Impact of iodinated oil in proton therapy on relative stopping power of liver post-cTACE.

Objective. Conventional transarterial chemoembolization (cTACE) is a common treatment for hepatocellular carcinoma (HCC), often with unsatisfactory local controls. Combining cTACE with radiotherapy shows a promise for unresectable large HCC, with proton therapy preserving healthy liver tissue. However, the proton therapy benefits are subject to the accuracy of tissue relative stopping power (RSP) prediction. The RSP values are typically derived from computed tomography (CT) images using stoichiometric calibration. Lipiodol deposition significantly increases CT numbers in liver regions of post-cTACE. Hence, it is necessary to evaluate the accuracy of RSP in liver regions of post-cTACE.Approach. Liver, water, and iodinated oil samples were prepared. Some liver samples contained iodinated oil. The water equivalent path length (WEPL) of sample was measured through the pullbacks of spread-out Bragg peak (SOBP) depth-dose profiles scanned in a water tank with and without sample in the beam path. Measured RSP values were compared to estimated RSP values derived from the CT number based on the stoichiometric calibration method.Main results. The measured RSP of water was 0.991, confirming measurement system calibration. After removing the RSP contribution from container walls, the pure iodinated oil and liver samples had RSP values of 1.12 and 1.06, while the liver samples mixed with varying oil volumes (5 ml, 10 ml, 15 ml) showed RSP values of 1.05, 1.05 and 1.06. Using the stoichiometric calibration method, pure iodinated oil and liver samples had RSP values of 2.79 and 1.06. Liver samples mixed with iodinated oil (5 ml, 10 ml, 15 ml) had calculated RSP values of 1.21, 1.34, and 1.46. The RSP discrepancy reached 149.1% for pure iodinated oil.Significance.Iodinated oil notably raises CT numbers in liver tissue. However, there is almost no effect on its RSP value. Proton treatment of post-cTACE HCC patients can therefore be overshooting if no proper measures are taken against this specific effect.

Unraveling the atmospheric oxidation mechanism and kinetics of naphthalene: Insights from theoretical exploration.

Naphthalene, the most abundant polycyclic aromatic hydrocarbon in the atmosphere, significantly influences OH consumption and secondary organic aerosol (SOA) formation. Naphthoquinone (NQ) is a significant contributor to ring-retaining SOA from naphthalene degradation, impacting the redox properties and toxicity of ambient particles. However, inconsistencies persist regarding concentrations of its isomers, 1,2-NQ and 1,4-NQ. In present work, our theoretical investigation into naphthalene's reaction with OH and subsequent oxygenation unveils their role in SOA formation. The reaction kinetics of initial OH and subsequent O2 oxidation was extensively studied using high-level quantum chemical methods (DLPNO-CCSD(T)/aug-ccpVQZ//M052x-D3/6-311++G(d,p)) combined with RRKM/master equation simulations. The reactions mainly proceed through electrophilic addition and abstraction from the aromatic ring. The total rate coefficient of naphthalene + OH at 300 K and 1 atm from our calculation (7.2 × 10-12 cm3 molecule-1 s-1) agrees well with previous measurements (∼1 × 10-11 cm3 molecule-1 s-1). The computed branching ratios facilitate accurate product yield determination. The largest yield of 1-hydroxynaphthalen-1-yl radical (add1) producing the major precursor of RO2 is computed to be 93.8 % in the ambient environment. Our calculated total rate coefficient (5.2 × 10-16 cm3 molecule-1 s-1) for add1 + O2 closely matches that of limited experimental data (8.0 × 10-16 cm3 molecule-1 s-1). Peroxy radicals (RO2) generated from add1 + O2 include 4-cis/trans-(1-hydroxynaphthalen-1-yl)-peroxy radical (add1-4OOadd-cis/trans, 66.0 %/17.5 %), 2-cis/trans-(1-hydroxynaphthalen-1-yl)-peroxy radical (add1-2OOadd-cis/trans, 10.3 %/6.3 %). Regarding the debated predominance of 1,4-NQ (corresponding to the parent RO2, i.e., add1-4OOadd-cis/trans) and 1,2-NQ (corresponding to the parent RO2, i.e., add1-2OOadd-cis/trans) in the atmosphere, our findings substantiate the dominance of 1,4-NQ. This study also indicates potential weakening of 1,4-NQ's dominance due to competition from decomposition reactions of add1-4OOadd-cis/trans and add1-2OOadd-cis/trans. Precise reaction kinetics data are essential for characterizing SOA transformation derived from naphthalene and assessing their climatic impacts within modeling frameworks.

Weakly supervised learning-based 3D bladder reconstruction from 2D ultrasound images for bladder volume measurement.

BACKGROUND: Accurate measurement of bladder volume is necessary to maintain the consistency of the patient's anatomy in radiation therapy for pelvic tumors. As the diversity of the bladder shape, traditional methods for bladder volume measurement from 2D ultrasound have been found to produce inaccurate results. PURPOSE: To improve the accuracy of bladder volume measurement from 2D ultrasound images for patients with pelvic tumors. METHODS: The bladder ultrasound images from 130 patients with pelvic cancer were collected retrospectively. All data were split into a training set (80 patients), a validation set (20 patients), and a test set (30 patients). A total of 12 transabdominal ultrasound images for one patient were captured by automatically rotating the ultrasonic probe with an angle step of 15°. An incomplete 3D ultrasound volume was synthesized by arranging these 2D ultrasound images in 3D space according to the acquisition angles. With this as input, a weakly supervised learning-based 3D bladder reconstruction neural network model was built to predict the complete 3D bladder. The key point is that we designed a novel loss function, including the supervised loss of bladder segmentation in the ultrasound images at known angles and the compactness loss of the 3D bladder. Bladder volume was calculated by counting the number of voxels belonging to the 3D bladder. The dice similarity coefficient (DSC) was used to evaluate the accuracy of bladder segmentation, and the relative standard deviation (RSD) was used to evaluate the calculation accuracy of bladder volume with that of computed tomography (CT) images as the gold standard. RESULTS: The results showed that the mean DSC was up to 0.94 and the mean absolute RSD can be reduced to 6.3% when using 12 ultrasound images of one patient. Further, the mean DSC also was up to 0.90 and the mean absolute RSD can be reduced to 9.0% even if only two ultrasound images were used (i.e., the angle step is 90°). Compared with the commercial algorithm in bladder scanners, which has a mean absolute RSD of 13.6%, our proposed method showed a considerably huge improvement. CONCLUSIONS: The proposed weakly supervised learning-based 3D bladder reconstruction method can greatly improve the accuracy of bladder volume measurement. It has great potential to be used in bladder volume measurement devices in the future.

A denoising method based on deep learning for proton radiograph using energy resolved dose function.

Objective.Proton radiograph has been broadly applied in proton radiotherapy which is affected by scattered protons which result in the lower spatial resolution of proton radiographs than that of x-ray images. Traditional image denoising method may lead to the change of water equivalent path length (WEPL) resulting in the lower WEPL measurement accuracy. In this study, we proposed a new denoising method of proton radiographs based on energy resolved dose function curves.Approach.Firstly, the corresponding relationship between the distortion of WEPL characteristic curve, and energy and proportion of scattered protons was established. Then, to improve the accuracy of proton radiographs, deep learning technique was used to remove scattered protons and correct deviated WEPL values. Experiments on a calibration phantom to prove the effectiveness and feasibility of this method were performed. In addition, an anthropomorphic head phantom was selected to demonstrate the clinical relevance of this technology and the denoising effect was analyzed.Main results.The curves of WEPL profiles of proton radiographs became smoother and deviated WEPL values were corrected. For the calibration phantom proton radiograph, the average absolute error of WEPL values decreased from 2.23 to 1.72, the mean percentage difference of all materials of relative stopping power decreased from 1.24 to 0.39, and the average relative WEPL corrected due to the denoising process was 1.06%. In addition, WEPL values correcting were also observed on the proton radiograph for anthropomorphic head phantom due to this denoising process.Significance.The experiments showed that this new method was effective for proton radiograph denoising and had greater advantages than end-to-end image denoising methods, laying the foundation for the implementation of precise proton radiotherapy.

Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss.

-Thoracic disease, like many other diseases, can lead to complications. Existing multi-label medical image learning problems typically include rich pathological information, such as images, attributes, and labels, which are crucial for supplementary clinical diagnosis. However, the majority of contemporary efforts exclusively focus on regression from input to binary labels, ignoring the relationship between visual features and semantic vectors of labels. In addition, there is an imbalance in data amount between diseases, which frequently causes intelligent diagnostic systems to make erroneous disease predictions. Therefore, we aim to improve the accuracy of the multi-label classification of chest X-ray images. Chest X-ray14 pictures were utilized as the multi-label dataset for the experiments in this study. By fine-tuning the ConvNeXt network, we got visual vectors, which we combined with semantic vectors encoded by BioBert to map the two different forms of features into a common metric space and made semantic vectors the prototype of each class in metric space. The metric relationship between images and labels is then considered from the image level and disease category level, respectively, and a new dual-weighted metric loss function is proposed. Finally, the average AUC score achieved in the experiment reached 0.826, and our model outperformed the comparison models.

VirtualDose-IR: a cloud-based software for reporting organ doses in interventional radiology.

A cloud-based software, VirtualDose-IR (Virtual Phantoms Inc., Albany, New York, USA), designed to report organ doses and effective doses for a diverse patient population from interventional radiology (IR) procedures has been developed and tested. This software is based on a comprehensive database of Monte Carlo-generated organ dose built with a set of 21 anatomically realistic patient phantoms. The patient types included in this database are both male and female people with different ages reflecting reference adults, obese people with different BMIs and pregnant women at different gestational stages. Selectable parameters such as patient type, tube voltage, filtration thickness, beam direction, field size, and irradiation site are also considered in VirtualDose-IR. The software has been implemented using the 'Software as a Service (SaaS)' delivery concept permitting simultaneous multi-user, multi-platform access without requiring local installation. The patient doses resulting from different target sites and patient populations were reported using the VirtualDose-IR system. The patient doses under different source to surface distances (SSD) and beam angles calculated by VirtualDose-IR and Monte Carlo simulations were compared. For most organs, the dose differences between VirtualDose-IR results and Monte Carlo results were less than 0.3 mGy at 15 000 mGy * cm2 kerma-area product (KAP). The organ dose results were compared with measurement data previously reported in literatures. The doses to organs that were located within the irradiation field match closely with experimental measurement data. The differences in the effective dose values between calculated using VirtualDose-IR and those measured were less than 2.5%. The dose errors of most organs between VirtualDose-IR and literature results were less than 40%. These results validate the accuracy of organ doses reported by VirtualDose-IR. With the inclusion of pre-specified clinical IR examination parameters (such as beam direction, target location, field of view and beam quality) and the latest anatomically realistic patient phantoms in Monte Carlo simulations, VirtualDose-IR provides users with accurate dose information in order to systematically compare, evaluate, and optimize IR plans.

A single detector energy-resolved proton radiography system: a proof of principle study by Monte Carlo simulations.

Taking advantage of Bragg peak and small spot size, pencil beam scanning proton therapy can deliver a highly conformal dose distribution to target while sparing normal tissues. However, such dose distributions can be highly sensitive to the proton range uncertainty which can reach 5% or higher in lung tissue. One proposed method for reducing range uncertainty is to measure the water equivalent path length (WEPL) by proton radiography. In this study, we followed a newly proposed proton beam radiography technique based on energy resolved dose functions (ERDF) to construct a Monte Carlo model for a single detector energy-resolved proton radiography system (SDPRS). This SDPRS model was constructed in the Monte Carlo software package TOPAS (TOol for PArticle Simulation) and it includes the Mevion HYPERSCAN™ pencil beam scanning treatment head and a 2D dose detector positioned downstream as the imager. A calibration phantom containing a number of tissue equivalent materials was simulated to evaluate the accuracy in WEPL measurement by SDPRS. The mean deviation of the obtained relative stopping power (RSP) from the reference values was 0.31%. Proton radiographs of an anthropomorphic head phantom were also generated to demonstrate the clinical relevance of the technique. Effects of different energy layer spacing and measurement noise were also studied.

EVALUATION OF SECONDARY DOSE AND CANCER RISK FOR OUT-OF-FIELD ORGAN IN ESOPHAGEAL CANCER IMRT IN A CHINESE HOSPITAL USING ATOM PHANTOM MEASUREMENTS.

There have been few studies on the secondary cancer after radiation treatment in Chinese hospitals. This study has measured out-of-field absorbed organ doses from intensity-modulated radiation therapy (IMRT) radiotherapy for esophageal cancer in a Chinese hospital and evaluated the risks of secondary cancer. The dose measurements were based on the thermo-luminescence dosemeter (TLD) and the ATOM® phantom, which represents an adult male. Over 100 TLD chips were placed in 35 different organ sites and one group of the same TLDs was set as background contrast. All TLDs were calibrated against the same Linac accelerator performing an IMRT plan for esophageal cancer. The measured doses were used to calculate the secondary cancer risks according to biological effects of ionizing radiation (BEIR) VII methodology. The baseline risks and survival data were based on relevant statistics for the Chinese population. It is found that the out-of-field organ doses depended greatly on the distance between organ sites and the target isocenter. The organ doses decreased exponentially as the distance from the target isocenter increased, and, for distances <15 cm, the organ doses fell off more rapidly and almost decreased by 99.55%. When compared with the calculation results by the Pinnacle treatment planning system (TPS), most of the out-of-field organ doses were underestimated in the TPS and the percentage of underestimation reached 100% for distant organs such as the bladder, prostate and testis. These trends are due to a known fact that out-of-field organs received secondary radiation resulted from patients and collimator scattering as well as leakage in the gantry head. The higher lifetime attribute risks (LARs) per 100 000 population were in the lower esophagus (186) and lungs (93.2) near the target. But all LARs of considered organs were found to be less than the baseline cancer risks. Results in this article can help to provide a database about the effect of radiotherapy-induced secondary cancer incidence during esophageal cancer treatment in China.