Physical and Engineering Sciences in Medicine最新文献

The aim of this study is to assess the dosimetric advantages and clinical feasibility of the Half-Field Volumetric Modulated Arc Therapy technique in comparison to conventional Full-Field Arc Therapy and Intensity-Modulated Radiation Therapy for the treatment of prostate cancer. 120 Treatment plans were created for 24 prostate cancer patients using Half-Field, Full-Field, and Intensity Modulated static fields (5-, 7-, and 9-fields). The dosimetric parameters and the homogeneity index were evaluated for the different Planning Target Volumes included pelvic lymph nodes, seminal vesicles, and prostate. Additionally, the dose burden to organs at risk was assessed. The efficiency of the plans was analyzed based on monitor unit usage and the gamma index. Half-Field plans exhibited comparable target coverage to static fields while demonstrating superior homogeneity in comparison to Full-Field plans. This technique resulted in a significant reduction in bladder and rectum doses within the mid- and high-dose ranges, with a V30 for the bladder of 67.8% in Half-Field compared to 75.3% in Full-Field (p < 0.001). The Half-Field technique required a significantly fewer monitor units than the Intensitiy-Modulated technique (600.8 vs. 1172.7 for 5-field, p < 0.001) resulting in a notable reduction in treatment. Half-Field represents an effective combination of the dosimetric precision of static Intensity Modulated fields with the efficiency of Full-Field arc therapy, offering a promising alternative for prostate cancer treatment. The technique ensures reduced organ at risks doses, enhanced treatment homogeneity and lower complexity, making it a viable option for moderately hypofractionated radiotherapy protocols.

Ovarian cancer is often diagnosed at advanced stages, with high-grade serous ovarian cancer (HGSOC) accounting for 70-80% of fatalities. Current predictive tools, limited by single-time-point data, fail to capture subtle temporal changes indicative of relapse. To evaluate the performance of OvarXNet, a novel deep learning framework integrating longitudinal PET/CT imaging and clinical data for early prediction of ovarian cancer relapse. This retrospective study included 58 advanced-stage HGSOC patients (mean age, 56 ± 10.4 years) who underwent [¹⁸F]FDG PET/CT scans from April 2019 to January 2025. Patients with uncontrolled diabetes or recent cancers were excluded. Each patient had a median of three PET/CT scans and associated clinical data. The OvarXNet framework combines 3D convolutional neural networks (CNNs) for volumetric feature extraction and bidirectional gated recurrent units for temporal analysis. Statistical analyses included area under the receiver operating characteristic curve (AUC), precision-recall (PR) metrics, and calibration plots. Fifty-eight patients (mean age 56 ± 10.4 years) contributed 1914 image sets post-augmentation. OvarXNet achieved an AUC of 0.92, outperforming single-time-point CNN (AUC: 0.84) and LSTM-based models (AUC: 0.89). PR analysis confirmed superior model performance (PR-AUC: OvarXNet > 0.90 vs. single-time-point CNN: 0.82). Calibration plots demonstrated robust probability estimates. Attention mechanisms highlighted time points with elevated CA-125 or progression-related clinical notes, enhancing interpretability. OvarXNet significantly improves early relapse prediction in advanced-stage HGSOC by leveraging longitudinal imaging and clinical data. The framework's accuracy and interpretability support its potential for guiding personalized treatment strategies.

Cardiovascular diseases represent a leading cause of mortality worldwide, necessitating accurate and early diagnosis for improved patient outcomes. Current diagnostic approaches for cardiac abnormalities often present challenges in clinical settings due to their complexity, cost, or limited accessibility. This study develops two deep learning architectures that offer fast, accurate, and cost-effective methods for automatic diagnosis of cardiac diseases, focusing specifically on addressing the critical challenge of limited labeled datasets in medical contexts. We propose two methodologies: first, a Multi-Branch Deep Convolutional Neural Network (MBDCN) that emulates human auditory processing by utilizing diverse convolutional filter sizes and power spectrum input for enhanced feature extraction; second, a Long Short-Term Memory-Convolutional Neural (LSCN) model that integrates LSTM blocks with MBDCN to improve time-domain feature extraction. The synergistic integration of multiple parallel convolutional branches with LSTM units enables superior performance in heart sound analysis. Experimental validation demonstrates that LSCN achieves multiclass classification accuracy of 89.65% and binary classification accuracy of 93.93%, significantly outperforming state-of-the-art techniques and traditional feature extraction methods such as Mel Frequency Cepstral Coefficients (MFCC) and wavelet transforms. A comprehensive fivefold cross-validation confirms robustness of our approach across varying data partitions. These findings establish the efficacy of our proposed architectures for automated heart sound analysis, offering clinically viable and computationally efficient solutions for early detection of cardiovascular diseases in diverse healthcare environments.

This study aims to explore the prognostic value of regionally modulated radiomics for patients with head and neck cancer (HNC) in positron emission tomography/computed tomography (PET/CT) imaging. The dataset included 224 HNC patients who underwent PET/CT imaging at five different centers. The primary tumor was manually contoured by experienced radiologists. For introducing regionally modulated radiomics, we developed four fuzzy masks by applying Gaussian filter, and four peritumor-included masks by applying morphological operations. For each patient, a total of 326 radiomic features were extracted from each of nine masks. Multivariate Cox proportional hazards model with ensemble strategy was adopted to construct classical, fuzzy, and peritumoral based prognostic models, respectively, for predicting progression-free survival. ComBat harmonization was applied to adjust for multicenter variability. A consistent modelling approach was employed to ensure the independence and comparability of these models. The models were evaluated by C-index, log-rank test, and the area under the time-dependent ROC curve (tAUC). The fuzzy radiomics model applied with 5 mm FWHM of Gaussian filter demonstrated superior performance compared to classical radiomics model (Testing C-index, 0.735 vs. 0.685; log-rank test, p < 0.007 vs. p < 0.035). Peritumoral radiomics models showed slightly improved performance compared to classical radiomics model (Testing C-index, 0.727 vs. 0.685; log-rank test, p < 0.014 vs. p < 0.035). The tAUC demonstrated consistent findings with the C-index. The harmonization strategy showed further improved performance for both fuzzy and peritumoral models. These results showed that regionally modulated radiomics analysis was superior for estimating prognosis in this multicenter HNC cohort when compared to classical radiomics. This demonstrated the potentially prognostic values by considering regional variations in radiomics analysis.

The purpose of this study is to develop a CT radiomics-based interpretable prognostic diagnostic model for vascularized bone graft hip preservation, with the objective of predicting postoperative hip preservation outcomes. The study recruited 107 patients, collecting preoperative CT scans and preoperative blood biochemistry data. Among these patients, 27 had a good prognosis, while 80 had a poor prognosis. Five machine learning algorithms were employed to develop predictive models evaluating the effectiveness of modified vascularized bone implants in hip preservation. The interpretability of the top-performing models was assessed using SHapley Additive exPlanations (SHAP). Nine radiomic features were extracted from preoperative CT scans to develop a radiomic score. Through univariate and multivariate logistic regression analyses, clinical indicators, including patient age and preoperative platelet-to-lymphocyte ratio (PLR), were retained. Fifteen models were constructed, incorporating clinical, radiomic, and combined approaches across various algorithms. The combined model utilizing the XGBoost algorithm demonstrated superior performance, achieving an AUC of 0.90 (95% CI 0.81-0.98) on the training set and 0.87 (95% CI 0.75-1.00) on the test set. These results showed improvements of around 31% and 28%, respectively, compared to the top performing clinical and radiomic models (p < 0.05). High radiomics scores, a high PLR, and older age were identified as significant predictors of poor prognosis. A robust joint clinical and radiomics model was developed using the XGBoost algorithm for predicting the prognosis of hip-preserving surgery. The predictions of this model were interpreted using SHAP to enhance clinical applications.

Cognitive workload refers to the mental effort required to perform a task and plays a vital role in cognitive functioning and daily decision-making. The precise estimation of cognitive workload can increase efficiency and decrease mental errors. EEG signals are non-invasive and trustworthy, containing useful information about mental and cognitive tasks, and are very effective in measuring cognitive workload. This study aims to classify various cognitive workload levels using EEG signals, primarily by channel selection based on the Pearson Correlation Coefficient, to reduce computational complexity and facilitate real-time applications. As time-frequency decomposition techniques can provide simultaneous time and frequency information for more accurate analysis, three techniques were adopted: Maximal Overlap Discrete Wavelet Transform (MODWT), Empirical Mode Decomposition (EMD), and a hybrid approach combining both. After decomposition, ten statistical features were extracted, and the Improved Distance Evaluation technique was employed to select the most critical features. Classification was performed on these features using three classifiers: Support Vector Machine (SVM), K-Nearest Neighbors, and Decision Tree. The findings revealed the important role of frontal EEG channels in assessing cognitive workload. Additionally, the combined use of MODWT and EMD with the SVM classifier yielded the best classification accuracy for both binary and three-class classification scenarios. The results indicate that the optimal choice of channels, combined with time-frequency decomposition methods, can significantly enhance classification accuracy while reducing system complexity in estimating cognitive workload.

Telehealthcare is an evolving area that typically employs cloud-connected wireless biomedical gadgets for diagnosis, monitoring, and prognosis of diseases. In such environment, data compression, transmission, security and processing effectiveness are key issues. This paper proposes a new method for the automated diagnosis of arrhythmia in an efficient and effective manner. The proposed technique fuses a combination of Level-Crossing Analog-Digital Converters (LCADCs), Enhanced Activity Selection Algorithm (EASA), Adaptive-Rate Filtering (ARF), and ID-CNN. The electrocardiogram (ECG) signal is sampled by using the level-crossing concept. The QRS based segmentation and ARF with lower tap filters are realized. The denoised segments, without any handcrafted features extraction, are classified with one dimensional (1-D) deep convolutional neural network (CNN). Comparison is performed with using statistically extracted features in combination with CNN, existing state-of-the-art classical methods for ECG classification, and recent advanced deep learning models. The goal is to reach an efficient method by attaining a real-time data size reduction, computationally efficient signal preconditioning and a lower latency accurate classification. Five clinically important classes of arrhythmias, collected from the MIT-BIH dataset, are used to examine its applicability. Our experimental results show a 4.2-times diminishing in the count of acquired samples, on average, compared to conventional fix-rate counterparts. Similarly, data dimension reduction results in a more than 7.2-times computational effectiveness of the post denoising stage over the conventional counterparts. Moreover, classification latency is also significantly reduced while still achieving an accuracy rate of 99%.

Background: The increased use of CT has raised concerns about patient radiation exposure. DRLs play a crucial role in optimising radiation dose while maintaining diagnostic quality. In Jordan, the absence of officially established national DRLs across a wide range of CT procedures may contributes to dose variability between healthcare facilities.

Methods: A multicentre, retrospective study was conducted across 10 hospitals in Jordan, involving 4310 adult patients (aged 18-96 years). Radiation dose metrics, including volume CTDI_vol and DLP, were collected from PACS and RIS. The proposed national DRLs were derived from the 75th percentile of the distribution of median CTDI_vol and DLP values from each hospital. Stepwise multiple regression analysis was performed to identify factors contributing to dose variability.

Results: Marked dose variations were observed across hospitals. Head routine non-contrast CT demonstrated the highest median CTDI_vol (65 mGy) and DLP (1572 mGy·cm), while high-resolution chest CT exhibited the lowest (CTDI_vol: 12 mGy; DLP: 230 mGy·cm). The product of mAs was identified as the most significant predictor of dose across all CT examinations. When compared to international DRLs, Jordan's CT dose levels were generally within acceptable ranges, though L-spine CT showed higher than average values.

Conclusion: This study proposes the first national DRLs for 14 common CT examinations in Jordan, based on data collected from hospitals across the country. These benchmarks support dose optimisation, promote standardised protocols, and highlight the need for continuous radiographer training. Future initiatives should expand DRL development to paediatric populations and integrate dose tracking into national quality frameworks.

An aneurysm, enlargement of an artery or vein, weakens the surrounding vascular wall, making it susceptible to rupture and the possibility of life-threatening bleeding, ultimately resulting in death. The placement of flow-diverting stents is a highly utilized and effective method for treating aneurysms. This study presents a novel approach combining CFD simulations, deep neural networks (DNN), and differential evolution optimization (DEO) to optimize hemodynamic conditions in aneurysms. Initially, CFD simulations were conducted to generate a comprehensive dataset of 2,700 simulations with various stent configurations. This dataset was then used to train a DNN model, enabling accurate predictions of velocity, vorticity, and wall shear stress for any stent configuration. The model demonstrated consistent and reliable performance across different configurations. DEO was applied to identify the optimal stent, resulting in a configuration with seven struts. The optimal strut sizes were 0.3184, 0.9599, 0.7889, 0.9599, 1.0073, 1.0073, and 2.9283, with gap sizes of 0.2238, 0.5897, 0.3379, 0.2996, 0.2052, 0.0371, and 0.3068 between the struts. This configuration achieved superior performance in reducing velocity, vorticity, and maximum wall shear stress. The study demonstrated that increasing the number of struts, with a concentration at the proximal aneurysm neck, enhanced flow diversion and minimized hemodynamic risks, especially in regions vulnerable to rupture. Validation through additional CFD simulations confirmed the effectiveness of the optimized stent, demonstrating the potential of the proposed methodology to improve stent design and hemodynamic outcomes in aneurysm treatment.

Glioma is primarily treated through surgical resection, but accurately identifying tumor boundaries remains challenging. Traditional intraoperative diagnostic techniques, such as frozen section pathological examination and intraoperative magnetic resonance imaging, suffer from issues such as long duration, high cost, and complex operation. A rapid and accurate intraoperative auxiliary diagnostic method for glioma based on the differences in dielectric properties combined with machine learning is proposed in this study. Using an open-ended coaxial probe technique, the dielectric properties of 81 glioma tissue samples and 47 normal brain tissue samples from 14 patients were measured over a frequency range of 1 MHz-4 GHz. After feature selection and dimensionality reduction using the Lasso method, four machine learning models-Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN)-were used to classify the samples. Model performance was evaluated using accuracy, precision, recall, F1 score, and the area under the Receiver Operating Characteristic curve (AUC value). The experimental results demonstrated that the dielectric properties of glioma tissues are higher than those of normal brain tissues (with an average increase of 22% in conductivity and 18% in relative permittivity). On the test set, the KNN model exhibited the highest classification accuracy (90%), while the ANN model showed the best AUC value (0.95). This study confirms that the rapid identification of glioma can be achieved based on dielectric properties combined with machine learning techniques, providing neurosurgeons with a novel auxiliary diagnostic technology for precise intraoperative margin detection of glioma.