Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools

Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Effects of pre-exercise glycerol supplementation on dehydration, metabolic, kinematic, and thermographic variables in international race walkers

BACKGROUND

Due to the increase in global temperature, it is necessary to investigate solutions so that athletes competing in hot conditions can perform in optimal conditions avoiding loss of performance and health problems. Therefore, this study aims to evaluate the effect of pre-exercise glycerol supplementation during a rectangular test at ambient temperature mid (28.2ºC) on dehydration variables in international race walkers.

METHODS

Eight international male race walkers (age: 28.0 years (4.4); weight: 65.6 kg (6.6); height: 180.0 cm (5.0); fat mass: 6.72% (0.66); muscle mass: 33.3 kg (3.3); VO2MAX: 66.5 ml · kg-1·min-1 (1.9)) completed this randomized crossover design clinical trial. Subjects underwent two interventions: they consumed placebo (n = 8) and glycerol (n = 8) acutely, before a rectangular test where dehydration, RPE, metabolic, kinematic, and thermographic variables were analyzed before, during and after the test.

RESULTS

After the intervention, significant differences were found between groups in body mass in favor of the placebo (Placebo: -2.23 kg vs Glycerol: -2.48 kg; p = 0.033). For other variables, no significant differences were found.

CONCLUSION

Therefore, pre-exercise glycerol supplementation was not able to improve any dehydration, metabolic, kinematic, or thermographic variables during a rectangular test at temperature mid in international race walkers. Possibly, a higher environmental temperature could have generated a higher metabolic and thermoregulatory stress, generating differences between groups like other previous scientific evidence.

Ultrasound-based 3D bone modelling in computer assisted orthopedic surgery - a review and future challenges

Computer-assisted orthopedic surgery requires precise representations of bone surfaces. To date, computed tomography constitutes the gold standard, but comes with a number of limitations, including costs, radiation and availability. Ultrasound has potential to become an alternative to computed tomography, yet suffers from low image quality and limited field-of-view. These shortcomings may be addressed by a fully automatic segmentation and model-based completion of 3D bone surfaces from ultrasound images. This survey summarizes the state-of-the-art in this field by introducing employed algorithms, and determining challenges and trends. For segmentation, a clear trend toward machine learning-based algorithms can be observed. For 3D bone model completion however, none of the published methods involve machine learning. Furthermore, data sets and metrics are identified as weak spots in current research, preventing development and evaluation of models that generalize well.

Surgical planning in virtual reality: a systematic review

Purpose

Virtual reality (VR) technology has emerged as a promising tool for physicians, offering the ability to assess anatomical data in 3D with visuospatial interaction qualities. The last decade has witnessed a remarkable increase in the number of studies focusing on the application of VR to assess patient-specific image data. This systematic review aims to provide an up-to-date overview of the latest research on VR in the field of surgical planning.

Approach

A comprehensive literature search was conducted based on the preferred reporting items for systematic reviews and meta-analyses covering the period from April 1, 2021 to May 10, 2023. It includes research articles reporting on preoperative surgical planning using patient-specific medical images in virtual reality using head-mounted displays. The review summarizes the current state of research in this field, identifying key findings, technologies, study designs, methods, and potential directions for future research.

Results

The selected studies show a positive impact on surgical decision-making and anatomy understanding compared to other visualization modalities. A substantial number of studies are reporting anecdotal evidence and case-specific outcomes. Notably, surgical planning using VR led to more frequent changes in surgical plans compared to planning with other visualization methods when surgeons reassessed their initial plans. VR demonstrated benefits in reducing planning time and improving spatial localization of pathologies.

Conclusions

Results show that the application of VR for surgical planning is still in an experimental stage but is gradually advancing toward clinical use. The diverse study designs, methodologies, and varying reporting hinder a comprehensive analysis. Some findings lack statistical evidence and rely on subjective assumptions. To strengthen evaluation, future research should focus on refining study designs, improving technical reporting, defining visual and technical proficiency requirements, and enhancing VR software usability and design. Addressing these areas could pave the way for an effective implementation of VR in clinical settings.

Technical advances in motion-robust MR thermometry

Proton resonance frequency shift (PRFS) MR thermometry is the most common method used in clinical thermal treatments because of its fast acquisition and high sensitivity to temperature. However, motion is the biggest obstacle in PRFS MR thermometry for monitoring thermal treatment in moving organs. This challenge arises because of the introduction of phase errors into the PRFS calculation through multiple methods, such as image misregistration, susceptibility changes in the magnetic field, and intraframe motion during MRI acquisition. Various approaches for motion correction have been developed for real-time, motion-robust, and volumetric MR thermometry. However, current technologies have inherent trade-offs among volume coverage, processing time, and temperature accuracy. These tradeoffs should be considered and chosen according to the thermal treatment application. In hyperthermia treatment, precise temperature measurements are of increased importance rather than the requirement for exceedingly high temporal resolution. In contrast, ablation procedures require robust temporal resolution to accurately capture a rapid temperature rise. This paper presents a comprehensive review of current cutting-edge MRI techniques for motion-robust MR thermometry, and recommends which techniques are better suited for each thermal treatment. We expect that this study will help discern the selection of motion-robust MR thermometry strategies and inspire the development of motion-robust volumetric MR thermometry for practical use in clinics.

Mass-manufacturable scintillation-based optical fiber dosimeters for brachytherapy

Scintillation-based fiber dosimeters are a powerful tool for minimally invasive localized real-time monitoring of the dose rate during Low Dose Rate (LDR) and High Dose Rate (HDR) brachytherapy (BT). This paper presents the design, fabrication, and characterization of such dosimeters, consisting of scintillating sensor tips attached to polymer optical fiber (POF). The sensor tips consist of inorganic scintillators, i.e. Gd2O2S:Tb for LDR-BT, and Y2O3:Eu+4YVO4:Eu for HDR-BT, dispersed in a polymer host. The shape and size of the tips are optimized using non-sequential ray tracing simulations towards maximizing the collection and coupling of the scintillation signal into the POF. They are then manufactured by means of a custom moulding process implemented on a commercial hot embossing machine, paving the way towards series production. Dosimetry experiments in water phantoms show that both the HDR-BT and LDR-BT sensors feature good consistency in the magnitude of the average photon count rate and that the photon count rate signal is not significantly affected by variations in sensor tip composition and geometry. Whilst individual calibration remains necessary, the proposed dosimeters show great potential for in-vivo dosimetry for brachytherapy.

Federated learning for medical image analysis: A survey

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.

Machine learning based on functional and structural connectivity in mild cognitive impairment

OBJECTIVE

Alzheimer's disease (AD) is a chronic, degenerative neurological disorder characterized by progressive cognitive decline and mental behavioral abnormalities. Mild cognitive impairment (MCI) is regarded as a transitional stage in the progression from normal elderly individuals to patients with AD. While studies have identified abnormalities in brain connectivity in patients with MCI, including functional and structural connectivity, accurately identifying patients with MCI in clinical screening remains challenging. We hypothesized that utilizing machine learning (ML) based on both functional and structural connectivity could yield meaningful results in distinguishing between patients with MCI and normal elderly individuals, so as to provide valuable information for early diagnosis and precise evaluation of patients with MCI.

METHODS

Following clinical criteria, we recruited 32 patients with MCI for the patient group, and 32 normal elderly individuals for the control group. All subjects underwent examinations for resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). Subsequently, significant functional and structural connectivity features were selected and combined with a support vector machine for classification of the patient and control groups.

RESULTS

We observed significantly different functional connectivity in the frontal lobe and putamen between the MCI group and normal controls. The results based on functional connectivity features demonstrated a classification accuracy of 71.88% and an area under the curve (AUC) value of 0.78. In terms of structural connectivity, we found that decreased fractional anisotropy in patients with MCI was significantly associated with Montreal Cognitive Assessment scores, specifically in regions such as the precuneus and cingulate gyrus. The classification results using the structural connectivity feature yielded an accuracy of 92.19% and an AUC value of 0.99. Lastly, combining functional and structural connectivity features resulted in a classification accuracy and AUC value of 93.75% and 0.99, respectively.

CONCLUSIONS

In this study, we demonstrated a high classification performance, underscoring the potential of both brain functional and structural connectivity in distinguishing patients with MCI from normal elderly individuals. Furthermore, the integration of functional connectivity and structural connectivity features indicated that utilizing rs-fMRI and DTI could enhance the accuracy and specificity of identifying patients with MCI compared with relying on a single neuroimaging technique.

Review of robotic systems for thoracoabdominal puncture interventional surgery

Cancer, with high morbidity and high mortality, is one of the major burdens threatening human health globally. Intervention procedures via percutaneous puncture have been widely used by physicians due to its minimally invasive surgical approach. However, traditional manual puncture intervention depends on personal experience and faces challenges in terms of precisely puncture, learning-curve, safety and efficacy. The development of puncture interventional surgery robotic (PISR) systems could alleviate the aforementioned problems to a certain extent. This paper attempts to review the current status and prospective of PISR systems for thoracic and abdominal application. In this review, the key technologies related to the robotics, including spatial registration, positioning navigation, puncture guidance feedback, respiratory motion compensation, and motion control, are discussed in detail.

Measurement of tibial slope using biplanar stereoradiography (EOS®)

OBJECTIVES

Posterior tibial slope (PTS) is an important anatomic parameter of the knee related to anteroposterior instability. Biplanar stereoradiography allows for simultaneous low-dose acquisition of anteroposterior and lateral views with 3D capability, enabling separate lateral and medial plateau analyses. We aimed to evaluate the possibility and compare the reproducibility of measuring medial and lateral PTS on EOS® images with two different patient positionings and compare it with CT of the knees as the gold standard.

METHODS

This is a retrospective study including volunteers who underwent lower limb stereoradiography and knee CT from 01/08/2016 to 07/31/2019. Sixty legs from 30 patients were studied. PTS were measured using stereoradiography and CT by two radiologists. Intraclass correlation was used to calculate intrarater and interrater reproducibilities. Pearson's correlation coefficients were used to calculate the correlation between stereoradiography and CT. We also compared the reproducibility of the stereoradiography of volunteers with 2 different positionings.

RESULTS

The mean stereoradiography PTS values for right and left knees were as follows: lateral, 12.2° (SD: 4.1) and 10.1° (SD: 3.5); medial,12.2° (SD: 4.4) and 11.6° (SD: 3.9). CT PTS mean values for right and left knee are as follows: lateral, 10.3° (SD:2.5) and 10.6° (SD: 2.8); medial: 8.7° (SD: 3.7) and 10.4° (SD: 3.5). Agreement between CT and EOS for angles between lateral and medial PTS was good (right, 0.874; left, 0.871). Regarding patient positioning on stereoradiography, interrater and intrarater reproducibilities were greater for patients with nonparallel feet (0.738-0.883 and 0.870-0.975).

CONCLUSIONS

Stereoradiography allows for appropriate delineation of tibial plateaus, especially in patients with nonparallel feet, for the purpose of measuring PTS. The main advantage is lower radiation doses compared to radiography and CT.

Advanced deep learning-based image reconstruction in lumbar spine MRI at 0.55 T - Effects on image quality and acquisition time in comparison to conventional deep learning-based reconstruction

Objectives

To evaluate an optimized deep leaning-based image post-processing technique in lumbar spine MRI at 0.55 T in terms of image quality and image acquisition time.

Materials and methods

Lumbar spine imaging was conducted on 18 patients using a 0.55 T MRI scanner, employing conventional (CDLR) and advanced (ADLR) deep learning-based post-processing techniques. Two musculoskeletal radiologists visually evaluated the images using a 5-point Likert scale to assess image quality and resolution. Quantitative assessment in terms of signal intensities (SI) and contrast ratios was performed by region of interest measurements in different body-tissues (vertebral bone, intervertebral disc, spinal cord, cerebrospinal fluid and autochthonous back muscles) to investigate differences between CDLR and ADLR sequences.

Results

The images processed with the advanced technique (ADLR) were rated superior to the conventional technique (CDLR) in terms of signal/contrast, resolution, and assessability of the spinal canal and neural foramen. The interrater agreement was moderate for signal/contrast (ICC = 0.68) and good for resolution (ICC = 0.77), but moderate for spinal canal and neuroforaminal assessability (ICC = 0.55). Quantitative assessment showed a higher contrast ratio for fluid-sensitive sequences in the ADLR images. The use of ADLR reduced image acquisition time by 44.4%, from 14:22 min to 07:59 min.

Conclusions

Advanced deep learning-based image reconstruction algorithms improve the visually perceived image quality in lumbar spine imaging at 0.55 T while simultaneously allowing to substantially decrease image acquisition times.

Clinical relevance

Advanced deep learning-based image post-processing techniques (ADLR) in lumbar spine MRI at 0.55 T significantly improves image quality while reducing image acquisition time.

Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer

Purpose

Segmentation of clinical target volumes (CTV) on medical images can be time-consuming and is prone to interobserver variation (IOV). This is a problem for online adaptive radiation therapy, where CTV segmentation must be performed every treatment fraction, leading to longer treatment times and logistic challenges. Deep learning (DL)-based auto-contouring has the potential to speed up CTV contouring, but its current clinical use is limited. One reason for this is that it can be time-consuming to verify the accuracy of CTV contours produced using auto-contouring, and there is a risk of bias being introduced. To be accepted by clinicians, auto-contouring must be trustworthy. Therefore, there is a need for a comprehensive commissioning framework when introducing DL-based auto-contouring in clinical practice. We present such a framework and apply it to an in-house developed DL model for auto-contouring of the CTV in rectal cancer patients treated with MRI-guided online adaptive radiation therapy.

Methods and Materials

The framework for evaluating DL-based auto-contouring consisted of 3 steps: (1) Quantitative evaluation of the model's performance and comparison with IOV; (2) Expert observations and corrections; and (3) Evaluation of the impact on expected volumetric target coverage. These steps were performed on independent data sets. The framework was applied to an in-house trained nnU-Net model, using the data of 44 rectal cancer patients treated at our institution.

Results

The framework established that the model's performance after expert corrections was comparable to IOV, and although the model introduced a bias, this had no relevant impact on clinical practice. Additionally, we found a substantial time gain without reducing quality as determined by volumetric target coverage.

Conclusions

Our framework provides a comprehensive evaluation of the performance and clinical usability of target auto-contouring models. Based on the results, we conclude that the model is eligible for clinical use.

Visualization of rat tendon in three dimensions using micro-Computed Tomography

Micro-computed tomography (CT) is an X-ray-based imaging modality that produces three-dimensional (3D), high-resolution images of whole-mount tissues, but is typically limited to dense tissues, such as bone. The X-rays readily pass-through tendons, rendering them transparent. Contrast-enhancing chemical stains have been explored, but their use to improve contrast in different tendon types and across developmental stages for micro-CT imaging has not been systematically evaluated. Therefore, we investigated how phosphotungstic acid (PTA) staining and tissue hydration impacts tendon contrast for micro-CT imaging. We showed that PTA staining increased X-ray absorption of tendon to enhance tissue contrast and obtain 3D micro-CT images of immature (postnatal day 21) and sexually mature (postnatal day 50) rat tendons within the tail and hindlimb. Further, we demonstrated that tissue hydration state following PTA staining significantly impacts soft tissue contrast. Using this method, we also found that tail tendon fascicles appear to cross between fascicle bundles. Ultimately, contrast-enhanced 3D micro-CT imaging will lead to better understanding of tendon structure, and relationships between the bone and soft tissues.•Simple tissue fixation and staining technique enhances soft tissue contrast for tendon visualization using micro-CT.•3D tendon visualization in situ advances understanding of musculoskeletal tissue structure and organization.

Head-to-head comparison of prostate-specific membrane antigen positron emission tomography/computed tomography and multiparametric magnetic resonance imaging in the detection of biochemical recurrence of prostate cancer: a systematic review and meta-analysis

AIM

Our main goal of this meta-analytical analysis was to evaluate the diagnostic effectiveness of prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) against multiparametric magnetic resonance imaging (mpMRI) in the context of identifying biochemical recurrence in patients with prostate cancer (PCa).

MATERIALS AND METHODS

A thorough search covering articles published until March 2023 was carried out across major databases such as PubMed, Embase, and Web of Science. Studies examining the direct comparison of PSMA PET/CT and mpMRI in patients with PCa suffering biochemical recurrence were included in the inclusion criteria. Using the renowned Quality Assessment of Diagnostic Performance Studies-2 technique, each study's methodological rigor was assessed.

RESULTS

We analyzed data from six eligible studies involving 290 patients in total. The combined data showed that for PSMA PET/CT and mpMRI, respectively, the pooled overall detection rates for recurrent PCa after definitive treatment were 0.69 (95% confidence interval [CI]: 0.45-0.89) and 0.70 (95% CI: 0.44-0.91). The detection rates for local recurrence were specifically 0.52 (95% CI: 0.39-0.65) and 0.62 (95% CI: 0.31-0.89), while they were 0.50 (95% CI: 0.26-0.74) and 0.32 (95% CI: 0.18-0.48) for lymph node metastasis. Notably, there was no discernible difference between the two imaging modalities in terms of the overall detection rate (P = 0.95). The detection rates for local recurrence and lymph node metastasis did not differ statistically significantly (P = 0.55, 0.23).

CONCLUSION

The performance of PSMA PET/CT and mpMRI in identifying biochemical recurrence in PCa appears to be comparable. However, the meta-analysis' findings came from research with modest sample sizes. In this context, more extensive research should be conducted in the future.

Survey of research attitudes of RTTs working in Scotland: A Scottish radiographer research forum collaboration

Purpose

Evidence-based practice (EBP) is associated with improved treatment outcomes and survival in cancer patients. Engagement from therapeutic radiographers/radiation therapists (RTTs) in research, has been identified as a challenge. The aim of this survey was to gain an understanding of RTT attitudes to research in Scotland.

Methods

This was a prospective study that used a mixed method cross-sectional survey, with an online survey tool (Webropol). The survey was developed with collaborators from all Scottish Radiotherapy Centres (n = 5) and piloted by 6 conveniently sampled RTT and validated by 8 experienced RTTs. The survey comprised 29 items, 7 selection-based demographic questions, and 18 statements with a Likert 5-point metric scale rating (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). The validity was measured with the content validity index (CVI) and item-CVI by 8 experienced RTTs. Low scoring I-CVI (<0.78) questions were removed.A total of 314 RTTs working in Scottish Radiotherapy Centres were invited to participate. Approvals were given by each Head of department (HoD), who also confirmed number of RTTs.

Results

A total of 102/314 (32.5 %) RTTs responded. The majority of RTTs agreed they were confident they had sufficient research skills to inform EBP (n = 58/102, 56.9 %), felt confident discussing EBP with colleagues (n = 67, 65.7 %) and felt research was important for role development (n = 89, 87.2 %). Low mean scores and standard deviation (SD) were observed for the following: "I know how to get involved in research" 3.2 (1.2), "I have been given the opportunity to get involved in research" 3.2 (1.1), and "I am well informed about current research projects in my department" 3.2 (1.1). 57.8 % (n = 59) of RTTs disagreed they were confident adequate time would be provided to be involved in research.

Conclusion

The survey results demonstrated a predominantly positive attitude to research amongst RTTs working in Scottish centres, with most common perceived barriers being access to protected time and staff; training, and support.

Investigation on radiation attenuation properties of natural stone samples traded in Turkey

Although radioprotection is globally regulated for high radiation exposure, formal guidelines concerning natural radiation exposure that causes stochastic radiation effects have not been established. The commonly used absorbers lead, tungsten and bismuth are not appropriate for the radioprotection of the public. The primary barrier against natural radiation is the structural components of buildings, where humans spend approximately 80 % of their lifetime. Natural stones are secondary materials, which are applied to walls and floors as coating. This study focuses on the radiation shielding properties of natural stones. Herein, the samples of marble, granite and sedimentary rock traded in Turkey were examined to determine whether they can serve as passive or alternative radioprotection materials. Notable gamma absorption rates were obtained when an intense gamma source with an energy of 662 keV was used. The calculated mass attenuation coefficients were comparable with those of copper and aluminium. The mean mass attenuation coefficient was 0.082 cm2/g for marbles and granites and 0.080 cm2/g for sedimentary rocks. Considering the stochastic effects, the use of the natural stones as construction materials is anticipated to significantly reduce the natural radiation level in inhabitable regions. Furthermore, natural stones can be used in clinics as secondary radiation shields against low-energy gamma and x-rays.

Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that the data-driven model has produced inconsistent results due to the data outliers, which significantly impact model reliability and accuracy. The present study was carried out to assess the impact of data outliers on a recently developed Irish Water Quality Index (IEWQI) model, which relies on data-driven techniques. To the author's best knowledge, there has been no systematic framework for evaluating the influence of data outliers on such models. For the purposes of assessing the outlier impact of the data outliers on the water quality (WQ) model, this was the first initiative in research to introduce a comprehensive approach that combines machine learning with advanced statistical techniques. The proposed framework was implemented in Cork Harbour, Ireland, to evaluate the IEWQI model's sensitivity to outliers in input indicators to assess the water quality. In order to detect the data outlier, the study utilized two widely used ML techniques, including Isolation Forest (IF) and Kernel Density Estimation (KDE) within the dataset, for predicting WQ with and without these outliers. For validating the ML results, the study used five commonly used statistical measures. The performance metric (R2) indicates that the model performance improved slightly (R2 increased from 0.92 to 0.95) in predicting WQ after removing the data outlier from the input. But the IEWQI scores revealed that there were no statistically significant differences among the actual values, predictions with outliers, and predictions without outliers, with a 95 % confidence interval at p < 0.05. The results of model uncertainty also revealed that the model contributed <1 % uncertainty to the final assessment results for using both datasets (with and without outliers). In addition, all statistical measures indicated that the ML techniques provided reliable results that can be utilized for detecting outliers and their impacts on the IEWQI model. The findings of the research reveal that although the data outliers had no significant impact on the IEWQI model architecture, they had moderate impacts on the rating schemes' of the model. This finding indicated that detecting the data outliers could improve the accuracy of the IEWQI model in rating WQ as well as be helpful in mitigating the model eclipsing problem. In addition, the results of the research provide evidence of how the data outliers influenced the data-driven model in predicting WQ and reliability, particularly since the study confirmed that the IEWQI model's could be effective for accurately rating WQ despite the presence of the data outliers in the input. It could occur due to the spatio-temporal variability inherent in WQ indicators. However, the research assesses the influence of data input outliers on the IEWQI model and underscores important areas for future investigation. These areas include expanding temporal analysis using multi-year data, examining spatial outlier patterns, and evaluating detection methods. Moreover, it is essential to explore the real-world impacts of revised rating categories, involve stakeholders in outlier management, and fine-tune model parameters. Analysing model performance across varying temporal and spatial resolutions and incorporating additional environmental data can significantly enhance the accuracy of WQ assessment. Consequently, this study offers valuable insights to strengthen the IEWQI model's robustness and provides avenues for enhancing its utility in broader WQ assessment applications. Moreover, the study successfully adopted the framework for evaluating how data input outliers affect the data-driven model, such as the IEWQI model. The current study has been carried out in Cork Harbour for only a single year of WQ data. The framework should be tested across various domains for evaluating the response of the IEWQI model's in terms of the spatio-temporal resolution of the domain. Nevertheless, the study recommended that future research should be conducted to adjust or revise the IEWQI model's rating schemes and investigate the practical effects of data outliers on updated rating categories. However, the study provides potential recommendations for enhancing the IEWQI model's adaptability and reveals its effectiveness in expanding its applicability in more general WQ assessment scenarios.

Investigating knowledge of DRLs, image quality and radiation dose in PET/CT and CT imaging among medical imaging professionals

Objective

To investigate the knowledge of diagnostic reference levels (DRLs), image quality, radiation dose and protocol parameters among Jordanian medical imaging professionals (MIPs) involved in PET/CT and CT scan procedures.

Materials and methods

A questionnaire was designed and distributed to MIPs in Jordan. The survey comprised four sections: demographic data, MIP knowledge on dose/protocol parameters, image quality, and DRLs. Statistical analyses were performed utilizing Pearson's correlation, t-tests, ANOVA, and linear regression, with a significance level of 95 % and a p-value threshold of <0.05.

Results

The study involved 147 participants. Most respondents were male (76.2 %), and most were aged 26-35 years (44.2 %). Approximately 51 % held a bachelor's degree, and the most common range of experience was 3-5 years (28.6 %). Participants showed a moderate level of knowledge regarding dose and protocol parameters, with a mean score of 61.8 %. The mean scores for knowledge of image quality and DRLs were 45.2 % and 44.8 %, respectively. The age group of the MIPs and the total experience were found to have a significant impact on the knowledge of the dose and protocol parameters, as well as the DRLs. Additionally, experience was found to have a significant influence on knowledge of the dose and protocol parameters. The study revealed a positive and significant effect of MIPs' knowledge of dose/protocol parameters and image quality on their knowledge of DRLs.

Conclusions

This study indicates that professionals across five specialties who are engaged in PET/CT and CT imaging possess a moderate understanding of dosage and protocol parameters. However, there is a notable gap in knowledge regarding DRLs and image quality. To address this issue, it is recommended that MIPs actively engage in educational programs emphasizing exposure parameters and their impact on image quality. Additionally, access to comprehensive education and training programs will enable MIPs to grasp the complexities of DRLs and their implications, facilitating their implementation in clinical practice.

Application of MR Images in Radiotherapy Planning for brain tumor Based on Deep Learning

PURPOSE

Explore the function and dose calculation accuracy of MRI images in radiotherapy planning through deep learning methods.

METHODS

131 brain tumor patients undergoing radiotherapy with previous MR and CT images were recruited for this study. A new series of MRI from the aligned MR was firstly registered to CT images strictly using MIM software and then resampled. A deep learning method (U-NET) was used to establish a MRI-to-CT conversion model, for which 105 patient images were used as the training set and 26 patient images were used as the tuning set. Data from additional 8 patients were collected as the test set, and the accuracy of the model was evaluated from a dosimetric standpoint.

RESULTS

Comparing the synthetic CT images with the original CT images, the difference in dosimetric parameters D98, D95, D2 and Dmean of PTV in 8 patients was less than 0.5%. The gamma passed rates of PTV and whole body volume were: 1%/1mm: 93.96%±6.75%, 2%/2mm: 99.87%±0.30%, 3%/3mm: 100.00%±0.00%; and 1%/1mm: 99.14%±0.80%, 2%/2mm: 99.92%±0.08%, 3%/3mm: 99.99%±0.01%.

CONCLUSION

MR images can be used both in delineation and treatment efficacy evaluation and in dose calculation. Using the deep learning way to convert MR image to CT image is a viable method and can be further used in dose calculation.

Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN

PURPOSE

Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans.

METHODS

A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling.

RESULTS

Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05).

CONCLUSION

The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.

Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively.Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom.Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97-0.99) and decreasing the RMSE (62-55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02-0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU).Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the potential of applying this CBCT system in more advanced scenarios by comparing the quality against fan-beam CT.

Phase contrast micro-CT with adjustable in-slice spatial resolution at constant magnification

Objective.To report on a micro computed tomography (micro-CT) system capable of x-ray phase contrast imaging and of increasing spatial resolution at constant magnification.Approach.The micro-CT system implements the edge illumination (EI) method, which relies on two absorbing masks with periodically spaced transmitting apertures in the beam path; these split the beam into an array of beamlets and provide sensitivity to the beamlets' directionality, i.e. refraction. In EI, spatial resolution depends on the width of the beamlets rather than on the source/detector point spread function (PSF), meaning that resolution can be increased by decreasing the mask apertures, without changing the source/detector PSF or the magnification.Main results.We have designed a dedicated mask featuring multiple bands with differently sized apertures and used this to demonstrate that resolution is a tuneable parameter in our system, by showing that increasingly small apertures deliver increasingly detailed images. Phase contrast images of a bar pattern-based resolution phantom and a biological sample (a mouse embryo) were obtained at multiple resolutions.Significance.The new micro-CT system could find application in areas where phase contrast is already known to provide superior image quality, while the added tuneable resolution functionality could enable more sophisticated analyses in these applications, e.g. by scanning samples at multiple scales.

Radiosurgical thalamotomy for essential tremor: state of the art, current challenges and future directions

INTRODUCTION

Essential tremor (ET) is the most frequent movement disorder, affecting up to 5% of adults > 65 years old. In 30-50% of cases, optimal medical management provides insufficient tremor relief and surgical options are considered. Thalamotomy is a time-honored intervention, which can be performed using radiofrequency (RF), stereotactic radiosurgery (SRS), or magnetic resonance-guided focused ultrasounds (MRgFUS). While the latter has received considerable attention in the last decade, SRS has consistently been demonstrated as an effective and well-tolerated option.

AREAS COVERED

This review discusses the evidence on SRS thalamotomy for ET. Modern workflows and emerging techniques are detailed. Current outcomes are analyzed, with a specific focus on tremor reduction, complications and radiological evolution of the lesions. Challenges for the field are highlighted.

EXPERT OPINION

SRS thalamotomy improves tremor in > 80% patients. The efficacy appears comparable to other modalities, including DBS, RF and MRgFUS. Side effects result mostly from idiosyncratic hyper-responses to radiation, which occur in up to 10% of treatments, are usually self-resolving, and are symptomatic in < 4% of patients. Future research should focus on accumulating more data on bilateral treatments, collecting long-term outcomes, refining targeting, and improving lesion consistency.

Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study

Objective. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters. This limited robustness hinders the generalizable validity of radiomics-assisted models. Our aim is to investigate a possible harmonization strategy based on matching image quality to improve feature robustness.Approach.We acquired CT scans of a phantom with two scanners across different dose levels and percentages of Iterative Reconstruction algorithms. The detectability index was used as a comprehensive task-based image quality metric. A statistical analysis based on the Intraclass Correlation Coefficient was performed to determine if matching image quality/appearance could enhance the robustness of radiomics features extracted from the phantom images. Additionally, an Artificial Neural Network was trained on these features to automatically classify the scanner used for image acquisition.Main results.We found that the ICC of the features across protocols providing a similar detectability index improves with respect to the ICC of the features across protocols providing a different detectability index. This improvement was particularly noticeable in features relevant for distinguishing between scanners.Significance.This preliminary study demonstrates that a harmonization based on image quality/appearance matching could improve radiomics features robustness and heterogeneous protocols can be used to obtain a similar image appearance in terms of the detectability index. Thus protocols with a lower dose level could be selected to reduce the amount of radiation dose delivered to the patient and simultaneously obtain a more robust quantitative analysis.

Multimodal radiotherapy dose prediction using a multi-task deep learning model

BACKGROUND

In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt-60-based systems and linear accelerators with C-arm, O-ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality-specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient-specific basis. However, manually generating treatment plans for each modality across every patient is time-consuming and clinically impractical.

PURPOSE

We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning-based convolutional neural networks. The baseline network performs a single-task (ST), predicting dose for a single modality. Our proposed multi-task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient-specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient-specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning.

METHODS

The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two-tailed Wilcoxon signed-rank test.

RESULTS

Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [-0.0865, -0.0712]).

CONCLUSION

Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre-treatment decision-making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site-agnostic dose prediction model, enhancing its generalizability and applicability.

Error detection for radiotherapy planning validation based on deep learning networks

BACKGROUND

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks.

PURPOSE

The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations.

METHOD

We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators.

RESULTS

The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors.

CONCLUSION

When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study

OBJECTIVE

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment.

METHODS

We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively.

RESULTS

In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively).

CONCLUSIONS

Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

Atherosclerosis is the primary cause of cardiovascular disease, resulting in mortality, elevated healthcare costs, diminished productivity, and reduced quality of life for individuals and their communities. This is exacerbated by the limited understanding of its underlying causes and limitations in current therapeutic interventions, highlighting the need for sophisticated models of atherosclerosis. This review critically evaluates the computational and biological models of atherosclerosis, focusing on the study of hemodynamics in atherosclerotic coronary arteries. Computational models account for the geometrical complexities and hemodynamics of the blood vessels and stenoses, but they fail to capture the complex biological processes involved in atherosclerosis. Different in vitro and in vivo biological models can capture aspects of the biological complexity of healthy and stenosed vessels, but rarely mimic the human anatomy and physiological hemodynamics, and require significantly more time, cost, and resources. Therefore, emerging strategies are examined that integrate computational and biological models, and the potential of advances in imaging, biofabrication, and machine learning is explored in developing more effective models of atherosclerosis.

Individual patient radiation dose tracking: Perceptions of radiographers in South Africa

INTRODUCTION

Medical imaging examinations that make use of ionising radiation provide valuable information towards patient management. Literature suggests that there is a significant rise in the number of patient referrals for such examinations. The concept "individual patient radiation dose tracking" (IPRDT) is introduced to optimise radiation monitoring. Many countries across the globe explored and implemented methods to enhance and promote the justification and optimisation principles essential for patient radiation safety. In South Africa (SA), however, attention to IPRDT is limited.

METHODS

A qualitative research design was employed. Radiographers in the Western Cape Province of SA were purposefully sampled for participation in one-on-one, semi-structured interviews. Thematic analysis was applied to the transcribed interview data.

RESULTS

This paper presents a theme developed from the radiographer cohort of ten (10) participants. The theme: the need for creating awareness and implementing legislative support structures, was developed from the data, with the following supporting subthemes: 1) stakeholder awareness and 'buy-in' 2) continuous professional development and 3) mandated practice.

CONCLUSION

This study provides findings that are of value for patient radiation safety in SA by giving a voice to local stakeholders. Other countries that are conducting similar research investigations toward the integration of an IPRDT model, method, or framework, may also benefit from these findings.

IMPLICATIONS FOR PRACTICE

The effective integration of IPRDT into the clinical environment requires unison amongst the relevant stakeholders and clarity on the various professionals' roles and responsibilities. The findings of this study furthermore suggest the involvement of regulatory organisations for the provision of a mandated form of practice at national and international levels.

Characterizing the voxel-based approaches in radioembolization dosimetry with reDoseMC

BACKGROUND

Yttrium-90 (90 Y $^{90}{\rm {Y}}$ ) represents the primary radioisotope used in radioembolization procedures, while holmium-166 (166 Ho $^{166}{\rm {Ho}}$ ) is hypothesized to serve as a viable substitute for90 Y $^{90}{\rm {Y}}$ due to its comparable therapeutic potential and improved quantitative imaging. Voxel-based dosimetry for these radioisotopes relies on activity images obtained through PET or SPECT and dosimetry methods, including the voxel S-value (VSV) and the local deposition method (LDM). However, the evaluation of the accuracy of absorbed dose calculations has been limited by the use of non-ideal reference standards and investigations restricted to the liver. The objective of this study was to expand upon these dosimetry characterizations by investigating the impact of image resolutions, voxel sizes, target volumes, and tissue materials on the accuracy of90 Y $^{90}{\rm {Y}}$ and166 Ho $^{166}{\rm {Ho}}$ dosimetry techniques.

METHODS

A specialized radiopharmaceutical dosimetry software called reDoseMC was developed using the Geant4 Monte Carlo toolkit and validated by benchmarking the generated90 Y $^{90}{\rm {Y}}$ kernels with published data. The decay spectra of both90 Y $^{90}{\rm {Y}}$ and166 Ho $^{166}{\rm {Ho}}$ were also compared. Multiple VSV kernels were generated for the liver, lungs, soft tissue, and bone for isotropic voxel sizes of 1 mm, 2 mm, and 4 mm. Three theoretical phantom setups were created with 20 or 40 mm activity and mass density inserts for the same three voxel sizes. To replicate the limited spatial resolutions present in PET and SPECT images, image resolutions were modeled using a 3D Gaussian kernel with a Full Width at Half Maximum (FWHM) ranging from 0 to 16 mm and with no added noise. The VSV and LDM dosimetry methods were evaluated by characterizing their respective kernels and analyzing their absorbed dose estimates calculated on theoretical phantoms. The ground truth for these estimations was calculated using reDoseMC.

RESULTS

The decay spectra obtained through reDoseMC showed less than a 1% difference when compared to previously published experimental data for energies below 1.9 MeV in the case of90 Y $^{90}{\rm {Y}}$ and less than 1% for energies below 1.5 MeV for166 Ho $^{166}{\rm {Ho}}$ . Additionally, the validation kernels for90 Y $^{90}{\rm {Y}}$ VSV exhibited results similar to those found in published Monte Carlo codes, with source dose depositions having less than a 3% error margin. Resolution thresholds (FWHM thresh s ${\rm {FWHM}}_\mathrm{thresh}{\rm {s}}$ ), defined as resolutions that resulted in similar dose estimates between the LDM and VSV methods, were observed for90 Y $^{90}{\rm {Y}}$ . They were 1.5 mm for bone, 2.5 mm for soft tissue and liver, and 8.5 mm for lungs. For166 Ho $^{166}{\rm {Ho}}$ , the accuracy of absorbed dose deposition was found to be dependent on the contributions of absorbed dose from photons. Volume errors due to variations in voxel size impacted the final dose estimates. Larger target volumes yielded more accurate mean doses than smaller volumes. For both radioisotopes, the radial dose profiles for the VSV and LDM approximated but never matched the reference standard.

CONCLUSIONS

reDoseMC was developed and validated for radiopharmaceutical dosimetry. The accuracy of voxel-based dosimetry was found to vary widely with changes in image resolutions, voxel sizes, chosen target volumes, and tissue material; hence, the standardization of dosimetry protocols was found to be of great importance for comparable dosimetry analysis.

The IAEA remote audit of small field dosimetry for testing the implementation of the TRS-483 code of practice

BACKGROUND

The TRS‑483, an IAEA/AAPM International Code of Practice on dosimetry of small static photon fields, underwent testing via an IAEA coordinated research project (CRP). Alongside small field output factors (OFs) measurements using active dosimeters by CRP participants, the IAEA Dosimetry Laboratory received a mandate to formulate a remote small field dosimetry audit method using its passive dosimetry systems.

PURPOSE

This work aimed to develop a small field dosimetry audit methodology employing radiophotoluminescent dosimeters (RPLDs) and radiochromic films. The methodology was subsequently evaluated through a multicenter pilot study with CRP participants.

METHODS

The developments included designing and manufacturing a dosimeter holder set and the characterization of an RPLD system for measurements in small photon fields using the new holder. The audit included verification of small field OFs and lateral beam profiles for small fields. At first, treatment planning system (TPS) calculated OFs were checked against a reference data set that was available for conventional linacs. Second, calculated OFs were verified through the RPLD measurement of point doses in a machine-specific reference field, 4 cm × 4 cm, 2 cm × 2 cm, and 1 cm × 1 cm, corresponding size circular fields or nearest achievable field sizes. Lastly, profile checks in in-plane and cross-plane directions were done for the two smallest fields by comparing film measurements with TPS calculations at 20%, 50%, and 80% isodose levels.

RESULTS

RPLD correction factors for small field measurements were approximately unity. However, they influenced the dose determination's overall uncertainty in small fields, estimated at 2.30% (k = 1 level). Considering the previous experience in auditing reference beam output following the TRS-398 Code of Practice, the acceptance limit of 5% for the ratio of the dose determined by RPLD to the dose calculated by TPS, DRPLD/DTPS, was considered adequate. The multicenter pilot study included 15 participants from 14 countries (39 beams). Consistent with the previous findings, the results of the OF check against the reference data confirmed that TPSs tend to overestimate OFs for the smallest fields included in this exercise. All except three RPLD measurement results were within the acceptance limit, and the spread of results increased for smaller field sizes. The differences between the film measured and TPS calculated dose profiles were within 3 mm for most of the beams checked; deviated results revealed problems with TPS commissioning and calibration of the treatment unit collimation systems.

CONCLUSION

The newly developed small field dosimetry audit methodology proved effective and successfully complemented the CRP OF measurements by participants with RPLD audit results.

Clinical diagnostic reference levels in neuroradiology based on clinical indication

This study focuses on patient radiation exposure in interventional neuroradiology (INR) procedures, a field that has advanced significantly since its inception in the 1980s. INR employs minimally invasive techniques to treat complex cerebrovascular diseases in the head, neck, and spine. The study establishes diagnostic reference levels (DRLs) for three clinical indications (CIs): stroke (S), brain aneurysms (ANs), and brain arteriovenous malformation (AVM). Data from 209 adult patients were analyzed, and DRLs were determined in terms of various dosimetric and technical quantities. For stroke, the established DRLs median values were found to be 78 Gy cm2, 378 mGy, 118 mGy, 12 min, 442 images, and 15 runs. Similarly, DRLs for brain AN are 85 Gy cm2, 611 mGy, 95.5 mGy, 19.5, 717 images, and 26 runs. For brain AVM, the DRL's are 180 Gy cm2, 1144 mGy, 537 mGy, 36 min, 1375 images, and 31 runs. Notably, this study is unique in reporting DRLs for specific CIs within INR procedures, providing valuable insights for optimizing patient safety and radiation exposure management.

Development of a phantom for assessing the precision of setup in skin mark-less surface-guided radiotherapy

BACKGROUND

Surface-guided radiotherapy (SGRT) is adopted by several institutions; however, reports on the phantoms used to assess the precision of the SGRT setup are limited.

PURPOSE

The purpose of this study was to develop a phantom to verify the accuracy of the irradiation position during skin mark-less SGRT.

METHODS

An acrylonitrile butadiene styrene (ABS) plastic cube phantom with a diameter of 150 mm on each side containing a dummy target of 15 mm and two types of body surface-shaped phantoms (breast/face shape) that could be attached to the cube phantom were fabricated. Films can be inserted on four sides of the cubic phantom (left, right, anterior and posterior), and the center of radiation can be calculated by irradiating the dummy target with orthogonal MV beams. Three types of SGRT using a VOXELAN-HEV600M (Electronics Research&Development Corporation, Okayama, Japan) were evaluated using this phantom: (i) SGRTCT-a SGRT set-up based solely on a computed tomography (CT)-reference image. (ii) SGRTCT + CBCT-a method where cone beam computed tomography (CBCT) matching was performed after SGRTCT. (iii) SGRTScan-a resetup technique using a scan reference image obtained after completing the (ii) step.

RESULTS

Both the breast and face phantoms were recognized in the SGRT system without problems. SGRTScan ensure precision within 1 mm/1° for breast and face verification, respectively. All SGRT methods showed comparable rotational accuracies with no significant disparities.

CONCLUSIONS

The developed phantom was useful for verifying the accuracy of skin mark-less SGRT position matching. The SGRTScan demonstrated the feasibility of achieving skin-mark less SGRT with high accuracy, with deviations of less than 1 mm. Additional research is necessary to evaluate the suitability of the developed phantoms for use in various facilities and systems. This phantom could be used for postal surveys in the future.

Radiation therapy for ventricular arrhythmias

Ventricular arrhythmias (VA) can be life-threatening arrhythmias that result in significant morbidity and mortality. Catheter ablation (CA) is an invasive treatment modality that can be effective in the treatment of VA where medications fail. Recurrence occurs commonly following CA due to an inability to deliver lesions of adequate depth to cauterise the electrical circuits that drive VA or reach areas of scar responsible for VA. Stereotactic body radiotherapy is a non-invasive treatment modality that allows volumetric delivery of energy to treat circuits that cannot be reached by CA. It overcomes the weaknesses of CA and has been successfully utilised in small clinical trials to treat refractory VA. This article summarises the current evidence for this novel treatment modality and the steps that will be required to bring it to the forefront of VA treatment.

Analysis of the retraining strategies for multi-label text message classification in call/contact center systems

Today, in many areas of technology, we can come across applications of various artificial intelligence methods. They usually involve models trained on some specific pool of learning data. Sometimes, however, the data analyzed by these solutions can change its nature over time. This usually results in a decrease in classification efficiency. In such a case, the use of techniques to retrain the originally trained reference models should be considered. One of the industries where the nature of data changes quite dynamically over time is the broadly defined call/contact center systems. An example of a module that is often found in this type of system and that, due to frequently changing marketing campaigns, requires the use of learning techniques is the automatic classification of text data. The paper describes the process of retraining the original reference models used in a multi-label text message classification method dedicated directly to call/contact center systems applications. In order to carry out the retraining process, Polish-language data from the actual archives of a large commercial contact center system and English-language data extracted from a publicly available database were used. The study was conducted for models based on artificial neural networks and bidirectional encoder representations from transformer type models. In addition, two different retraining strategies were studied, the results of which were compared with data obtained from the operation of reference models. As a result of the research work, an improvement of up to 5% in classification efficiency, as described by the metric Emotica was obtained, which means that proper integration of the retraining process brings tangible benefits to the solution tested in the article. Thus, it can also benefit the solutions used in business.

A perspective on tumor radiation resistance following high-LET radiation treatment

High-linear energy transfer (LET) radiation is a promising alternative to conventional low-LET radiation for therapeutic gain against cancer owing to its ability to induce complex and clustered DNA lesions. However, the development of radiation resistance poses a significant barrier. The potential molecular mechanisms that could confer resistance development are translesion synthesis (TLS), replication gap suppression (RGS) mechanisms, autophagy, epithelial-mesenchymal transition (EMT) activation, release of exosomes, and epigenetic changes. This article will discuss various types of complex clustered DNA damage, their repair mechanisms, mutagenic potential, and the development of radiation resistance strategies. Furthermore, it highlights the importance of careful consideration and patient selection when employing high-LET radiotherapy in clinical settings.

Dose and DNA damage modelling of diffusing alpha-emitters radiation therapy using Geant4

PURPOSE

Diffusing alpha-emitters radiation therapy (DaRT) is a brachytherapy technique using α-particles to treat solid tumours. The high linear energy transfer (LET) and short range of α-particles make them good candidates for the targeted treatment of cancer. Treatment planning of DaRT requires a good understanding of the dose from α-particles and the other particles released in the 224Ra decay chain.

METHODS

The Geant4 Monte Carlo toolkit has been used to simulate a DaRT seed to better understand the dose contribution from all particles and simulate the DNA damage due to this treatment.

RESULTS

Close to the seed α-particles deliver the majority of dose, however at radial distances greater than 4 mm, the contribution of β-particles is greater. The RBE has been estimated as a function of number of double strand breaks (DSBs) and complex DSBs. A maximum seed spacing of 5.5 mm and 6.5 mm was found to deliver at least 20 Gy RBE weighted dose between the seeds for RBEDSB and RBEcDSB respectively.

CONCLUSIONS

The DNA damage changes with radial distance from the seed and has been found to become less complex with distance, which is potentially easier for the cell to repair. Close to the seed α-particles contribute the majority of dose, however the contribution from other particles cannot be neglected and may influence the choice of seed spacing.

Recent developments in the diagnosis of pancreatic neuroendocrine neoplasms

INTRODUCTION

Pancreatic Neuroendocrine Neoplasms (PanNENs) are characterized by a highly heterogeneous clinical and biological behavior, making their diagnosis challenging. PanNENs diagnostic work-up mainly relies on biochemical markers, pathological examination, and imaging evaluation. The latter includes radiological imaging (i.e. computed tomography [CT] and magnetic resonance imaging [MRI]), functional imaging (i.e. 68Gallium [68 Ga]Ga-DOTA-peptide PET/CT and Fluorine-18 fluorodeoxyglucose [18F]FDG PET/CT), and endoscopic ultrasound (EUS) with its associated procedures.

AREAS COVERED

This review provides a comprehensive assessment of the recent advancements in the PanNENs diagnostic field. PubMed and Embase databases were used for the research, performed from inception to October 2023.

EXPERT OPINION

A deeper understanding of PanNENs biology, recent technological improvements in imaging modalities, as well as progresses achieved in molecular and cytological assays, are fundamental players for the achievement of early diagnosis and enhanced preoperative characterization of PanNENs. A multimodal diagnostic approach is required for a thorough disease assessment.

Feasibility of patient-specific quality assurance (PSQA) for real-time robotic stereotactic body radiotherapy (SBRT) based on tumor motion traces

PURPOSE

To design a patient specific quality assurance (PSQA) process for the CyberKnife Synchrony system and quantify its dosimetric accuracy using a motion platform driven by patient tumor traces with rotation.

METHODS

The CyberKnife Synchrony system was evaluated using a motion platform (MODUSQA) and a SRS MapCHECK phantom. The platform was programed to move in the superior-inferior (SI) direction based on tumor traces. The detector array housed by the StereoPhan was placed on the platform. Extra rotational angles in pitch (head down, 4.0° ± 0.15° or 1.2° ± 0.1°) were added to the moving phantom to examine robot capability of angle correction during delivery. A total of 15 Synchrony patients were performed SBRT PSQA on the moving phantom. All the results were benchmarked by the PSQA results based on static phantom.

RESULTS

For smaller pitch angles, the mean gamma passing rates were 99.75% ± 0.87%, 98.63% ± 2.05%, and 93.11% ± 5.52%, for 3%/1 mm, 2%/1 mm, and 1%/1 mm, respectively. Large discrepancy in the passing rates was observed for different pitch angles due to limited angle correction by the robot. For larger pitch angles, the corresponding mean passing rates were dropped to 93.00% ± 10.91%, 88.05% ± 14.93%, and 80.38% ± 17.40%. When comparing with the static phantom, no significant statistic difference was observed for smaller pitch angles (p = 0.1 for 3%/1 mm), whereas a larger statistic difference was observed for larger pitch angles (p < 0.02 for all criteria). All the gamma passing rates were improved, if applying shift and rotation correction.

CONCLUSIONS

The significance of this work is that it is the first study to benchmark PSQA for the CyberKnife Synchrony system using realistically moving phantoms with rotation. With reasonable delivery time, we found it may be feasible to perform PSQA for Synchrony patients with a realistic breathing pattern.

BraNet: a mobil application for breast image classification based on deep learning algorithms

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client-server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader's agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts' accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model.

On the necessity of specialized knowledge-based models for SBRT prostate treatments plans

PURPOSE

Test whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments.

MATERIALS AND METHODS

A KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.25 Gy/5fx). The performance and robustness of this model were compared against a high-quality KBP-model (ST-model) that was already clinically adopted for hypo-fractionated (70 Gy/28fx and 60 Gy/20fx) prostate treatments. The two models were compared in terms of their predictions robustness, and the quality of their outcomes were evaluated against a set of reference clinical SBRT plans. Plan quality was assessed using DVH metrics, blinded clinical ranking, and a dedicated Plan Quality Metric algorithm.

RESULTS

The plan libraries of the two models were found to share a high degree of anatomical similarity. The overall quality (APQM%) of the plans obtained both with the ST- and SBRT-models was compatible with that of the original clinical plans, namely (93.7 ± 4.1)% and (91.6 ± 3.9)% vs (92.8.9 ± 3.6)%. Plans obtained with the ST-model showed significantly higher target coverage (PTV V95%): (97.9 ± 0.8)% vs (97.1 ± 0.9)% (p < 0.05). Conversely, plans optimized following the SBRT-model showed a small but not-clinically relevant increase in OAR sparing. ST-model generally provided more reliable predictions than SBRT-model. Two radiation oncologists judged as equivalent the plans based on the KBP prediction, which was also judged better that reference clinical plans.

CONCLUSION

A KBP model trained on moderately fractionated prostate treatment plans provided optimal SBRT prostate plans, with similar or larger plan quality than an embryonic SBRT-model based on a limited number of cases.

Lateral response artifact correction method using image stitching technique in radiochromic film dosimetry

PURPOSE

Lateral response artifact (LRA) is caused by the interaction between film and flatbed scanner in the direction perpendicular to the scanning direction. This can significantly affect the accuracy of patient-specific quality assurance (QA) in cases involving large irradiation fields. We hypothesized that by utilizing the central area of the flatbed scanner, where the magnitude of LRA is relatively small, the LRA could be mitigated effectively. This study proposes a practical solution using the image-stitching technique to correct LRA for patient-specific QA involving large irradiation fields.

METHODS

Gafchromic™ EBT4 film and Epson Expression ES-G11000 flatbed scanner were used in this study. The image-stitching algorithm requires a spot between adjacent images to combine them. The film was scanned at three locations on a flatbed scanner, and these images were combined using the image-stitching technique. The combined film dose was then calculated and compared with the treatment planning system (TPS)-calculated dose using gamma analysis (3%/2 mm). Our proposed LRA correction was applied to several films exposed to 18 × 18 cm2 open fields at doses of 200, 400, and 600 cGy, as well as to four clinical Volumetric Modulated Arc Therapy (VMAT) treatment plans involving large fields.

RESULTS

For doses of 200, 400, and 600 cGy, the gamma analysis values with and without LRA corrections were 95.7% versus 67.8%, 95.5% versus 66.2%, and 91.8% versus 35.9%, respectively. For the clinical VMAT treatment plan, the average pass rate ± standard deviation in gamma analysis was 94.1% ± 0.4% with LRA corrections and 72.5% ± 1.5% without LRA corrections.

CONCLUSIONS

The effectiveness of our proposed LRA correction using the image-stitching technique was demonstrated to significantly improve the accuracy of patient-specific QA for VMAT treatment plans involving large irradiation fields.

MR-guided stereotactic radiation therapy for head and neck cancers

Purpose

MR-guided radiotherapy (MRgRT) has the advantage of utilizing high soft tissue contrast imaging to track daily changes in target and critical organs throughout the entire radiation treatment course. Head and neck (HN) stereotactic body radiation therapy (SBRT) has been increasingly used to treat localized lesions within a shorter timeframe. The purpose of this study is to examine the dosimetric difference between the step-and-shot intensity modulated radiation therapy (IMRT) plans on Elekta Unity and our clinical volumetric modulated arc therapy (VMAT) plans on Varian TrueBeam for HN SBRT.

Method

Fourteen patients treated on TrueBeam sTx with VMAT treatment plans were re-planned in the Monaco treatment planning system for Elekta Unity MR-Linac (MRL). The plan qualities, including target coverage, conformity, homogeneity, nearby critical organ doses, gradient index and low dose bath volume, were compared between VMAT and Monaco IMRT plans. Additionally, we evaluated the Unity adaptive plans of adapt-to-position (ATP) and adapt-to-shape (ATS) workflows using simulated setup errors for five patients and assessed the outcomes of our treated patients.

Results

Monaco IMRT plans achieved comparable results to VMAT plans in terms of target coverage, uniformity and homogeneity, with slightly higher target maximum and mean doses. The critical organ doses in Monaco IMRT plans all met clinical goals; however, the mean doses and low dose bath volumes were higher than in VMAT plans. The adaptive plans demonstrated that the ATP workflow may result in degraded target coverage and OAR doses for HN SBRT, while the ATS workflow can maintain the plan quality.

Conclusion

The use of Monaco treatment planning and online adaptation can achieve dosimetric results comparable to VMAT plans, with the additional benefits of real-time tracking of target volume and nearby critical structures. This offers the potential to treat aggressive and variable tumors in HN SBRT and improve local control and treatment toxicity.

Robust Optimization for Prostate Radiation Therapy: Assessment of Delivered Dose by Incorporating Intrafraction Prostate Position Deviations

Purpose

To assess the robustness of the dose delivered to the clinical target volume (CTV) between planning target volume (PTV)-based and robust optimization planning approaches in localized prostate cancer radiation therapy.

Methods and Materials

Retrospective data of 20 patients with prostate cancer, including radiation therapy and real-time prostate position, were analyzed. Two sets of volumetric modulated arc therapy plans were generated per patient: PTV-based and robust optimization. PTV-based planning used a 7-mm CTV-PTV margin, whereas robust planning considered same-magnitude position deviations. Differences in CTV dose delivered to 99% volume (D99), PTV dose delivered to 95% volume (D95), and bladder and rectum V40 (volume receiving 40 Gy) and V60 (volume receiving 60 Gy) values were evaluated. The target position, determined by in-house position monitoring system, was incorporated for dose assessment with and without position deviation correction.

Results

In the robust optimization approach, compared with PTV-based planning, the mean (standard deviation) V40 and V60 values of the bladder were reduced by 5.2% (4.1%) and 5.1% (1.9%), respectively. Similarly, for the rectum, the reductions were 0.8% (0.5%) and 0.6% (0.6%). In corrected treatment scenarios, both planning approaches resulted in a mean (standard deviation) CTV D99 difference of 0.1 Gy (0.1 Gy). In the not corrected scenario, PTV-based planning reduced CTV D99 by 0.1 Gy (0.5 Gy), whereas robust planning reduced it by 0.2 Gy (0.6 Gy). There was no statistically significant difference observed in the planned and delivered rectum and bladder dose for both corrected and not corrected scenarios.

Conclusions

Robust optimization resulted in lower V40 and V60 values for the bladder compared with PTV-based planning. However, no difference in CTV dose accuracy was found between the 2 approaches.

Clinical and surgical effectiveness of the multi-disciplinary standardized management model in the treatment of retroperitoneal liposarcoma: Evidence-based clinical practice experience from Fudan Zhongshan

BACKGROUND

To assess the correlation between clinical outcomes and diagnostic accuracy of evaluations carried out by a preoperative multidisciplinary team versus standard surgical care for patients with retroperitoneal liposarcoma undergoing surgery.

METHODS

This comparative study was conducted retrospectively at a specialist assessment center within Zhongshan Hospital, Fudan University, China, between April 2011 and March 2021. Patients were assigned to a multidisciplinary team or nonmultidisciplinary team cohort based on referral to the multidisciplinary team. The primary outcome measured was long-term clinical prognosis, with other outcomes including diagnostic accuracy, 30-day reoperation, duration of stay, perioperative mortality, and medical complications. To mitigate selection bias, we conducted propensity-score matching. Uni- and multivariable Cox proportional hazard models were then used to evaluate the effect of multidisciplinary teams on postoperative survival. The previously specified questionnaire was used to measure the enhancement of awareness and treatment adherence facilitated by multidisciplinary team management. Data analysis was carried out between January 2023 and August 2023.

RESULTS

Of the 521 records that were screened, 139 patients were deemed eligible for inclusion and defined as the multidisciplinary team cohort. At the same time, 382 patients without multidisciplinary team management were also included during that period and defined as the nonmultidisciplinary team cohort. The multidisciplinary team cohort exhibited lower numbers of primary retroperitoneal liposarcoma but a higher tumor grade and a greater proportion of R2 resection. After propensity-score matching, the 1-, 3-, and 5-year overall survival rates were 89.5%, 70.5%, and 62.9%, respectively, in the multidisciplinary team cohort, and 77.1%, 49.8%, and 45.1% in the nonmultidisciplinary team cohort. The diagnostic consistency of the multidisciplinary team group was significantly superior to that of the nonmultidisciplinary cohort (92.5% vs 83.6%, P = .042). Although no significant links were shown with duration of stay (P = .232) and 30-day reoperation (P = .447), the multidisciplinary team participation was linked to a substantial decrease in perioperative mortality (P = .036) and postoperative complications (P = .002). Additionally, the multidisciplinary team group indicated stronger illness awareness and postoperative adherence among individuals with retroperitoneal liposarcoma.

CONCLUSION

The study's findings indicate that multidisciplinary team management could result in improved clinical outcomes, higher diagnostic accuracy, and reduced duration of postoperative stays, complications, and perioperative mortality. The intervention may also enhance disease awareness and postoperative compliance in retroperitoneal liposarcoma patients who undergo surgery. However, evidence quality was deemed low, and prospective studies with robust designs are required. Nonetheless, these results are worth considering.

Prediction of severe radiation-induced oral mucositis in locally advanced nasopharyngeal carcinoma using the combined systemic immune-inflammatory index and prognostic nutritional index

OBJECTIVE

Severe radiation-induced oral mucositis (sRIOM) can seriously affect patients' quality of life and treatment compliance. This study was to investigate the utility of the systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) in predicting sRIOM in patients with locally advanced nasopharyngeal carcinoma (LANPC).

METHODS

295 patients with LANPC were retrospectively screened. The pre-radiotherapy SII and PNI were calculated based on peripheral blood samples. A receiver operating characteristic (ROC) curve was used to determine the cut-off value. Logistic regression was used for univariate and multivariate analyses. Patients were classified into three groups based on the SII-PNI score: score of 2, high SII (> cut-off value) and low PNI (≤ cut-off value); score of 1, either high SII or low PNI; score of 0, neither high SII nor low PNI.

RESULTS

The SII-PNI demonstrated significant predictive ability for sRIOM occurrence, as evidenced by an area under the curve (AUC) of 0.738. The incidence rates of sRIOM with SII-PNI score of 2, 1, and 0 were 73.86%, 44.35%, and 18.07%, respectively. Multivariate analysis confirmed that the SII-PNI score was an independent risk factor for sRIOM.

CONCLUSION

The SII-PNI score is a reliable and convenient indicator for predicting sRIOM in patients with LANPC.

Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography

Characterizing left ventricular deformation and strain using 3D+time echocardiography provides useful insights into cardiac function and can be used to detect and localize myocardial injury. To achieve this, it is imperative to obtain accurate motion estimates of the left ventricle. In many strain analysis pipelines, this step is often accompanied by a separate segmentation step; however, recent works have shown both tasks to be highly related and can be complementary when optimized jointly. In this work, we present a multi-task learning network that can simultaneously segment the left ventricle and track its motion between multiple time frames. Two task-specific networks are trained using a composite loss function. Cross-stitch units combine the activations of these networks by learning shared representations between the tasks at different levels. We also propose a novel shape-consistency unit that encourages motion propagated segmentations to match directly predicted segmentations. Using a combined synthetic and in-vivo 3D echocardiography dataset, we demonstrate that our proposed model can achieve excellent estimates of left ventricular motion displacement and myocardial segmentation. Additionally, we observe strong correlation of our image-based strain measurements with crystal-based strain measurements as well as good correspondence with SPECT perfusion mappings. Finally, we demonstrate the clinical utility of the segmentation masks in estimating ejection fraction and sphericity indices that correspond well with benchmark measurements.

Dual-Energy Computed Tomography in the Evaluation and Management of Subarachnoid Hemorrhage, Intracranial Hemorrhage, and Acute Ischemic Stroke

Dual-energy computed tomography (DECT) has emerged as a valuable imaging modality in the diagnosis and management of various cerebrovascular pathologies, including subarachnoid hemorrhage, intracranial hemorrhage, and acute ischemic stroke. This article reviews the principles of DECT and its applications in the evaluation and management of these conditions. The authors discuss the advantages of DECT over conventional computed tomography, as well as its limitations, and provide an overview of current research and future directions in the field.

Quantitative and Visual Analysis of Data Augmentation and Hyperparameter Optimization in Deep Learning-Based Segmentation of Low-Grade Glioma Tumors Using Grad-CAM

This study executes a quantitative and visual investigation on the effectiveness of data augmentation and hyperparameter optimization on the accuracy of deep learning-based segmentation of LGG tumors. The study employed the MobileNetV2 and ResNet backbones with atrous convolution in DeepLabV3+ structure. The Grad-CAM tool was also used to interpret the effect of augmentation and network optimization on segmentation performance. A wide investigation was performed to optimize the network hyperparameters. In addition, the study examined 35 different models to evaluate different data augmentation techniques. The results of the study indicated that incorporating data augmentation techniques and optimization can improve the performance of segmenting brain LGG tumors up to 10%. Our extensive investigation of the data augmentation techniques indicated that enlargement of data from 90° and 225° rotated data,up to down and left to right flipping are the most effective techniques. MobilenetV2 as the backbone,"Focal Loss" as the loss function and "Adam" as the optimizer showed the superior results. The optimal model (DLG-Net) achieved an overall accuracy of 96.1% with a loss value of 0.006. Specifically, the segmentation performance for Whole Tumor (WT), Tumor Core (TC), and Enhanced Tumor (ET) reached a Dice Similarity Coefficient (DSC) of 89.4%, 70.1%, and 49.9%, respectively. Simultaneous visual and quantitative assessment of data augmentation and network optimization can lead to an optimal model with a reasonable performance in segmenting the LGG tumors.