereotactic body radiation therapy (SBRT): Substructure with fraction-wise analysis.

BACKGROUND: MR-guided radiation therapy (MRgRT) systems provide superior soft tissue contrast than x-ray based systems and can acquire real-time cine for treatment gating. These features allow treatment planning margins to be reduced, allowing for improved critical structure sparing and reduced treatment toxicity. Despite this improvement, genitourinary (GU) toxicity continues to affect many patients. PURPOSE: (1) To identify dosimetric predictors, potentially in combination with clinical parameters, of GU toxicity following SBRT by leveraging MRgRT to accurately monitor daily dose, beyond predicted dose calculated during planning. (2) Improve awareness of toxicity-sensitive bladder substructures, specifically the trigone and urethra. METHODS: Sixty-nine prostate cancer patients (NCT04384770 clinical trial) were treated on a ViewRay MRIdian MRgRT system, with 40 Gy prescribed to 95% of the PTV in over five fractions. Overall, 17 (24.6%) prostate patients reported acute grade 2 GU toxicity. The CTV, PTV, bladder, bladder wall, trigone, urethra, rectum, and rectal wall were contoured on the planning and daily treatment MRIs. Planning and daily treatment DVHs (0.1 Gy increments), organ doses (min, max, mean), and organ volumes were recorded. Daily dose was estimated by transferring the planning dose distributions to the daily MRI based on the daily setup alignment. Patients were partitioned into a training (55) and testing set (14). Dose features were pre-filtered using a t-test followed by maximum relevance minimum redundancy (MRMR) algorithm. Logistic regression was investigated with regularization to select dosimetric predictors. Specifically, two approaches: time-group least absolute shrinkage and selection (LASSO), and interactive grouped greedy algorithm (IGA) were investigated. Shared features across the planning and five treatment fractions were grouped to encourage consistency and stability. The conventional flat non-temporally grouped LASSO was also evaluated to provide a solid benchmark. After feature selection, a final logistic regression model was trained. Dosimetric regression models were compared to a clinical regression model with only clinical parameters (age, baseline IPSS, prostate gland size, ADT usage, etc.) and a hybrid model, combining the best performing dosimetric features with the clinical parameters, was evaluated. Final model performance was evaluated on the testing set using accuracy, sensitivity, and specificity determined by the optimal threshold of the training set. RESULTS: IGA had the best testing performance with an accuracy/sensitivity/specificity of 0.79/0.67/0.82, selecting 12 groups covering the bladder (V19.8 Gy, V20.5 Gy), bladder wall (19.7 Gy), trigone (15.9, 18.2, 43.3 Gy), urethra (V41.4 Gy, V41.7 Gy), CTV (V41.9 Gy), rectum (V8.5 Gy), and rectal wall (1.2, 44.1 Gy) dose features. Absolute bladder V19.8 Gy and V20.5 Gy were the most important features, followed by relative trigone 15.9  and 18.2 Gy. Inclusion of clinical parameters in the hybrid model with IGA did not significantly change regression performance. CONCLUSION: Overall, IGA feature selection resulted in the best GU toxicity prediction performance. This exploratory study demonstrated the feasibility of identification and analysis of dosimetric toxicity predictors with awareness to sensitive substructures and daily dose to potentially provide consistent and stable dosimetric metrics to guide treatment planning. Further patient accruement is warranted to further assess dosimetric predictor and perform validation.

Impact of Interfractional Bladder and Trigone Displacement and Deformation on Radiation Exposure and Subsequent Acute Genitourinary Toxicity: A Post Hoc Analysis of Patients Treated with Magnetic Resonance Imaging-Guided Prostate Stereotactic Body Radiation Therapy in a Phase 3 Randomized Trial.

PURPOSE: Emerging data suggest that trigone dosimetry may be more associated with poststereotactic body radiation therapy (SBRT) urinary toxicity than whole bladder dosimetry. We quantify the dosimetric effect of interfractional displacement and deformation of the whole bladder and trigone during prostate SBRT using on-board, pretreatment 0.35T magnetic resonance images (MRI). METHODS AND MATERIALS: Seventy-seven patients treated with MRI-guided prostate SBRT (40 Gy/5 fractions) on the MRI arm of a phase 3 single-center randomized trial were included. Bladder and trigone structures were contoured on images obtained from a 0.35T simulation MRI and 5 on-board pretreatment MRIs. Dice similarity coefficient (DSC) scores and changes in volume between simulation and daily treatments were calculated. Dosimetric parameters including Dmax, D0.03 cc, Dmean, V40 Gy, V39 Gy, V38 Gy, and V20 Gy for the bladder and trigone for the simulation and daily treatments were collected. Both physician-scored (Common Terminology Criteria for Adverse Events, version 4.03 scale) as well as patient-reported (International Prostate Symptom Scores and the Expanded Prostate Cancer Index Composite-26 scores) acute genitourinary (GU) toxicity outcomes were collected and analyzed. RESULTS: The average treatment bladder volume was about 30% smaller than the simulation bladder volume; however, the trigone volume remained fairly consistent despite being positively correlated with total bladder volume. Overall, the trigone accounted for <2% of the bladder volume. Median DSC for the bladder was 0.79, whereas the median DSC of the trigone was only 0.33. No statistically significant associations between our selected bladder and trigonal dosimetric parameters and grade ≥2 GU toxicity were identified, although numerically, patients with GU toxicity (grade ≥2) had higher intermediate doses to the bladder (V20 Gy and Dmean) and larger volumes exposed to higher doses in the trigone (V40 Gy, V39 Gy, and V38 Gy). CONCLUSIONS: The trigone exhibits little volume change, but considerable interfractional displacement/deformation. As a result, the relative volume of the trigone receiving high doses during prostate SBRT varies substantially between fractions, which could influence GU toxicity and may not be predicted by radiation planning dosimetry.

[Effects of allicin on insulin resistance and free fatty acid levels in obese rats with periodontitis].

PURPOSE: To explore the effects of allicin on insulin resistance and free fatty acids (FFAs) levels in obese rats with periodontitis. METHODS: Forty rats were randomly divided into healthy group, periodontitis group, and low, medium and high dose groups, with 8 rats in each group. The healthy group was healthy rats, and the other groups were induced by sodium glutamate(MSG). After successfully establishing an obesity model, the maxillary molars were ligated and smeared to establish a periodontitis model. Both the periodontitis group and the healthy group were given normal saline, and the allicin low, medium and high dose groups were given allicin 20，40 and 60 mg·kg-1·d-1, mixed with feed for oral administration. After 21 days of treatment, the fasting blood glucose(FPG), fasting insulin (FINS), insulin resistance index (HOMA-IR) scores and FFAs levels of the homeostatic model in rats were detected. The protein expression of TLR4/MyD88 signaling pathway were compared. Statistical analysis was performed with SPSS 22.0 software package. RESULTS: Compared with the healthy group, FPG, FINS levels, HOMA-IR, IL-6 and TNF-α levels of the periodontitis group were significantly increased, and the expression of TLR4 and MyD88 proteins was significantly increased(P＜0.05). Compared with the periodontitis group, FPG, FINS levels, HOMA-IR, IL-6 and TNF-α levels of low, medium and high-doses groups were significantly decreased, and the expression of TLR4 and MyD88 proteins was significantly decreased (P＜0.05). Compared with the low-dose group, the levels of FPG and FINS, HOMA-IR, IL-6 and TNF-α levels of the middle and high-dose groups were significantly decreased, and the expression of TLR4 and MyD88 proteins was significantly decreased (P＜0.05). Compared with the middle-dose group, the levels of FPG and FINS, HOMA-IR, IL-6 and TNF-α levels of the high-dose group were significantly decreased, and the expression of TLR4 and MyD88 proteins was significantly decreased (P＜0.05). After treatment, FFAs of the low, medium and high-dose groups were significantly lower than those before treatment(P＜0.05). Compared with the healthy group, FFAs levels of the periodontitis group, low-dose and medium-dose groups were significantly increased. Compared with the periodontitis group, FFAs levels of the low, medium and high-dose groups were significantly increased. Compared with the low-dose group, FFAs levels of the high-dose group were significantly increased. Compared with the middle-dose group, FFAs levels of the high-dose group were significantly increased (P＜0.05). CONCLUSIONS: Allicin can improve insulin resistance and obesity in obese rats with periodontitis, and its mechanism of action is related to the TLR4/MyD88 signaling pathway.

First platelet transfusion refractoriness in a patient with acute myelocytic leukemia: A case report.

BACKGROUND: Platelet transfusion is of great significance in the treatment of thrombocytopenia caused by myelosuppression during intensive chemotherapy in patients with acute leukemia. In recent years, with platelet transfusion increasing, ineffective platelet transfusion has become increasingly prominent. Generally speaking, platelet antibodies can be produced after repeated transfusion, thus rendering subsequent platelet transfusion ineffective. We report a case of first platelet transfusion refractoriness (PTR) in a patient with acute myelocytic leukemia (AML). Due to the rarity of such cases in clinical practice, there have been no relevant case reports so far. CASE SUMMARY: A 51-year-old female patient attended the hospital due to throat pain and abnormal blood cells for 4 d. Her diagnosis was acute myelocytic leukemia [M2 type Fms related receptor tyrosine kinase 3, Isocitrate Dehydrogenase 1, Nucleophosmin 1, Neuroblastoma RAS viral oncogene homolog (+) high-risk group]. She was treated with "IA" (IDA 10 mg day 1-3 and Ara-C 0.2 g day 1-5) chemotherapy. When her condition improved, the patient was discharged from the hospital, instructed to take medicine as prescribed by the doctor after discharge, and returned to the hospital for further chemotherapy on time. CONCLUSION: We report a rare case of first platelet transfusion failure in a patient with AML during induction chemotherapy, which may be related to the production of platelet antibodies induced by antibiotics and excessive tumor load. This also suggests that we should consider the influence of antibiotics when the rare situation of first platelet transfusion failure occurs in patients with AML. When platelet antibodies are produced, immunoglobulins can be used to block antibodies, thereby reducing platelet destruction. For patients with PTR, both immune and non-immune factors need to be considered and combined in clinical practice along with individualized treatment to effectively solve the problem.

First report of bacterial leaf spot on Hami melon caused by Pseudomonas aeruginosa in China.

Hami melon (Cucumis melon var. saccharinus) is an economically important crop all over the world. It is being extensively planted in greenhouse in the southwest part of Hainan province, China. A new bacterial leaf spot was observed in a 20 hm2 Hami melon plantation in Huangliu town, Ledong county, Hainan province, in January 2022. The incidence of the disease was approximately 5%. Symptoms were irregularly shaped, brown lesions with yellow haloes on mature leaves, and first appeared as small, dark-green, water-soaking spots. Specimens from the lesion margin were disinfected by submersion in 0.1% mercuric chloride for 1 min, then soaked with 75% alcohol for 30 s, and rinsed with sterilized distilled water. The tissues were then ground in 2 ml of sterile water and allowed to stand for 5min. The supernatant was streaked onto nutrient agar (NA) and incubated for 48h at 30°C. Colonies were round, smooth, colorless, nearly transparent, bead-shaped at first, and then became lightly blue. After being cultured for days on NA at 30℃, the bacteria can turn the media brown. Yellow green pigments (pyoverdin) that fluoresce under ultraviolet light could be produced by the isolates in the Luria Broth. The bacteria were gram-negative, rod shaped with a single polar flagellum, 0.4 to 1.1 × 1.4 to 3.4 µm. Its physiological and biochemical characteristics were as follows: positive for the oxidase, aerobic, arginine dihydrolase, gelatin liquefaction, denitrification, lipase, growth at 41℃, utilization of mannitol, and production of pyocyanin tests; negative for the hydrolysis of starch, levan formation, lecithinase, growth at 4℃, growth in media supplemented with 8.5% NaCl, and utilization of maltose, xylose, and ethylene glycol tests. The 16S rRNA (1,437 bp), gyrB (1,181 bp), and rpoB genes (1,510 bp) were amplified with 27F/1492R (Zhang et al. 2016）, UP-1s/UP-2sr（Hannula M，2007）, and rpoB-F/rpoB-R (Ogier, JC. et al., 2019) primer sets respectively. One of the 5 isolates collected was sequenced. A BLASTn search of GenBank revealed that the sequence of 16S rRNA gene (OQ918303) had 99.7% identity and 98% coverage comparing with the best hit Pseudomonas aeruginosa strain DSM 50071(NR_117678.1), and both gyrB (OR261077) and rpoB (OR261078) had 99.9% identity and over 98% coverage comparing with P. aeruginosa E90 (CP044006.1). A pathogenicity test was conducted by spraying a suspension of the bacteria (108 CFU/mL) onto 10 Hami melon seedlings with two true leaves. Controls were inoculated with sterile water. All inoculated plants were maintained at 28℃ with 80 to 85% relative humidity in a greenhouse. Dark-green, water soaking spots appeared on the cotyledon and stems of treated seedlings 3-5 days after inoculation, and dark green lesions with halos were observed on the true leaves at the same time. Symptoms did not occur on the control plants. The bacteria which were re-isolated from the inoculated plants were confirmed as P. aeruginosa based on the 16S rRNA gene sequence. The bacterium was not isolated from control plants. P. aeruginosa has been reported to cause disease on a variety of plants including tomato (Zhang et al., 2021), poplar (Liu, et al., 2019), ginseng (Gao et al., 2014), tinda (Mondal et al., 2012), onion (Abd-Alla et al., 2011), tobacco (Yu et al., 2008) and sweet basil (Walker et al., 2004). As far as we know, this is the first report of P. aeruginosa causing leaf spot on Hami melon in China.. This report will contribute to the recognition and diagnosis of the new disease for the Hami melon growers.

A unified path seeking algorithm for IMRT and IMPT beam orientation optimization.

Objective. Fully automated beam orientation optimization (BOO) for intensity-modulated radiotherapy and intensity modulated proton therapy (IMPT) is gaining interest, since achieving optimal plan quality for an unknown number of fixed beam arrangements is tedious. Fast group sparsity-based optimization methods have been proposed to find the optimal orientation, but manual tuning is required to eliminate the exact number of beams from a large candidate set. Here, we introduce a fast, automated gradient descent-based path-seeking algorithm (PathGD), which performs fluence map optimization for sequentially added beams, to visualize the dosimetric benefit of one added field at a time.Approach. Several configurations of 2-4 proton and 5-15 photon beams were selected for three head-and-neck patients using PathGD, which was compared to group sparsity-regularized BOO solved with the fast iterative shrinkage-thresholding algorithm (GS-FISTA), and manually selected IMPT beams or one coplanar photon VMAT arc (MAN). Once beams were chosen, all plans were compared on computational efficiency, dosimetry, and for proton plans, robustness.Main results. With each added proton beam, Clinical Target Volume (CTV) and organs at risk (OAR) dosimetric cost improved on average across plans by [1.1%, 13.6%], and for photons, [0.6%, 2.0%]. Comparing algorithms, beam selection for PathGD was faster than GS-FISTA on average by 35%, and PathGD matched the CTV coverage of GS-FISTA plans while reducing OAR mean and maximum dose in all structures by an average of 13.6%. PathGD was able to improve CTV [Dmax, D95%] by [2.6%, 5.2%] and reduced worst-case [max, mean] dose in OARs by [11.1%, 13.1%].Significance. The benefit of a path-seeking algorithm is the beam-by-beam analysis of dosimetric cost. PathGD was shown to be most efficient and dosimetrically desirable amongst group sparsity and manual BOO methods, and highlights the sensitivity of beam addition for IMPT in particular.