Accurate Radiation Research Articles

The proton arc therapy treatment planning problem is NP-Hard

Proton arc therapy (PAT) is an advanced radiotherapy technique using charged particles in which the radiation device rotates continuously around the patient while irradiating the tumor. Compared to conventional, fixed-angle beam delivery mode, proton arc therapy has the potential to further improve the quality of cancer treatment by delivering accurate radiation dose to tumors while minimizing damage to surrounding healthy tissues. However, the computational complexity of treatment planning in PAT raises challenges as to its effective implementation. In this paper, we demonstrate that designing a PAT plan through algorithmic methods is a NP-hard problem (in fact, NP-complete), where the problem size is determined by the number of discrete irradiation angles from which the radiation can be delivered. This finding highlights the inherent complexity of PAT treatment planning and emphasizes the need for efficient algorithms and heuristics to address the challenges associated with optimizing the delivery of radiation doses in this context.

Performance of an offline systematic error correction strategy in pediatric patients receiving adjuvant conformal radiotherapy for Wilm's tumor.

Radiotherapy plays a key role as an adjuvant treatment in pediatric Wilm's tumor, improving both survival and quality of life. The success of radiotherapy depends on the precise delivery of radiation dose to the tumor while sparing radiosensitive structures in the vicinity of the tumor. Pediatric patients pose unique challenges in achieving accurate radiotherapy delivery due to their inability to understand instructions and the high radiosensitivity of their tissues. Thus, it is important to determine the optimum geometric verification strategy that will ensure accurate delivery of the prescribed target as specified in the patient's treatment plan. To evaluate the performance of an offline geometric correction strategy in ensuring accuracy and reproducibility during radiotherapy delivery in Wilm's tumor patients. The extended no-action level offline correction strategy was applied in the radiotherapy delivery of 45 Wilm's tumor patients. Gross errors from the first three fractions were used to calculate the mean errors which were then applied as offline correction factors. Mean errors among different groups were compared using a two-way analysis of variance (ANOVA) and Dunnett's pairwise comparisons. All statistical analyses and data visualization were performed using GraphPad Prism version 7 (Insight Partners, GraphPad Holdings, LLC). A total of 45 patients were included in the study. In all three orthogonal directions, the recorded gross errors were significantly lower after the application of the systematic error corrections. Random errors were significantly larger in the longitudinal direction compared to lateral (mean difference = 0.28, p = 0.036) and vertical directions (mean difference = 0.37 cm, p = 0.003). Patients' age was a significant predictor of random errors whereby the magnitude of random error decreased with increasing age. This study shows that the offline correction strategy used is effective in ensuring the accuracy of radiotherapy delivery in pediatric Wilm's tumor patients.

Beginning of clinical treatment using the 1.5 Tesla MR-Linac system in Japan: a narrative review.

In the field of radiation therapy, image-guided radiotherapy (IGRT) technology has been gradually improving and highly accurate radiation treatment has been possible. Research on IGRT using 1.5 Tesla magnetic resonance imaging (MRI) began in 1999, and a radiation therapy device called 1.5 Tesla magnetic resonance linear accelerator (MR-Linac), which combines a linear accelerator with 1.5 Tesla MRI, was developed in Europe. The aim of this review is to present an overview of 1.5 Tesla MR-Linac with a review of the literature and our experience. Reports related to 1.5 Tesla MR-Linac were searched for in PubMed and are discussed in relation to our experience. The 1.5 Tesla MR-Linac enables IGRT using 1.5 Tesla MRI, further enhancing the precision of radiation therapy. Position verification by cone-beam computed tomography (CBCT) is performed in many institutions, but soft tissue contrast is often unclear in CBCT images of the abdomen and mediastinal organs. Since the 1.5 Tesla MR-Linac allows position verification using MRI, position verification can be performed using clear MRI even in regions where CBCT is unclear. With the 1.5 Tesla MR-Linac, it is possible to perform online adaptive radiotherapy (ART) using 1.5 Tesla MRI. Online ART is a method in which images are acquired while the patient is on the treatment table. The method is based on the current condition of the organs in the body on that day and an optimal treatment field is recreated. Additionally, it allows monitoring of tumor motion using cine images obtained by 1.5 Tesla MRI during the delivery of X-ray radiation. A previous report showed that patients with prostate cancer who received radiotherapy by MR-Linac had fewer side effects than those in patients who received conventional CBCT radiation therapy. The 1.5 Tesla MR-Linac obtained CE-mark certification in Europe in August 2018 and it has been used for clinical treatment. In Japan, clinical treatment using this device started in 2021. By using 1.5 Tesla MR-Linac, patients can be provided with higher precision radiotherapy. In this review, we provide an overview of 1.5 Tesla MR-Linac.

Retrospective study on the toxicity induced by stereotactic body radiotherapy: overview of the reunion experience on prostate cancer in elderly patients.

Prostate cancer is the fourth most commonly diagnosed cancer among men worldwide. Various tools are used to manage disease such as conventional radiotherapy. However, it has been demonstrated that large prostate volumes were often associated with higher rates of genitourinary and gastrointestinal toxicities. Currently, the improvements in radiotherapy technology have led to the development of stereotactic body radiotherapy, which delivers higher and much more accurate radiation doses. In order to complete literature data about short-term outcome and short-term toxic effects of stereotactic body radiotherapy, we aimed to share our experience about gastrointestinal and genitourinary toxicities associated with stereotactic body radiotherapy in prostate cancer in patients over 70 years old. We retrospectively reviewed the medical records of elderly patients with prostate cancer treated between 2021 and 2022. The elderly patients were treated with a non-coplanar robotic stereotactic body radiotherapy platform using real-time tracking of implanted fiducials. The prostate, with or without part of the seminal vesicles, was treated with a total dose of 36.25 Gy delivered in five fractions, each fraction being administered every other day. We analyzed a total of 80 elderly patients, comprising 38 low-, 37 intermediate- and 5 high-risk patients. The median follow-up duration was 12 months. We did not observe biochemical/clinical recurrence, distant metastasis, or death. Grade 2 acute genitourinary toxicity was observed in 9 patients (11.25%) and Grade 2 acute gastrointestinal toxicity in 4 patients (5.0%). We did not observe any grade 3 or more acute or late toxicities. Over the follow-up period, we noted a low frequency of gastrointestinal and genitourinary toxicities induced by stereotactic body radiotherapy in the context of prostate cancer in elderly patients. Therefore, stereotactic body radiotherapy seems to represent a promising treatment option for elderly patients, with acceptable acute toxicity.

The Role of Lung Density in the Voxel-Based Dosimetry of 90Y-TARE Evaluated with the Voxel S-Value (VSV) Method and Fast Monte Carlo Simulation

(1) Background: In 90Y-TARE treatments, lung-absorbed doses should be calculated according to the manufacturer’s instructions, using the MIRD-scheme. This scheme is derived from the assumption that 90Y-microspheres deliver the dose in a water-equivalent medium. Since the density of the lungs is quite different from that of the liver, the absorbed dose to the lungs could vary considerably, especially at the liver/lungs interface. The aim of this work is to compare the dosimetric results obtained by two dedicated software packages implementing a water-equivalent dose calculation and a Monte Carlo (MC) simulation, respectively. (2) Methods: An anthropomorphic IEC phantom and a retrospective selection of 24 patients with a diagnosis of HCC were taken into account. In the phantom study, starting from a 90Y-PET/CT acquisition, the liver cavity was manually fixed with a uniform activity concentration on PET series, while the lung compartment was manually expanded on a CT series to simulate a realistic situation in which the liver and lungs are adjacent. These steps were performed by using MIM 90Y SurePlan. Then, a first simulation was carried out with only the liver cavity filled, while a second one was carried out, in which the lung compartment was also manually fixed with a uniform activity concentration corresponding to 10% lung shunt fraction. MIM 90Y SurePlan was used to obtain Voxel S-Value (VSV) approach dose values; instead, Torch was used to obtain MC approach dose values for both the phantom and the patients. (3) Results: In the phantom study, the percentage mean dose differences (∆D%) between VSV and MC in the first and second simulation, respectively were found to be 1.2 and 0.5% (absolute dose variation, ∆D, of 0.7 and 0.3 Gy) for the liver, −56 and 70% (∆D of −0.3 and −16.2 Gy) for the lungs, and −48 and −60% (∆D of −4.3 and −16.5 Gy) for the Liver/Lungs Edge region. The patient study reports similar results with ∆D% between VSV and MC of 7.0%, 4.1% and 6.7% for the whole liver, healthy liver, and tumor, respectively, while the result was −61.2% for the left lung and −61.1% for both the right lung and lungs. (4) Conclusion: Both VSV and MC allowed accurate radiation dose estimation with small differences (<7%) in regions of uniform water-equivalent density (i.e., within the liver). Larger differences between the two methods (>50%) were observed for air-equivalent regions in the phantom simulation and the patient study.

Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data

Objective. As the most common solution to motion artefact for cone-beam CT (CBCT) in radiotherapy, 4DCBCT suffers from long acquisition time and phase sorting error. This issue could be addressed if the motion at each projection could be known, which is a severely ill-posed problem. This study aims to obtain the motion at each time point and motion-free image simultaneously from unsorted projection data of a standard 3DCBCT scan. Approach. Respiration surrogate signals were extracted by the Intensity Analysis method. A general framework was then deployed to fit a surrogate-driven motion model that characterized the relation between the motion and surrogate signals at each time point. Motion model fitting and motion compensated reconstruction were alternatively and iteratively performed. Stochastic subset gradient based method was used to significantly reduce the computation time. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on clinical scans from six patients. Results. For digital phantom experiments, motion models fitted with ground-truth or extracted surrogate signals both achieved a much lower motion estimation error and higher image quality, compared with non motion-compensated results.For the public SPARE Challenge datasets, more clear lung tissues and less blurry diaphragm could be seen in the motion compensated reconstruction, comparable to the benchmark 4DCBCT images but with a higher temporal resolution. Similar results were observed for two real clinical 3DCBCT scans. Significance. The motion compensated reconstructions and motion models produced by our method will have direct clinical benefit by providing more accurate estimates of the delivered dose and ultimately facilitating more accurate radiotherapy treatments for lung cancer patients.

GRACE-FO radiation pressure modelling for accurate density and crosswind retrieval

Uncertainties in radiation pressure modelling play a significant role in the thermospheric density and crosswind observations derived from the GRACE-FO accelerometer, especially during low solar activity. Under such conditions, the radiation pressure acceleration matches the magnitude of the aerodynamic acceleration along the track and exceeds it in the cross-track direction. The GRACE-FO mission has been operating for several years at such high altitudes during both low and rising solar activity, providing a perfect opportunity to study the effects of radiation pressure. This research uses ray tracing based on a high-fidelity satellite geometry model to calculate the radiation pressure acceleration. We numerically fine-tuned the coefficients describing the thermo-optical surface properties to obtain more accurate radiation pressure accelerations than those specified in the GRACE-FO mission manual. We also used in situ temperature measurements from thermistors on the solar arrays to model the satellite’s thermal emission. These temperature measurements allowed a realistic setup of the thermal model, extended by the parameter describing the efficiency of the solar cells, and reproduced the acceleration of the thermal emission with an accuracy of RMS 0.148 nms−2. The combination of the updated thermal model and the fine-tuning of the surface coefficients improved the accuracy of the crosswind acceleration to an RMS of 0.55 nms−2, compared to an RMS of 4.22 nms−2 when using panel models and instantaneous thermal radiation. We compared the observed crosswind with two models: HWM14 and TIE-GCM. While both models capture most of the salient features of the observed crosswind, HWM14 shows particularly good agreement at high latitudes. Compared to the previously employed radiation pressure model, the crosswind observations have been improved in low and mid-latitudes, especially during periods of higher solar activity. Since the effect of radiation pressure is most significant in the crosswind direction, the effect on density was small compared to previously published datasets.

Optimisation of the performance of a prototype instrument for measuring solar irradiation using artificial neural network

Accurate measurement of solar radiation is essential for understanding climate patterns, assessing solar energy potential, and predicting weather conditions. Over the years, solar radiation measuring instruments such as pyranometers and solarimeters have been used to achieve this objective. However, the high cost of these standard instruments makes the technology less accessible, especially to students and academic researchers in low-to-middle-income countries. A low-cost prototype solarimeter has been developed that operates using a mini solar PV panel and a microcontroller. During testing, it was found that as temperature increased the instrument had significant accuracy deviations. As such, this study seeks to optimise the performance of the prototype solarimeter using Artificial Neural Networks (ANNs), a powerful data-driven machine learning tool for optimisation. Solar radiation data was simultaneously collected using the prototype solarimeter and a standard solarimeter. Corresponding ambient temperature was also recorded for each solar radiation measurement. The data was used to train the ANN model to learn data patterns and to predict accurate solar radiation in spite of the ambient temperature. Results of the study revealed that temperature has a negative correlation (–0.7381) with accuracy, such that an increase in temperature reduces the accuracy of the prototype solarimeter. Increased temperature caused an accuracy deviation of about 27.16 %. The ANN model successfully predicts accurate solar radiation measurement with an R-squared of 0.974, RMSE of 49.24 W/m2, and an accuracy of 86.32 % which represents a 13.39 % improvement in the performance of the prototype solarimeter. This study's novelty stands in its attempt to use machine learning to address the temperature sensitivity of a PV-based solar irradiance instrument. The results revealed here exposes the potency of deep learning models for optimising engineering systems.

The Application of Deep Learning Methods in the Forecasting and Imputation of Solar Irradiance Data

Accurate solar radiation forecasts are necessary for solar power plants and local energy grids. Precise solar predictions can optimise plant operations and assist in managing power generation fluctuations. High-quality solar data are required to perform solar forecasting and may be accessed through open databases such as the Baseline Surface Radiation Network. Due to maintenance and malfunctioning equipment, these databases may have missing data. Deep learning methods can extract relevant features and non-linearities in data, which make them suitable for forecasting and imputation tasks. The development of the transformer architecture in 2017 marked an improvement from recurrent sequence processing using RNN (recurrent neural net) architectures to parallel sequence handling using self-attention mechanisms. Previous deep learning irradiance forecasting models utilised a RNN or RNN extension architecture. This paper presents novel applications of Transformer models to solar imputation and forecasting. Two deep learning architectures – Transformer and SAITS (Self-Attention-based Imputation for Time Series) – were trained to impute time series of global horizontal irradiance. Additionally, a Temporal Fusion Transformer (TFT) deep learning architecture was trained to forecast future values of global horizontal irradiance time series. Feature concatenation allows for the combination of differing exogenous variables into a single input vector for a deep learning solar forecasting model. Previous solar forecasting models do not incorporate cloud data or otherwise utilise an exogenous simple categorical mask to indicate cloud cover. In this paper, GOES-16 cloud optical depth and particle size datasets provide an improved representation of cloud properties. Classical imputation and forecasting models were also developed and used as baselines for comparison with deep learning models. The models were evaluated using standard statistical metrics for time series. It was found that the deep learning TFT forecasting model produced better forecasts than the classical regression model. However, the classical KNN imputer performed better than the two deep learning imputation models.

Role of Radiotherapy Technologists in Clinical Radiation Therapy Practices: A Comprehensive Review

Radiation therapists, also known as radiotherapy technologists, are allied health professionals who specialise in providing high doses of radiation to patients with malignant growths. High-energy radiation damages cells’ genetic material {Deoxyribonucleic Acid (DNA)}, stopping them from replicating and multiplying further. Radiation can be used to treat cancer or as a highly effective palliative therapy to alleviate cancer-related pain in patients. Radiation therapy can be used in conjunction with other treatments, including surgery, chemotherapy, and immunotherapy. Because radiation harms both healthy tissues and malignant cells, the primary objective of radiation therapy is to optimise radiation exposure to abnormal cancer cells while avoiding irradiation of healthy tissues adjacent to or in the path of radiation. Remarkable precision is required to provide ionising radiation to patients and target specific cells for consistent, accurate, and successful radiation treatment. A team consisting of radiation oncologists, medical physicists, radiation therapists, medical dosimetrists, and nurses work together to achieve this goal. Radiation therapists are responsible for delivering radiation treatment, as well as maintaining and testing the equipment used to monitor and administer the treatment. The radiation therapist administers the patient’s treatment dose according to the prescription plan made by the radiation oncologists and medical physicists. Radiation therapists must communicate with patients and their families, respond to their inquiries, and clarify the radiation treatment plan. They must also track the patient’s progress and report any changes to their seniors. Radiation therapists must maintain high levels of precision while administering therapy, think critically, and exercise autonomous, professional, and ethical judgment in all aspects of their work. The field of radiotherapy is continuously evolving with new advancements in technology and treatment delivery techniques that maximise patient satisfaction and treatment delivery efficacy. Radiation therapists undergo continuing education and training to broaden their knowledge of the field and stay current with standard practice. This review addresses the critical role of radiation therapists in clinical radiation therapy practices. It highlights their significance in delivering safe and effective treatment to radiotherapy patients. The review covers various aspects, including the responsibilities of technologists in treatment planning, equipment operation, patient care and safety, quality assurance, and research. This present review provides a comprehensive understanding of the importance of well-trained and skilled radiation therapists in ensuring the success of radiation therapy treatments.

METHODS FOR IMPROVING THE DRG-05M DOSIMETER IN BRACHYTHERAPY

Relevance: The article discusses one of the ways to improve the DRG-05M dosimeter in brachytherapy by using new components and technologies in an improved scheme, which includes more accurate sensors, advanced signal processing techniques, efficient power cells, and other solutions. The application of such advanced components and technologies in the framework of DRG-05M dosimeter modernization is a new and original contribution to the field of dosimetry. The scientific novelty of the work is the improvement of the existing circuitry, namely, the improvement of the DRG-05M dosimeter’s electrical circuitry based on the analysis of its shortcomings. This includes replacing components, optimizing circuit structure, eliminating noise and interference, and improving the stability and accuracy of measurements.
 The study aimed to improve the DRG-05M dosimeter to provide more accurate and reliable radiation measurements in brachytherapy.
 Methods: The paper analyses the existing components and their use in the electrical circuit to improve the measurement accuracy of the DRG-05M dosimeter, offers a new electrical circuit based on the collected data and requirements, considering the optimal location of components, their characteristics, performed calculations, and modeled circuit operation. This scientific research was carried out within the framework of the PCF scientific program “Metrological support of dosimetric measurements in contact radiation therapy,” IRN BR12967832.
 Results: We have selected the components for improving the DRG-05M dosimeter:photomultiplier tube (PMT), analog-digital converter (ADC), indication unit, and power supply. The proposed changes to improve the DRG-05M dosimeter shall result in a very compact scintillation-type dosimeter. Its size and weight shall be reduced by at least 3 times; the accuracy and speed of measurement will increase, and the lifetime of the instrument shall improve.
 Conclusion: Improvement of the dosimeter in brachytherapy is an important task to ensure the accuracy and reliability of measurement in treating cancer. Using components such as PMT, calibration source, battery charger, and microcontroller KR572PV5 can significantly improve the operation of the DRG-05M dosimeter and increase the accuracy of radiation dose measurement in brachytherapy.

Pseudo-Computed Tomography Generation from Noisy Magnetic Resonance Imaging with Deep Learning Algorithm

Purpose: Magnetic Resonance Imaging (MRI) applications offer superior soft tissue contrast compared with Computed Tomography (CT) for accurate radiotherapy planning although MRI images suffer from poor image quality and lack electron density for radiation dose calculation. The present study aims to use the Deep Learning (DL) approach to 1) enhance the quality of MRI images and 2) generate synthetic CT images using MRI images for more accurate radiotherapy planning.
 Materials and Methods: In this paper, the pix2pix Generative Adversarial Network was utilized to synthesize CT images from noisy MRI images of 20 arbitrarily patients with brain disease. The standard statistical measurements investigated the accuracy comparison of the modeled Hounsfield Unit (HU) value from MRI images and referenced CT of each patient. The famous quality metrics that were used to compare synthetic CTs and referenced CTs were the Mean Absolute eError (MAE), the structural similarity index (SSIM), and the Peak Signal-to-Noise Ratio (PSNR).
 Results: The higher quality measurements between the synthetic pseudo-CT and the referenced CT images as PSNR and SSIM should correlate with the lower MAE value. For the overall brain among blind test data, the measured peak signal-to-noise ratio, mean absolute error, and structural similarity index values were about 16.5, 28.13, and 93.46, respectively.
 Conclusion: The proposed method provides an acceptable level of statistical measurements computed on the Pseudo-CT and referenced CT, and it could be concluded that the p-CT can be implemented in radiotherapy treatment planning with acceptable accuracy.

Modeling global solar radiation using ambient temperature

Accurate information about solar radiation is regarded as the most important stage in determining the availability of solar energy. As well, it is considered the principal input for various applications of solar energy. Because solar radiation measurements are unavailable at many locations throughout the world, many solar radiation models have been developed to predict global solar radiation. In this regard, this study objects to develop accurate and quick Global Solar Radiation (GSR) models for new locations, which currently lack an accurate model. Additionally, assess the performance of a recently introduced model, one of the best temperature-based models for GSR estimation, in these five new sites. Moreover, the proposed model's generalization capacity is investigated over the whole zone, the Suez Canal Zone, and a comparative analysis of its performance is presented. Models’ estimation is compared to the observed values, and the most common performance indicators are obtained to assess models’ performance. The findings indicate that the developed models in this study can predict global solar radiation accurately. Where the Models’ performance, both the local and general models, are larger than 95 % at all sites except for the local one at Port Said City (coastal site), it is 91 %. Additionally, the developed models have good RMSE and MABE values which range from 0.8 to 1.8 (MJ/m2 day−1) and 0.7 to 1.7 (MJ/m2 day−1), successively. Besides, they have excellent performance with coefficient of determination (R2) values ranging from 0.95 to 0.98, whereas prior research's ranges are 0.884–0.895 for R2 and 2.69–3.367 (MJ/m2 day−1) for RMSE. Therefore, the developed models in this study can be utilized for accurate GSR forecast, with their high applicability which may be achieved by combining them with various long- or short-term weather forecasting approaches, that are mostly used to reliably anticipate weather temperature. As well, these models' precise and speedy computation of GSR can also be employed in the design and performance evaluation of various solar applications.

SPNet: a size-variant progressive network for aero-optical thermal radiation effects correction.

When an aircraft is flying at a high speed, the airflow meets the optical cover and is compressed, resulting in aero-optical thermal radiation effects that degrade image quality. In this paper, based on the inherent characteristic that the degrade level of the thermal radiation bias field remains consistent regardless of image size, a size-variant progressive aero-optical thermal radiation effects correction network (SPNet) is proposed. First, SPNet uses two sub-networks to progressively correct degraded image, first and second sub-networks are responsible for learning coarse and accurate thermal radiation bias fields respectively. Second, we introduce the multi-scale feature upsampling module (MFUM) to leverage the multi-scale information of the features and promote inter-channel information interaction. Third, we propose an adaptive feature fusion module (AFFM) to dynamically fuse features from different scales by assigning different weights. At last, a multi-head self-attention feature extraction module (MSFEM) is proposed to extract global information feature maps. Compared with state-of-the-art thermal radiation effects correction methods, experiments on both simulated and real degraded images demonstrate the performance of our proposed method.

Comparison of the sensitivities and accuracies of optoelectronic transducers for solar irradiance measurement

This paper compares the sensitivities and accuracies of four different optoelectronic transducers in measuring solar irradiance for the fabrication of portable and low-cost pyranometers. Trans-impedance conditioning circuits were designed for two phototransistors (BP103 and SFH3310) and two photodiodes (BPW21 and BPW34). The Arduino Mega 2560 was used as an interface between the analogue signal produced by the conditioning circuits and the digital output ports. The transducers with a standard pyranometer RSRA_05V were arranged on a vero board and exposed to the sun. Statistical analysis of the experimental results produced the least root mean square error value of 6.58794 Wm−2 for phototransistor BP103 during the dry season followed by 13.35216 Wm−2 for phototransistor SFH3310 during the dry season. The correlation coefficients of BPW21, BPW34, BP103, and SFH3310 with the standard pyranometer are 0.9489, 0.9916, 0.9976, and 0.9905, respectively. The experimental results obtained from the phototransistors BP103 and SFH3310 strongly correlated with those of the standard pyranometer coupled with lower root mean square error and mean bias error values than those of the photodiodes. Thus, the phototransistors BP103 and SFH3310 are more accurate and effective for measuring solar irradiance. This study contributes to the development of low-cost and accurate solar radiation meters.

Golgi-triggered radiation sensitizer retention enhances the efferocytosis and immunotherapy in breast cancer with skeleton metastases

Radiotherapy with higher precision and better efficiency is required for breast cancer bone metastasis therapy. In this study, we synthesized a golgi-triggered radiation sensitizer, Furin Activated Iron Oxide nanoparticles (FAIOs), which could suppress breast cancer bone metastasis by targeting tumor micro-environment (TME) and enhancing immune response. As a multi-functional theranostic mediator, FAIOs enabled tumor-specific MRI and accurate radiation therapy in TMEs with enriched FURIN. The increased efferocytosis triggered by FAIOs-mediated tumor cell apoptosis offered remarkable benefits. Moreover, the osteoclast-mediated bone destruction was markedly alleviated. These encouraging findings provided insights into therapeutic and diagnostic value of these multifunctional FAIOs for bone-metastatic breast cancer. (Scheme)

Application of customisable 3D-printed bite blocks combined with nano head, neck and shoulder mask to head and neck neoplasm radiotherapy

This study investigated the effectiveness of customisable 3D-printed bite blocks combined with nano head, neck and shoulder mask in radiotherapy for head and neck neoplasms. The images of 50 patients with head and neck neoplasms treated at our hospital from 2020 to 2022 were collected using CBCT. The patients were divided into two groups: 25 in the mask group and 25 in the combination group. The mask group utilized thermoplastic polyurethane (TPU) nanofiber membrane to stabilize the position of the patients’ head, shoulder, and neck. On the other hand, the combined group added a personalized bite block produced by 3D printing technology on the basis of the mask group. Cone beam CT (CBCT) was used to calibrate any errors, and the set-up errors and number of head rotations after image calibration were analyzed between the two groups. The conformation number (CN) and heterogeneity index (HI) in the target area of the intensity-modulated radiation therapy (IMRT) plan of the two groups were calculated, and the dose-volume parameters of the tongue and other organs at risk, including Dmax and Dmean, were compared using paired tests. Follow-up observations were performed to assess the occurrence of adverse reactions to radiation damage among the patients. The findings revealed a statistically significant difference in the Y -axis set-up error between the combination group and the mask group (P < 0.05). Additionally, the combination group exhibited a higher proportion of set-up errors < 2 mm in both the Y -axis and Z-axis directions compared to the mask group. Significantly fewer images showed head rotation in the combination group than in the mask group. In terms of the target area, there were no significant differences observed in the conformation number (CN) and heterogeneity index (HI) between the two groups. However, wearing the personalized bite block resulted in significantly lower Dmax and Dmean values for the tongue. In contrast, the mask group experienced a higher incidence of oral mucositis and taste disorders. By utilizing customizable 3D-printed bite blocks in conjunction with a nano-mask, accurate repositioning for chemotherapy was achieved, leading to a reduction in set-up error and a significant decrease in radiation dose to the tongue and its surface oral mucosa. Consequently, the occurrence of adverse reactions such as oral mucositis and taste disorders was also reduced. The production of personalized bite blocks is simple, with accurate radiotherapy, making it a technique that should be widely adopted.

Blockchain + Radiotherapy: The Scenarios of the 5G‐Enabled Smart Radiotherapy Era

AbstractIn cancer radiotherapy (RT), each step can be digitalized, from CT simulation, planning, dose verification, and image guidance to treatment delivery. Therefore, RT may be the most digitized discipline in the field of internal medicine. Then, questions arise. What potential development opportunities does the development of network transmission technologies, such as blockchain and 5G, provide to promote the progress of tumor RT? What will the future of smart RT (SART) look like? The purpose of this article is to introduce briefly the main characteristics of blockchain and 5G transmission technologies and share ideas to imagine the “tomorrow” of smart RT, hoping to provide a more accurate, safe, and comfortable RT experience for patients in the future.

Photochromic ceramics for multimode detection of UV-VIS radiation dose

Electromagnetic radiation in the ultraviolet-to-visible (UV-VIS) band is closely related to human life, and therefore, accurate measurement of UV-VIS radiation dose is essential. However, there are disadvantages to existing radiation dose equipment: it is cumbersome to operate, takes time to analyze, and lacks multimodal measurements. In this study, a photochromic ceramic with rare earth ions Ho3+ and Yb3+ co-doped Ca2SnO4 host was developed. It produces rapid color switching between light yellow and black under UV and VIS radiation. The XPS and EPR results indicate that oxygen vacancies are important in coloring. Based on the photochromic effect, the intensity of down and up-conversion luminescence can be regulated. The maximum regulation rate is 95 %, higher than most photochromic materials of the same type. A multimode UV-VIS radiation detection method is proposed based on photochromism, down-conversion, and up-conversion luminescence light intensity modulation. The results show that the ceramics respond to a wide spectral range of electromagnetic radiation from UV-VIS with varying degrees, and the best response is to UVB (Ultraviolet Radiation B). A convenient scheme for on-site radiation dosimetry by colorimetry and changes in luminescence intensity was designed to verify the method's reliability. Stability tests have shown that these ceramics have good cyclic stability, making them an ideal detection material for accurate UV-VIS radiation dose detection.

Different-resolution solar radiation prediction with accompanying microclimate data and long-term monitoring experiment by LSTM, RF, and SVR: a case of campus area

ABSTRACT Solar radiation plays a pivotal role in urban microclimate, significantly, impacting building energy consumption and renewable energy utilization. Utilizing a comprehensive three- and-a- half-year dataset of high-precision urban microclimate, this study proposes a three-step approach to solar radiation prediction: 1) Data Preprocessing and Characterization: The microclimate data are meticulously preprocessed and categorized, with solar radiation values categorized into three resolutions: total daily solar radiation, hourly solar radiation, and 30-min solar radiation. 2) Feature Selection: Various climatic parameters are carefully selected for solar radiation prediction at different resolutions. 3) Machine Learning Models: Three machine learning models, namely Long Short-Term Memory (LSTM), Random Forest (RF) and Support Vector Regression (SVR), are employed for solar radiation prediction. The results show that all three models exhibit prediction accuracy with closely matched errors. Particularly noteworthy findings include SVR’s superior performance in predicting total daily solar radiation with a minimal error rate of about 6.0% and an impressive coefficient of determination of 0.9789. LSTM exhibits adaptability, achieving R2 values of 0.9906 and 0.9956 for hourly and 30-min radiation predictions, respectively. This study offers valuable insights into solar radiation and its implications for building energy research. It underscores that large datasets enable accurate solar radiation predictions, even when excluding certain parameters like daily maximum temperature and relative humidity.