搜索结果：Physical and engineering sciences in medicine

Dementia is a progressive neurodegenerative disorder that severely impacts cognitive functions and daily living, especially in aging populations. Among its subtypes, Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit overlapping clinical symptoms, making early and accurate differentiation a critical challenge. Electroencephalography (EEG), as a non-invasive and cost-effective modality, provides valuable insights into the neurophysiological disruptions associated with these conditions. This study aims to develop a robust EEG-based diagnostic framework capable of accurately classifying AD, FTD, and healthy controls (HC) by integrating domain-specific signal processing with advanced deep learning techniques. This study employed a publicly accessible dataset consisting of resting-state EEG recordings from a total of 88 participants, comprising 29 individuals with AD, 23 diagnosed with FTD, and 36 age-matched HC. The proposed model integrates Common Spatial Pattern (CSP) filtering with a sequential modified hybrid architecture that combines Convolutional Neural Networks (CNNs) and a Vision Transformer (ViT). By fusing domain-informed spatial filtering with deep hierarchical feature learning, the model captures both local signal characteristics and global contextual dependencies. A 10-fold cross-validation approach was employed to assess model performance and generalizability. The proposed model achieved notable classification accuracies of 95.86%, 94.76%, 94%, and 92.14% for the AD/HC, FTD/HC, AD/FTD, and AD/FTD/HC classification tasks, respectively. These results underscore the diagnostic potential of EEG-based deep learning frameworks in distinguishing among neurodegenerative conditions and highlight their promise in supporting more precise and individualized clinical interventions. This study presents a novel end-to-end EEG classification pipeline that fuses domain-guided spatial filtering with deep neural feature learning. The promising results suggest that the proposed method could serve as a valuable component in future clinical decision support systems for dementia, contingent upon further validation in real-world clinical settings.

To evaluate the dosimetric impact of depth-dependent ion recombination and empirical effective point of measurement (EPOM) positioning in megavoltage photon beams, with particular focus on flattening filter-free (FFF) beams. Ion recombination correction factors ([Formula: see text]) were characterised as a function of depth and field size for three ionisation chambers (Roos, SNC125, CC13) using the two-voltage method under reference conditions (SSD 100 cm and 10 cm field size) and for additional MLC-defined 5 cm and 2 cm square field sizes, on a point-by-point basis across multiple beam energies. Empirical EPOMs were derived by aligning percentage depth ionisation (PDI) curves to a reference plane-parallel chamber. The dosimetric consequences of using generic [Formula: see text] and EPOM assumptions were assessed, and scan-derived [Formula: see text] values were validated against point dose measurements. A marked depth dependence in [Formula: see text] was observed for all chambers, most notably in FFF beams. The CC13 exhibited the greatest depth-related variation, resulting in recombination-related PDD deviations of up to 1.3% at extended depths. Empirically determined EPOMs were consistently smaller than the conventional 0.6[Formula: see text] shift, with normalised values of 0.42 and 0.38 for SNC125 and CC13, respectively. Using the conventional shift would introduce a residual dose deviation of approximately - 0.5%. The combined influence of uncorrected ion recombination and the generic EPOM produced a PDD bias of 0.8% at 10 cm depth for the 10 FFF beam, which is relevant both for reference dosimetry and for depth-sensitive treatment sites. This study demonstrates that empirical, chamber-specific EPOM and [Formula: see text] correction factors improve dosimetric accuracy and PDD measurements, and consequently, reference dosimetry and TPS beam modelling for which PDD10 cm is a key parameter. With the increasing adoption of FFF beams, reliance on generic assumptions for [Formula: see text] and EPOM introduces clinically relevant systematic deviations, approaching 1.0% at the calibration depth and becoming larger at greater depths. These corrections should be considered an essential component of linac and chamber commissioning to ensure robust reference dosimetry and accurate beam modelling. Given their measurable impact, such practices warrant inclusion in ACPSEM guidelines, in alignment with emerging best-practice frameworks and the evolving precision requirements of modern radiotherapy.

Cardiovascular diseases (CVDs) are still the leading cause of death worldwide, emphasizing the critical need for reliable diagnostic systems. This study aims to create a standardized electrocardiogram (ECG) dataset that can be used to detect and classify six major CVDs using machine learning techniques and investigate feature selection and extraction methods for improved performance. A large dataset of 34,580 12-lead ECG recordings was collected from Sher-i-Kashmir Institute of Medical Sciences (SKIMS), Srinagar, Jammu and Kashmir spanning six clinically confirmed classes: Normal, Cardiac Arrhythmia, Coronary Heart Disease, Cardiomyopathy, Stroke, and Heart Failure. Data pre-processing involved baseline correction, removal of artifacts and the extraction of 14 clinically informative features. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, resulting in an equal distribution of 16.7% of the data across each class. Ten Machine learning and deep learning models-Logistic Regression, Decision Tree, Random Forest, SVM, KNN, Naive Bayes, Gradient Boosting, MLP, DNN, and RNN-were trained and tested. SHAP and LIME methods were used for interpretability. On the raw dataset, Random Forest and Gradient Boosting produced highest performance with test accuracy of 99.88%, precision of 99.88%, recall of 99.88%, and F1-score of 99.88%. After SMOTE, DNN significantly improved (Accuracy: 97.62%, Precision: 97.66%, Recall: 97.62%, F1-score: 97.64%), while MLP obtained an F1-score of 98.49% and RNN obtained 94.76%. All models exhibited better generalization and stability after SMOTE. The balanced, heterogeneous, and clinically verified ECG dataset supported the highly accurate, interpretable, and real-time classification of CVD. SMOTE significantly improved the performance of the model, particularly for deep networks, substantiating its effectiveness in the class imbalance problem. These results place the proposed model and dataset as effective tools for clinical decision support in the diagnosis of cardiovascular disease.

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by pain, swelling, stiffness, and loss of joint function, making early diagnosis challenging. The study aims to assess the differences between RA patients (n = 70) and healthy individuals (n = 30) while classifying Ritchie Articular Index (RAI) values (0-3) based on inflammation levels using artificial intelligence algorithms. Metacarpophalangeal (MCP), and proximal-interphalangeal (PIP) joints were analyzed for the degree of inflammation. Static thermal data was collected from individuals at rest in a controlled environment. Then, alcohol was applied to the participants' hand regions, followed by a 180-second thermal video recording of the same region. In the pre-processing step, background noise cleaning and alignment were performed. Background was eliminated using Snake algorithm. Thermal video recordings were aligned using Scale Invariant Feature Transform (SIFT) algorithm. The Skeletonization algorithm was employed to detect fingers and joint regions in the images. For static thermal analysis, initial temperature ([Formula: see text]) values were extracted from the resting thermogram data. In dynamic thermal analysis, the temperature parameters [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were calculated. A statistical analysis of the four temperature parameters across different RAI values revealed that [Formula: see text] (p = 0.025) and [Formula: see text] (p = 0.042) exhibited statistically significant differences among the four RAI levels. Machine learning models were trained using the resting temperature values of patient and healthy groups, and the SVM achieved the highest success rate of 93%. It is believed that the proposed system may help diagnose RA in clinical settings and contribute to determining the severity of inflammation.

Sudden Unexpected Death in Epilepsy (SUDEP) is a major cause of epilepsy-related mortality, yet discussions about SUDEP in clinical settings remain inconsistent. This study aimed to assess the perspectives, practices, and barriers related to SUDEP counselling for epilepsy professionals in Spain. A cross-sectional online survey of 17 Likert style questions was disseminated via the Spanish Epilepsy Society between September 2023 and February 2024 to epilepsy professionals in Spain using a non-discriminatory exponential snowballing technique leading to non-probability sampling. The survey was a validated instrument previously employed in similar international studies. Questions revolved around SUDEP communication and counselling. Descriptive and comparative analyses were conducted. 54 professionals responded, with the majority being adult neurologists-epileptologists. While most respondents acknowledged the importance of SUDEP counselling, none reported discussing it with all patients. SUDEP was typically discussed in response to risk changes (85 %) or patient enquiry (47 %). Only 9 % used structured communication tools, and 28 % had access to bereavement support services. Perceived low clinical risk (74 %), concern about patient distress (62 %), and limited consultation time (57 %) were the most common barriers. Comparative analysis revealed no statistically significant differences between adult neurologists and paediatric neurologists, though paediatricians reported more negative counselling experiences. Despite strong recognition of its importance, SUDEP communication in Spain is infrequent and inconsistent. Key barriers include clinical judgment, time constraints, and limited resources. The findings underscore the need for national guideline, structured tools, and targeted training to support routine SUDEP counselling in Spanish clinical practice.

The demand for bedside radiography is increasing due to critical clinical needs, including infection control and the limited mobility of severely ill patients. However, radiation dose adjustment in these settings remains heavily reliant on the expertise and experience of radiographers. To address this issue, a novel flat panel detector (FPD) integrated with an automatic exposure control (AEC) system has been developed. This study aims to experimentally evaluate the fundamental performance of this system and clarify its clinical utility, including its potential limitations. The dependency of the AEC performance on object thickness and tube voltage was investigated using acrylic phantoms. To simulate clinical scenarios, the AEC response was examined using a chest phantom. Additionally, the effects of source-to-image distance and oblique X-ray incidence on the AEC performance were also evaluated using a quality-control test device. Our results elucidated the behavior of the exposure index (EI) and image quality under varying tube voltage and object thickness. In clinical conditions, the introduction of the AEC system significantly reduced EI, confirming its potential for effective dose management. Multiple factors were identified that influence both the AEC response and image quality, such as sensor positioning, imaging distance, and beam angle. These findings demonstrate that the AEC-equipped FPD system maintains consistent image quality while effectively reducing the radiation dose under various simulated imaging conditions. Our results also underscore the importance of accounting for environmental factors that affect dose control and image characteristics, highlighting the need for practical adjustment in routine clinical operation.

A treatment planning system (TPS) is responsible for calculating the radiation dose for patients undergoing brachytherapy. However, to verify TPS dose accuracy of intracavitary brachytherapy, which feature particularly steep and complex dose gradients, 3D-printed phantoms made of polylactic acid (PLA) can be used. A study was designed to create an in-house phantom for verification of gynecological brachytherapy measurement using a radiophotoluminescent glass dosimeters (RPLGDs) and to evaluate the dosimetric differences between measurement and calculation by the treatment planning system under clinical conditions.An in-house phantom holder was designed to move the axis of the holder to the rectum point that differs according to the patient's anatomy. The holder of the applicator was designed for various types of applicators in intracavitary brachytherapy. This clinical study was used to quantify variations between the calculated and measured dose for 6 plans at various points in the phantom, which included point A, point B, the bladder point, and the rectum points.The RPLGDs demonstrated a linear dose response up to 10 Gy, excellent angular dependence, and an associated uncertainty of 3.3% (k  =  1). In the clinical case, the dose differences between the measured and calculated values at Point A, Point B, bladder, and rectum were +1.99  ±  1.11%, 1.01  ±  0.02 Gy, and 0.10 Gy, +4.42 ± 2.56%. and + 3.53  ± 1.44%, respectively.Dosimetry with RPLGDs using the 3D printed in-house phantom can accurately verify delivered dose in intracavitary brachytherapy for quality assurance purposes.

Breast cancer is a significant global health issue, demanding early identification to provide appropriate therapy and satisfactory survival results. This work used two independent imbalanced datasets (EIS-BT and WBCD). CNN1D with Long Short-Term Memory (LSTM) was integrated to acquire features from these datasets to identify breast cancer. Three scenarios for breast cancer detection were investigated based on CNN1D-LSTM derived characteristics from Dataset-1, Dataset-2, and their combination. The Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the collected features in all three scenarios. The suggested CNN1D-LSTM-SMOTE approach, in conjunction with Support Vector Classification (SVC), yields impressive results with a Matthews Correlation Coefficient (MCC) of 97.2 % on Dataset-1 and 100.0% on Dataset-2. Random Forest Classifiers (RFC) perform better, achieving an MCC of 98.4% on the combined features. The K-fold approach was used, yielding average MCCs of 91.7%, 74.1%, and 96.9% on Dataset-1, Dataset-2, and the combined features, respectively. Statistical analysis revealed a p-value of 0.01, signifying the significance of the findings, and a standard error of 0.006 for the combined features. Bootstrapping was employed to calculate confidence intervals, resulting in Lower Confidence Intervals (LCI) of 95.6% and Higher Confidence Intervals (HCI) of 97.9% for the combined features. These findings highlight the model's potential clinical application in supporting oncologists with early, real-time, reliable, and automated breast cancer diagnosis, leading to improved diagnostic procedures and patient outcomes.

Radiotherapy dosimetry in composite and modulated fields remains complex, especially when using small field ionization chambers in the second part of the Alfonso et al. formalism. This study investigates the response of the IBA- CC 01 ionization chamber in machine-specific reference, one static field of, and clinical IMRT step-and-shoot composite fields for a 6 MV flattening-filter-free (FFF) TrueBeam STx ® photon beam. A previously validated BEAMnrc model of the TrueBeam linac was used to generate high-statistics phase-space files, which were then combined with an egs_chamber model of the IBA-CC01 to calculate absorbed dose to water and detector response in static and composite fields. Latent variance was evaluated at three fixed points (central, off-axis, and peripheral) across several IMRT step-and-shoot fields, showing that the detector's latent variance remains below 0.2% and is largely independent of the detector's position. Radiochromic film measurements using Gafchromic EBT4 in a solid water phantom, following AAPM TG-235, validated the Monte Carlo simulation of the plan-class-specific reference fields. For these, off-axis ratio profiles from film and Monte Carlo agree within a few percent in the high-dose region, and a gamma analysis with 3.5%/2.5 mm criteria (global) yielded passing rates of 97% and 95% for cross-planes and in-plane profiles, respectively. Monte Carlo-derived correction factors for the IBA-CC01 in IMRT step-and-shoot composite fields are close to one and, within a mean absolute difference of less than 1.5%, align with the small field correction factors reported in IAEA-AAPM TRS 483 for static fields of similar size. These findings suggest that, for the 6 MV FFF TrueBeam beam and the IMRT step-and-shoot fields examined here, the IBA-CC01 functions effectively as a practical field detector for relative dosimetry and for calculating detector-specific correction factors in composite fields. In contrast, absolute reference dosimetry should still rely on reference-class ionization chambers under conventional reference conditions.

Children are more radiosensitive than adults, making dose optimisation in paediatric computed tomography (CT) essential. Although Diagnostic Reference Levels (DRLs) are internationally recommended as optimisation tools, national DRLs for paediatric CT in Jordan remain limited and outdated. This study aimed to establish national DRLs for six paediatric CT protocols, evaluate inter-hospital and age-related dose variations, and compare results with international benchmarks. A retrospective multicentre study was conducted across six Jordanian hospitals between December 2023 and September 2025, including 3794 paediatric patients stratified into four age groups (<&#x2009;1, 1-4, 5-10, and 11-18&#xa0;years). Volumetric CT dose index (CTDIvol) and dose-length product (DLP) data were collected for six protocols: brain, chest, abdomen-pelvis, chest-abdomen-pelvis, sinuses, and contrast-enhanced neck soft tissue. DRLs were defined as the 75th percentile of institutional median CTDIvol and DLP values. Inter-hospital and age group variations were analysed, and univariable regression analyses assessed acquisition parameters associated with dose variation. Dose increased with patient age for trunk protocols, whereas brain, sinuses, and neck CT showed comparatively stable patterns. Substantial inter-hospital variability was observed across protocols, with institutional median CTDIvol differing markedly between centres, particularly for trunk examinations in younger age groups. In univariable regression analyses, all four acquisition parameters (kVp, mAs, pitch, and slice thickness) were significantly associated with CTDIvol and DLP (p&#x2009;&#x2264;&#x2009;0.001), with kVp demonstrating the strongest association (R2&#x2009;=&#x2009;0.603 for CTDIvol; R2&#x2009;=&#x2009;0.630 for DLP). Compared with published international DRLs, Jordanian brain CT dose metrics were higher in multiple age groups; chest comparisons should be interpreted cautiously where international benchmarks are weight-banded. This study established national paediatric CT DRLs for Jordan, highlighting the need for standardised protocols and periodic DRL review to enhance radiation protection.

To evaluate a proof-of-concept three-dimensional surface reconstruction technique using a hybrid LiDAR and RGB sensor system with an open-source, GPU-accelerated pipeline. The goal is to generate photorealistic digital twins of phantom surfaces for integration into radiotherapy collision avoidance workflows. A portable Intel RealSense sensor was used to acquire synchronized depth and color images. Sensor performance, including depth accuracy, fill rate, and planar root mean square error, was evaluated to determine practical scan range. A reconstruction pipeline was implemented using the Open3D library with a voxel-based framework, signed distance function integration, ray casting, and color and depth-based simultaneous localization and mapping for pose tracking. Surface meshes were generated using the Marching Cubes algorithm. Validation involved scanning rectangular box phantoms and an anthropomorphic Rando phantom in a single circular motion. Reconstructed models were registered to CT-derived meshes using manual point picking and iterative closest point alignment. Accuracy was assessed using cloud-to-mesh distance metrics and compared to Poisson surface reconstruction. Highest accuracy was observed within the 0.3 to 2.0&#xa0;m range. Dimensional differences for box models were within five millimeters. The Rando phantom showed a registration error of 1.8&#xa0;mm and 100% theoretical overlap with the CT reference. Global mean signed distance was minus 0.32&#xa0;mm with a standard deviation of 3.85&#xa0;mm. This technique has strong potential to enables accurate, realistic surface modeling using low-cost, open-source tools and supports future integration into radiotherapy digital twin systems.

In medical imaging, particularly in enhancing computed tomography (CT) scan images, improving image quality while preserving diagnostic content is critical for detecting different types of abnormalities, especially in cases such as tumors, inflammatory conditions, or vascular issues. This paper proposes a novel image enhancement pipeline that integrates several image enhancement techniques into a sequential workflow that is specifically designed for abdominal CT scan images. The proposed pipeline combines windowing, contrast-limited adaptive histogram equalization, denoising via non-local means, and unsharp masking to concurrently address several issues affecting the quality of the images. Unlike existing methods, the proposed combinational approach improves contrast, suppresses noise, and sharpens structural detail, guaranteeing the balance between the enhancement and the diagnostic integrity. The workflow was evaluated on datasets from The Cancer Imaging Archive and the Medical Segmentation Decathlon. The proposed approach is assessed using key image quality metrics, yielding an average Peak Signal-to-Noise Ratio of 31.79 dB, Universal Image Quality Index of 0.96, Feature Similarity Index of 0.93, Absolute Mean Brightness Error of 7.12, and Edge Content of 7.78. These results indicate significant improvements in contrast enhancement, noise reduction, and the preservation of structural details. We performed an additional qualitative analysis by generating histograms and saliency maps that further confirm the method's effectiveness in enhancing the diagnostic quality of the CT images for both clinical and research purposes.

This study evaluated whether half-acquisition (180&#xb0; scan) pediatric cone-beam computed tomography (CBCT; 3D Accuitomo F17, J. Morita, Kyoto, Japan) reduces radiation exposure while maintaining sufficient diagnostic image quality for identifying ectopic eruptions and impacted teeth. Additionally, it was investigated whether a low-noise reconstruction filter (G_101) mitigates image quality degradation in 180&#xb0; scans. Three board-certified oral and maxillofacial radiologists certified by the Japanese Society for Oral and Maxillofacial Radiology visually evaluated clinical images from 12 pediatric patients (aged 6-10&#xa0;years). The image quality was objectively assessed using phantom-based analyses of the modulation transfer function (MTF), noise power spectrum (NPS), and comprehensive objective image quality calculated from MTF and NPS values. Although 180&#xb0; images showed increased noise and slightly lower visual assessment scores compared with 360&#xb0; images, they remained diagnostically acceptable. In 180&#xb0; reconstructions, the median visual scores with the G_101 filter were slightly higher than those with the standard G_001 filter, with small differences (within approximately 0-3 points on a 100-point scale), although the differences were not statistically significant. Interestingly, in approximately 28% of 180 evaluations, 180&#xb0; images scored higher than 360&#xb0; images, likely due to reduced motion artefacts from shorter acquisition. In a previous phantom experiment, the dose area product (DAP) for 360&#xb0; and 180&#xb0; scans was 490&#xa0;mGy&#xa0;cm2 and 249&#xa0;mGy&#xa0;cm2, respectively, indicating that 180&#xb0; scan reduces radiation exposure while maintaining clinically acceptable image quality. These findings suggest that half-acquisition, when combined with an appropriate reconstruction filter, may offer a practical, low-dose alternative for pediatric dental imaging.

In the digital era, the medical industry creates a large amount of information related to patients. Manual processing&#xa0;of this produced information by a physician is very difficult. Therefore, the Internet of Things (IoT)-enabled heart disease detection is currently gaining high attention from various technical fields, particularly for personalized medical care. Still, in several cases,&#xa0;efficient detection of heart disease and&#xa0;24-h consultation with an expert is not possible because of various reasons. Additionally, there are a lot of heart-related deaths, and the death count is rising every day. Prediction and detection of heart disease&#xa0;need&#xa0;high&#xa0;perfection&#xa0;and&#xa0;precision since a minor error could result in a serious condition or the death of an individual. So, an IoT-based network is developed in this paper to tackle these issues. The introduced approach is implemented in two phases. At the beginning phase, the required signal is collected and it is converted into spectrogram images with the help of a Short-Time Fourier Transform. In the second phase, sensor data are collected using IoT devices. This collected sensor data and the spectrogram images are given to the Hybrid and Multi-dilated Convolution based Adaptive Residual Attention Network (HMDCARAN) for predicting heart disease. The suggested HMDCARAN's parameters are tuned by the Modified Crayfish Optimization Algorithm. The outcome of the implemented network is compared with the traditional approaches to verify its effectiveness. Here, the developed framework achieved an accuracy of 96.52%, precision of 98.29%, and sensitivity of 97.18, which is enhanced than the other frameworks. Thus, the outcome proved that the designed network can identify heart disease in the initial stage and overcome the risk factors caused at the advanced stages of the heart disorder.

This study assessed a high-resolution ionisation chamber-based PTW 1600SRS detector array (array) for beam profile analysis and patient-specific quality assurance (PSQA) in CyberKnife (CK) stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT). The goal was to determine its suitability for small fields and non-isocentric delivery, which is unique to robotic platforms. Detector performance was examined for dose linearity, reproducibility, beam profiles, output factors, dose-rate dependence, and verification of the Iris collimator field size. Results were benchmarked against diode-based commissioning data. Clinical applicability was tested by retrospectively verifying 20 intracranial SRS and 20 extracranial SBRT plans using gamma analysis with criteria ranging from 3%/3&#xa0;mm to 1%/1&#xa0;mm, as well as 4%/1&#xa0;mm. The detector showed strong dose linearity (R2&#x2009;=&#x2009;0.999) and stable reproducibility. Beam profiles matched commissioning values within 0.5&#xa0;mm, and output factors agreed within 2% for most collimators, with a maximum deviation of 3% at 5&#xa0;mm. Dose-rate variation remained within 2.5% across relevant SADs. Iris collimator field sizes were consistent with reference measurements. Clinical validation achieved high passing rates, all with tolerance limit of >&#x2009;95%. It enables accurate beam characterization and reliable PSQA in CK treatments. This work provides the first combined evaluation of beam analysis and clinical validation for this detector on a robotic radiosurgery system, supporting its routine use in small-field quality assurance.

Radiation dosimetry is essential in the optimisation and justification of medical imaging procedures. However, the complexity of modern imaging equipment often surpasses the capabilities of standard dose calculation software, necessitating the use of commercially available dosimetry phantoms, which are often prohibitively expensive. This study aimed to develop a cost-effective, 3D-printed newborn-equivalent dosimetry phantom for measuring organ and whole-body effective doses. Several Polylactic Acid (PLA)-based filaments were investigated for tissue equivalency through Hounsfield-value analysis via micro-CT (40-70&#xa0;kVp) and clinical CT (70-140 kVp) measurements. Standard PLA at 93% (&#x3c1;&#x2009;=&#x2009;1.14&#xa0;g/cm3) and 26% (&#x3c1;&#x2009;=&#x2009;0.41&#xa0;g/cm3) infill density was selected for soft tissue and lung, respectively, while StoneFil composite PLA (FormFutura) at 81% (&#x3c1;&#x2009;=&#x2009;1.21&#xa0;g/cm3) infill was chosen for bone. The phantom was modelled on a modified Cristy and Eckerman newborn design, with 21 sections generated using MATLAB and printed on a Bambu Lab X1 Carbon 3D printer. A total of 186 thermoluminescent dosimeter (TLD) capsules were embedded in the phantom, and TLD measurements from whole-body 60 kVp radiographs were compared with Monte Carlo (PCXMC 2.0) simulations for validation. The phantom demonstrated accurate dosimetry for the radiographic exposure, with average organ doses closely matching the simulated exposure, and the effective dose (ICRP 103) within 2% of the simulation. The phantom required 135&#xa0;h to print, with a material cost of A$165. This study successfully developed and validated a cost-effective dosimetry phantom for paediatric radiography, with the potential to print larger phantoms for older children. Future work will explore the phantom's application in other X-ray imaging modalities.

To compare dose to the organ at risk (OAR) and target coverage of carbon-ion beam, protons, and photons for patients with head and neck cancer. Treatment plans for carbon-ion pencil beam scanning (C-PBS) (64&#xa0;Gy (RBE) in 16 fractions), proton pencil beam scanning (P-PBS), and volumetric modulated arc therapy (VMAT) (70&#xa0;Gy in 35 fractions for P-PBS and VMAT) were generated and compared using different dose constraints per treatment modality. Dose metrics (e.g. D95,V20) were analyzed. Statistical significance was assessed by the Wilcoxon signed-rank test. Also, we investigated howmany normal tissues were irradiated above the constraint after achieving the planning goals (pass rate) in the OARs. C-PBS outperformed P-PBS and VMAT in PTV coverage (p&#x2009;=&#x2009;0.01 for both); however, P-PBS and VMAT did not differ substantially from each another (p&#x2009;=&#x2009;0.35). C-PBS was superior in limiting the dose to the OAR. The pass rates for C-PBS, P-PBS, and VMAT were 94%, 81%, and 69%, respectively. C-PBS demonstrated superior performance compared to VMAT and P-PBS in terms of dose conformation to the target volume and normal tissue sparing, and achieved the highest pass rate in meeting dose constraints.

The role of adipose tissue in predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains unclear. This study proposes a method that integrates deep learning and machine learning techniques to investigate the role of adipose tissue in identifying MVI status in HCC patients. We collected enhanced Computed Tomography images from 517 HCC patients across two independent centers, dividing them into a training set, validation set, and test set. The model was constructed using adipose and tumor deep learning features along with clinical features, and the features were input into a classifier for prediction. The model performance was evaluated using the area under the curve(AUC), decision curve analysis, scatter plots, and box plots. Furthermore, we compared the model's performance with that of three radiologists. After incorporating the adipose tissue modality, the venous-phase AUC reached 0.866 (95% CI 0.803-0.920), while the arterial-phase AUC was 0.864 (95% CI 0.792-0.920). The inclusion of the adipose tissue modality provided significant value for clinical diagnosis, which was further validated through visualization analysis. Using predicted labels for grouping, it shows that the overall survival of the high-risk group was significantly lower than that of the low-risk group. Comparative analysis showed that the predictive performance of the model surpassed that of radiologists. Univariate analysis identified the adipose region as a risk factor for predicting MVI status. We developed a hybrid multimodal model that performed comparably to radiologists' assessments. The inclusion of the adipose tissue modality enhanced the accuracy of MVI diagnosis.

Oral cavity cancers are a debilitating form of head and neck cancer with high rates of mortality. Radiotherapy is one of the main forms of treatment but relies on minimizing doses that are delivered to organs at risk and considering any motion in the mouth. One solution is to use a block within the mouth which acts to reduce motion and decreases tissue heterogeneity. In this work, we developed a process for designing a customized tissue-equivalent 3D-printed tongue bite and evaluated its impact on the radiation treatment. Six patients with stage III or IV oral cancer were involved. Computed tomography (CT) images for each patient were acquired with currently used Styrofoam tongue bites within the mouth. The designs of 3D tongue bites were prepared using those CT images and then printed on an SLA printer using F80 resin which is a tissue-equivalent and biocompatible material. Secondary CT images were then acquired for each patient with the 3D tongue bites to have a dosimetric comparison. Volumetric-modulated arc therapy (VMAT) planning was carried out for individual patients on both CT images. Plan parameters, fractionation scheme and optimization priorities were all kept the same. The radiotherapy plans utilizing 3D tongue bites showed better PTV coverage and reduced Dmax (p&#x2009;=&#x2009;0.028). Doses to organs at risk (OARs) including brainstem, parotid glands and hard palate were also reduced (p&#x2009;<&#x2009;0.028) except for the spinal cord (p&#x2009;>&#x2009;0.05). The dose conformity and homogeneity were also improved (p&#x2009;=&#x2009;0.028 and p&#x2009;=&#x2009;0.044 respectively). All patients reported that the 3D tongue bites were soft, conformal to the oral cavity, comfortable and did not cause any gag reflex. We conclude that the 3D tongue bite is a useful utility that improves the treatment of patients with oral cancer.