A preliminary study on rectal dose reduction associated with hyaluronic acid implantation in brachytherapy for prostate cancer.

Objectives: Hydrogel spacer (HS) was developed to reduce rectal toxicities caused by radiotherapy, but has been reported to cause major adverse events. Our institute has attempted to introduce a hyaluronic acid (HA) as an alternative spacer. This study aimed to compare rectal doses and geometric distributions between the HS and HA implantation in prostate cancer. Methods: HS and HA were inserted in 20 and 18 patients undergoing high-dose brachytherapy, respectively. The rectum spacer volumes injected were 10 mL and 22 mL, respectively. In the treatment planning system, 13.5 Gy was administered with common catheter positions. The rectal dose indices were assessed between the spacer groups for dosimetry evaluation. Distances between the prostate and rectum and configurations of the spacers were compared. Results: The mean doses irradiated to 0.1 and 2 mL of the rectum were 10.45 Gy and 6.71 Gy for HS, and 6.73 Gy and 4.90 Gy for HA (p<0.001). The mean minimum distances between the prostate and rectum were 1.23 cm and 1.79 cm for HS and HA, respectively (p<0.05). Geometrical configuration comparisons revealed that HA has a higher ability to expand the space than HS. Conclusion: The rectal dose reduction ability of HA is significantly greater than that of HS, suggesting its potential as a new spacer.

Monte carlo simulation study on the dose and dose-averaged linear energy transfer distributions in carbon ion radiotherapy.

Dose-averaged linear energy transfer (LETd) is conventionally evaluated from the relative biological effectiveness (RBE)-LETd fitted function used in the treatment planning system. In this study, we calculated the physical doses and their linear energy transfer (LET) distributions for patterns of typical CIRT beams using Monte Carlo (MC) simulation. The LETd was then deduced from the MC simulation and compared with that obtained from the conventional method. The two types of LETd agreed well with each other, except around the distal end of the spread-out Bragg peak. Furthermore, an MC simulation was conducted with the material composition of water and realistic materials. The profiles of physical dose and LETd were in good agreement for both techniques. These results indicate that the previous studies to analyze the minimum LETd in CIRT cases are valid for practical situations, and the material composition conversion to water little affects the dose distribution in the irradiation field.

Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study.

Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning-based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854-0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912-0.965, p-value < 0.001) compared with anterior-posterior (AUC 0.782, 95% CI 0.644-0.897) or portable anterior-posterior (AUC 0.869, 95% CI 0.814-0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823-0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765-0.904) and Shenzhen (AUC 0.806, 95% CI 0.771-0.839). A deep learning-based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation.

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model.

Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients.

Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

The admixed brushtail possum genome reveals invasion history in New Zealand and novel imprinted genes.

Combining genome assembly with population and functional genomics can provide valuable insights to development and evolution, as well as tools for species management. Here, we present a chromosome-level genome assembly of the common brushtail possum (Trichosurus vulpecula), a model marsupial threatened in parts of their native range in Australia, but also a major introduced pest in New Zealand. Functional genomics reveals post-natal activation of chemosensory and metabolic genes, reflecting unique adaptations to altricial birth and delayed weaning, a hallmark of marsupial development. Nuclear and mitochondrial analyses trace New Zealand possums to distinct Australian subspecies, which have subsequently hybridised. This admixture allowed phasing of parental alleles genome-wide, ultimately revealing at least four genes with imprinted, parent-specific expression not yet detected in other species (MLH1, EPM2AIP1, UBP1 and GPX7). We find that reprogramming of possum germline imprints, and the wider epigenome, is similar to eutherian mammals except onset occurs after birth. Together, this work is useful for genetic-based control and conservation of possums, and contributes to understanding of the evolution of novel mammalian epigenetic traits.

Monte Carlo-calculated beam quality and perturbation correction factors validated against experiments for Farmer and Markus type ionization chambers in therapeutic carbon-ion beams.

Objective. In current dosimetry protocols, the estimated uncertainty of the measured absorbed dose to waterDwin carbon-ion beams is approximately 3%. This large uncertainty is mainly contributed by the standard uncertainty of the beam quality correction factorkQ. In this study, thekQvalues in four cylindrical chambers and two plane-parallel chambers were calculated using Monte Carlo (MC) simulations in the plateau region. The chamber-specific perturbation correction factorPof each chamber was also determined through MC simulations.Approach.kQfor each chamber was calculated using MC code Geant4. The simulatedkQratios in subjected chambers and reference chambers were validated through comparisons against our measured values. In the measurements in Heavy-Ion Medical Accelerator in Chiba,kQratios were obtained fromDwvalues of60Co, 290- and 400 MeV u-1carbon-ion beams that were measured with the subjected ionization chamber and the reference chamber. In the simulations,fQ(the product of the water-to-air stopping power ratio andP) was acquired fromDwand the absorbed dose to air calculated in the sensitive volume of each chamber.kQvalues were then calculated from the simulatedfQand the literature-extractedWairand compared with previous publications.Main results. The calculatedkQratios in the subjected chambers to the reference chamber agreed well with the measuredkQratios. ThekQuncertainty was reduced from the current recommendation of approximately 3% to 1.7%. ThePvalues were close to unity in the cylindrical chambers and nearly 1% above unity in the plane-parallel chambers.Significance. ThekQvalues of carbon-ion beams were accurately calculated in MC simulations and thekQratios were validated through ionization chamber measurements. The results indicate a need for updating the current recommendations, which assume a constantPof unity in carbon-ion beams, to recommendations that consider chamber-induced differences.

Expanding the range of the respiratory infectome in Australian feedlot cattle with and without respiratory disease using metatranscriptomics.

BACKGROUND: Bovine respiratory disease (BRD) is one of the most common diseases in intensively managed cattle, often resulting in high morbidity and mortality. Although several pathogens have been isolated and extensively studied, the complete infectome of the respiratory complex consists of a more extensive range unrecognised species. Here, we used total RNA sequencing (i.e., metatranscriptomics) of nasal and nasopharyngeal swabs collected from animals with and without BRD from two cattle feedlots in Australia. RESULTS: A high abundance of bovine nidovirus, influenza D, bovine rhinitis A and bovine coronavirus was found in the samples. Additionally, we obtained the complete or near-complete genome of bovine rhinitis B, enterovirus E1, bovine viral diarrhea virus (sub-genotypes 1a and 1c) and bovine respiratory syncytial virus, and partial sequences of other viruses. A new species of paramyxovirus was also identified. Overall, the most abundant RNA virus, was the bovine nidovirus. Characterisation of bacterial species from the transcriptome revealed a high abundance and diversity of Mollicutes in BRD cases and unaffected control animals. Of the non-Mollicutes species, Histophilus somni was detected, whereas there was a low abundance of Mannheimia haemolytica. CONCLUSION: This study highlights the use of untargeted sequencing approaches to study the unrecognised range of microorganisms present in healthy or diseased animals and the need to study previously uncultured viral species that may have an important role in cattle respiratory disease. Video Abstract.

Gammaretroviruses, novel viruses and pathogenic bacteria in Australian bats with neurological signs, pneumonia and skin lesions.

More than 70 bat species are found in mainland Australia. While most studies of bat viromes focus on sampling seemingly healthy individuals, little is known about the viruses and bacteria associated with diseased bats. We performed traditional diagnostic techniques and metatranscriptomic sequencing on tissue samples from 43 Australian bats, comprising three flying fox (Pteropodidae) and two microbat species experiencing a range of disease syndromes, including mass mortality, neurological signs, pneumonia and skin lesions. Of note, we identified the recently discovered Hervey pteropid gammaretrovirus in a bat with lymphoid leukemia, with evidence of replication consistent with an exogenous virus. The possible association of Hervey pteropid gammaretrovirus with lymphoid leukemia clearly merits additional investigation. One novel picornavirus and at least three new astroviruses and bat pegiviruses were also identified in a variety of tissue types, as well as a number of likely bacterial pathogens or opportunistic infections, most notably Pseudomonas aeruginosa.

Winter is coming-The role of seasonality through the lens of the rodent virome.

Rodent virus communities (viromes) are overrepresented with zoonotic viruses, and as such are a key host system for the study of zoonotic viruses. However, the extent of viral diversity beyond characterized zoonotic viruses, and the factors that modulate the viromes of rodents remain opaque. In this issue of Molecular Ecology, Raghwani et al. (2023) use rodents as a model to understand the role of seasonality in dictating virome abundance and composition-a factor known to play an important role in most animal one-host, one-pathogen systems. These data are not only highly relevant to rodents, but have broad applications across understanding and disentangling animal virome ecology.

Additional single dose GnRH agonist during luteal phase support may improve live birth rate in GnRHa-HRT frozen-thawed embryo transfer cycle: a retrospective cohort study.

BACKGROUND: GnRH agonist (GnRHa) has been reported to have direct effects and functional roles in the endometrium and embryos. Several meta-analyses have shown that GnRHa administration in the luteal phase improved the live birth rate or pregnancy rate in both fresh and frozen embryo transfer (FET) cycles. The aim of this study was to investigate whether luteal GnRHa administration could also improve in vitro fertilization (IVF) outcomes in patients undergoing hormone replacement therapy (HRT) cycles with GnRHa suppression. METHODS: The retrospective cohort study included a total of 350 patients undergoing GnRHa-HRT FET cycles. The study group included 179 patients receiving an additional single dose of GnRHa in the luteal phase following embryo transfer. A total of 171 patients in the control group did not receive luteal GnRHa. The baseline and cycle characteristics and reproductive outcomes were compared between the two groups. RESULTS: Baseline and cycle characteristics were similar between the two groups, except lower AMH levels were found in the luteal GnRHa group than in the control group. The luteal GnRHa group had a significantly higher ongoing pregnancy rate and live birth rate than the control group. The multivariate analysis revealed that luteal GnRHa administration was positively associated with ongoing pregnancy (OR 2.04, 95% CI 1.20-3.47, P = 0.008) and live birth (OR 2.03, 95% CI 1.20-3.45, P = 0.009). When the subgroup of patients with recurrent implantation failure was analyzed, the multivariate analysis also showed that luteal GnRHa administration had beneficial effects on ongoing pregnancy (OR 4.55, 95% CI 1.69-12.30, P = 0.003) and live birth (OR 4.30, 95% CI 1.59-11.65, P = 0.004). CONCLUSIONS: Our data suggest that the addition of one luteal dose of GnRHa may improve the live birth rate in patients undergoing the GnRHa-HRT protocol.

Film measurement and analytical approach for assessing treatment accuracy and latency in a magnetic resonance-guided radiotherapy system.

PURPOSE: We measure the dose distribution of gated delivery for different target motions and estimate the gating latency in a magnetic resonance-guided radiotherapy (MRgRT) system. METHOD: The dose distribution accuracy of the gated MRgRT system (MRIdian, Viewray) was investigated using an in-house-developed phantom that was compatible with the magnetic field and gating method. This phantom contains a simulated tumor and a radiochromic film (EBT3, Ashland, Inc.). To investigate the effect of the number of beam switching and target velocity on the dose distribution, two types of target motions were applied. One is that the target was periodically moved at a constant velocity of 5 mm/s with different pause times (0, 1, 3, 10, and 20 s) between the motions. During different pause times, different numbers of beams were switched on/off. The other one is that the target was moved at velocities of 3, 5, 8, and 10 mm/s without any pause (i.e., continuous motion). The gated method was applied to these motions at MRIdian, and the dose distributions in each condition were measured using films. To investigate the relation between target motion and dose distribution in the gating method, we compared the results of the gamma analysis of the calculated and measured dose distributions. Moreover, we analytically estimated the gating latencies from the dose distributions measured using films and the gamma analysis results. RESULTS: The gamma pass rate linearly decreased with increasing beam switching and target velocity. The overall gating latencies of beam-hold and beam-on were 0.51 ± 0.17 and 0.35 ± 0.05 s, respectively. CONCLUSIONS: Film measurements highlighted the factors affecting the treatment accuracy of the gated MRgRT system. Our analytical approach, employing gamma analysis on films, can be used to estimate the overall latency of the gated MRgRT system.

Failure modes and effects analysis study for accelerator-based Boron Neutron Capture Therapy.

BACKGROUND: Boron Neutron Capture Therapy (BNCT) has recently been used in clinical oncology thanks to recent developments of accelerator-based BNCT systems. Although there are some specific processes for BNCT, they have not yet been discussed in detail. PURPOSE: The aim of this study is to provide comprehensive data on the risk of accelerator-based BNCT system to institutions planning to implement an accelerator-based BNCT system. METHODS: In this study, failure mode and effects analysis (FMEA) was performed based on a treatment process map prepared for the accelerator-based BNCT system. A multidisciplinary team consisting of a medical doctor (MD), a registered nurse (RN), two medical physicists (MP), and three radiologic technologists (RT) identified the failure modes (FMs). Occurrence (O), severity (S), and detectability (D) were scored on a scale of 10, respectively. For each failure mode (FM), risk priority number (RPN) was calculated by multiplying the values of O, S, and D, and it was then categorized as high risk, very high risk, and other. Additionally, FMs were statistically compared in terms of countermeasures, associated occupations, and whether or not they were the patient-derived. RESULTS: The identified FMs for BNCT were 165 in which 30 and 17 FMs were classified as high risk and very high risk, respectively. Additionally, 71 FMs were accelerator-based BNCT-specific FMs in which 18 and 5 FMs were classified as high risk and very high risk, respectively. The FMs for which countermeasures were "Education" or "Confirmation" were statistically significantly higher for S than the others (p = 0.019). As the number of BNCT facilities is expected to increase, staff education is even more important. Comparing patient-derived and other FMs, O tended to be higher in patient-derived FMs. This could be because the non-patient-derived FMs included events that could be controlled by software, whereas the patient-derived FMs were impossible to prevent and might also depend on the patient's condition. Alternatively, there were non-patient-derived FMs with higher D, which were difficult to detect mechanically and were classified as more than high risk. In O, significantly higher values (p = 0.096) were found for FMs from MD and RN associated with much patient intervention compared to FMs from MP and RT less patient intervention. Comparing conventional radiotherapy and accelerator-based BNCT, although there were events with comparable risk in same FMs, there were also events with different risk in same FMs. They could be related to differences in the physical characteristics of the two modalities. CONCLUSIONS: This study is the first report for conducting a risk analysis for BNCT using FMEA. Thus, this study provides comprehensive data needed for quality assurance/quality control (QA/QC) in the treatment process for facilities considering the implementation of accelerator-based BNCT in the future. Because many BNCT-specific risks were discussed, it is important to understand the characteristics of BNCT and to take adequate measures in advance. If the effects of all FMs and countermeasures are discussed by multidisciplinary team, it will be possible to take countermeasures against individual FMs from many perspectives and provide BNCT more safely and effectively.

Development of the DICOM-based Monte Carlo dose reconstruction system for a retrospective study on the secondary cancer risk in carbon ion radiotherapy.

Objective.A retrospective study on secondary cancer risk on carbon ion radiotherapy (CIRT) is ongoing at the Heavy Ion Medical Accelerator in Chiba (HIMAC). The reconstruction of the whole-body patient dose distribution is the key issue in the study because dose distribution only around the planning target volume was evaluated in the treatment planning system.Approach.We therefore developed a new dose reconstruction system based on the Particle and Heavy Ion Transport code System (PHITS) coupled with the treatment plan DICOM data set by extending the functionalities of RadioTherapy package based on PHITS (RT-PHITS). In the system, the geometry of patient-specific beam devices such as the range shifter, range compensator, and collimators as well as the individual patient's body are automatically reconstructed. Various functions useful for retrospective analysis on the CIRT are implemented in the system, such as those for separately deducing dose contributions from different secondary particles and their origins.Main results.The accuracy of the developed system was validated by comparing the dose distribution to the experimental data measured in a water tank and using a treatment plan on an anthropomorphic phantom.Significance.The extended RT-PHITS will be used in epidemiological studies based on clinical data from HIMAC.

Meta-transcriptomic analysis of the virome and microbiome of the invasive Indian myna (Acridotheres tristis) in Australia.

Invasive species exert a serious impact on native fauna and flora and have become the target of eradication and management efforts worldwide. Invasive avian species can also be important pathogen reservoirs, although their viromes and microbiomes have rarely been studied. As one of the top 100 invasive pest species globally, the expansion of Indian mynas (Acridotheres tristis) into peri-urban and rural environments, in conjunction with increasing free-ranging avian agricultural practices, may increase the risk of microbial pathogens jumping species boundaries. Herein, we used a meta-transcriptomic approach to explore the microbes present in brain, liver and large intestine of 16 invasive Indian myna birds in Sydney, Australia. From this, we discovered seven novel viruses from the families Adenoviridae, Caliciviridae, Flaviviridae, Parvoviridae and Picornaviridae. Interestingly, each of the novel viruses identified shared less than 80% genomic similarity with their closest relatives from other avian species, indicative of a lack of detectable virus transmission between invasive mynas to native or domestic species. Of note, we also identified two coccidian protozoa, Isospora superbusi and Isospora greineri, from the liver and gut tissues of mynas. Overall, these data demonstrate that invasive mynas can harbor a diversity of viruses and other microorganisms such that ongoing pathogen surveillance in this species is warranted.

Efficacy of tangential irradiation with volumetric modulated arc therapy on scalp angiosarcoma using medical linac.

PURPOSE: To increase the superficial dose and reduce the brain dose for radiotherapy of scalp angiosarcoma, we propose a novel irradiation technique of tangential irradiation volumetric modulated arc therapy (TI-VMAT). METHODS: TI-VMAT and the conventional VMAT treatment plans for thirteen scalp angiosarcoma patients were created with a prescribed dose of 70 Gy. Each treatment was normalized to cover 95% of the planning target volume (PTV) with its prescribed dose. To realize TI-VMAT, an avoidance structure (AS) function was applied. AS was defined as a contour subtracted PTV by a certain space from the brain contour. TI-VMAT treatment plans for six different spaces between PTV and AS were developed and compared with the conventional VMAT treatment plan with respect to the following dosimetric parameters: homogeneity index (HI) and conformity index (CI) of the PTV, mean brain dose, and brain volume irradiated with 20% (V20% [cc]), 40% (V40% [cc]), 60% (V60% [cc]), 80% (V80% [cc]), and 100% (V100% [cc]) of the prescribed dose. RESULTS: HI and CI were comparable between TI-VMAT and the conventional VMAT, the mean brain dose for TI-VMAT with AS defined by a space of 2.0 cm and jaw tracking was 14.27 Gy, which was significantly lower than that for the conventional VMAT (21.20 Gy). In addition, dosimetric parameters such as V20% [cc] were significantly suppressed compared to those for high doses. CONCLUSION: Our proposed irradiation technique TI-VMAT shows the potential to reduce radiation doses in the brain with maintaining higher dose coverage on the PTV.

Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.

BACKGROUND: Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated. METHODS: Twenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Contour ProtégéAI was used to apply the AI-based segmentation. Three similarity indices, the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA), were evaluated and compared with manual delineations. In addition, radiation oncologists visually evaluated the delineation accuracies. RESULTS: Among patients with prostate cancer, the AI-based model demonstrated higher accuracy than the atlas-based on DSC, HD, and MDA for the bladder and rectum. Upon visual evaluation, some errors were observed in the atlas-based delineations when the boundary between the small bowel or the seminal vesicle and the bladder was unclear. For patients with head and neck cancer, no significant differences were observed between the two models for almost all OARs, except small delineations such as the optic chiasm and optic nerve. The DSC tended to be lower when the HD and the MDA were smaller in small volume delineations. CONCLUSIONS: In terms of efficiency, the processing time for head and neck cancers was much shorter than manual delineation. While quantitative evaluation with AI-based segmentation was significantly more accurate than atlas-based for prostate cancer, there was no significant difference for head and neck cancer. According to the results of visual evaluation, less necessity of manual correction in AI-based segmentation indicates that the segmentation efficiency of AI-based model is higher than that of atlas-based model. The effectiveness of the AI-based model can be expected to improve the segmentation efficiency and to significantly shorten the delineation time.

Evaluation of the Correction Methods Using Age and BMI for Estimating CT Organ Dose Using a Radiophotoluminescence Glass Dosimeter and a Monte Carlo-based Dose Calculator.

ABSTRACT: The size-specific dose estimates (SSDE) have been recommended to replace the volume computed tomography dose index (CTDIvol) because it takes patient size into account. On the other hand, organ dose is thought to be a more appropriate quantity in the radiation protection field due to its correlation with radiation risk. The web-based computed tomography (CT) dose calculator WAZA-ARIv2 only offers organ doses for adults with four different body shapes and for children with five different ages. Since the American Association of Physicists in Medicine (AAPM) offers the conversion factors for SSDE and the correlation of SSDE with organ dose has been demonstrated, implementation of the conversion table might improve the accuracy of WAZA-ARIv2. This study aimed to evaluate a body mass index (BMI)-based and age-based correction method for estimation of the organ dose by using a radiophotoluminescence dosimeter (RGD), an anthropomorphic phantom, and the dose calculator WAZA-ARIv2. RGDs were individually calibrated by using an ISOVOLT TITAN-320 x-ray generator. The ratio of the SSDE conversion factors (CFSSDE) was used as the comparison index. For the BMI-based correction method, the ratio of CFSSDE values for the adult phantoms was expected to be 1.065, and the average ratio of the organ doses for the adult phantoms was 1.163 ± 0.169. For the age-based correction method, the ratio of CFSSDE value for 5- and 10-y-old pediatric phantoms was expected to be 0.889, and the ratios of the organ doses were 0.866 ± 0.024 and 0.909 ± 0.047 for the WAZA-ARIv2 dosimetry system and RGD dosimetry system, respectively. In conclusion, both evaluations of the experimental results showed the consistency between WAZA-ARIv2 and the SSDE conversion factor table. Moreover, the importance of taking the measurement position into account when applying the mass attenuation coefficient was demonstrated according to this study.

Direct energy spectrum measurement of X-ray from a clinical linac.

A realistic X-ray energy spectrum is essential for accurate dose calculation using the Monte Carlo (MC) algorithm. An energy spectrum for dose calculation in the radiation treatment planning system is modeled using the MC algorithm and adjusted to obtain acceptable agreement with the measured percent depth dose (PDD) and off-axis ratio. The simulated energy spectrum may not consistently reproduce a realistic energy spectrum. Therefore, direct measurement of the X-ray energy spectrum from a linac is necessary to obtain a realistic spectrum. Previous studies have measured low photon fluence directly, but the measurement was performed with a nonclinical linac with a thick target and a long target-to-detector distance. In this study, an X-ray energy spectrum from a clinical linac was directly measured using a NaI(Tl) scintillator at an ultralow dose rate achieved by adjusting the gun grid voltage. The measured energy spectrum was unfolded by the Gold algorithm and compared with a simulated spectrum using statistical tests. Furthermore, the PDD was calculated using an unfolded energy spectrum and a simulated energy spectrum was compared with the measured PDD to evaluate the validity of the unfolded energy spectrum. Consequently, there was no significant difference between the unfolded and simulated energy spectra by nonparametric, Wilcoxon's rank-sum, chi-square, and two-sample Kolmogorov-Smirnov tests with a significance level of 0.05. However, the PDD calculated from the unfolded energy spectrum better agreed with the measured compared to the calculated PDD results from the simulated energy spectrum. The adjustment of the incident electron parameters using MC simulation is sensitive and takes time. Therefore, it is desirable to obtain the energy spectrum by direct measurement. Thus, a method to obtain the realistic energy spectrum by direct measurement was proposed in this study.