A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is the most prevalent cause of dementia, characterized by a steady decline in mental, behavioral, and social abilities and impairs a person's capacity for independent functioning. It is a fatal neurodegenerative disease primarily affecting older adults.

OBJECTIVES

The purpose of this literature review is to investigate various AD detection techniques, datasets, input modalities, algorithms, libraries, and performance evaluation metrics used to determine which model or strategy may provide superior performance.

METHOD

The initial search yielded 807 papers, but only 100 research articles were chosen after applying the inclusion-exclusion criteria. This SLR analyzed research items published between January 2019 and December 2022. The ACM, Elsevier, IEEE Xplore Digital Library, PubMed, Springer and Taylor & Francis were systematically searched. The current study considers articles that used Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), APOe4 genotype, Diffusion Tensor Imaging (DTI) and Cerebrospinal Fluid (CSF) biomarkers. The study was performed following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines.

CONCLUSION

According to the literature survey, most studies (n = 76) used the DL strategy. The datasets used by studies were primarily derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The majority of studies (n = 73) used single-modality neuroimaging data, while the remaining used multi-modal input data. In a multi-modality approach, the combination of MRI and PET scans is commonly preferred. Also, Regarding the algorithm used, Convolution Neural Network (CNN) showed the highest accuracy, 100 %, in classifying AD vs. CN subjects whereas the SVM was the most common ML algorithm, with a maximum accuracy of 99.82 %.

Isolation forest-voting fusion-multioutput: A stroke risk classification method based on the multidimensional output of abnormal sample detection

BACKGROUND AND OBJECTIVE

Stroke has become a major disease threatening the health of people around the world. It has the characteristics of high incidence, high fatality, and a high recurrence rate. At this stage, problems such as poor recognition accuracy of stroke screening based on electronic medical records and insufficient recognition of stroke risk levels exist. These problems occur because of the systematic errors of medical equipment and the characteristics of the collectors during the process of electronic medical record collection. Errors can also occur due to misreporting or underreporting by the collection personnel and the strong subjectivity of the evaluation indicators.

METHODS

This paper proposes an isolation forest-voting fusion-multioutput algorithm model. First, the screening data are collected for numerical processing and normalization. The composite feature score index of this paper is used to analyze the importance of risk factors, and then, the isolation forest is used. The algorithm detects abnormal samples, uses the voting fusion algorithm proposed in this article to perform decision fusion prediction classification, and outputs multidimensional (risk factor importance score, abnormal sample label, risk level classification, and stroke prediction) results that can be used as auxiliary decision information by doctors and medical staff.

RESULTS

The isolation forest-voting fusion-multioutput algorithm proposed in this article has five categories (zero risk, low risk, high risk, ischemic stroke (TIA), and hemorrhagic stroke (HE)). The average accuracy rate of stroke prediction reached 79.59 %.

CONCLUSIONS

The isolation forest-voting fusion-multioutput algorithm model proposed in this paper can not only accurately identify the various categories of stroke risk levels and stroke prediction but can also output multidimensional auxiliary decision-making information to help medical staff make decisions, thereby greatly improving the screening efficiency.

Successful localisation of recurrent thyroid cancer using preoperative patent blue dye injection

PURPOSE

In the follow-up of patients with thyroid cancer, recurrences are often detected, posing challenges in locating and removing these lesions in a reoperative setting. This study aimed to assess the effectiveness of preoperative ultrasound (US)-guided injection of patent blue (PB) dye into the recurrences to aid in their safe and efficient removal.

METHODS

In this retrospective analysis, we reviewed the records of the patients in a tertiary care centre between February 2019 and March 2023 who underwent US-guided PB injection in the endocrinology outpatient clinic before reoperative neck surgery. The duration between the injection of PB and the initiation of surgery was recorded. The complications and effectiveness of the procedure were evaluated using ultrasonographic, laboratory, surgical, and pathologic records.

RESULTS

We reached 23 consecutive patients with 28 lesions. The recurrences averaged 8.8 mm (4.1-15.6) in size and were successfully stained in all cases. The median time between the PB injection and the incision was 90 (35-210) min. There were no complications related to the dye injection. The blue recurrences were conveniently identified and removed in all cases.

CONCLUSIONS

A preoperative US-guided injection of PB is a safe, readily available and highly effective technique for localising recurrent tumours, even in small lesions within scarred reoperative neck surgeries.

Acoustic radiation force-induced longitudinal shear wave for ultrasound-based viscoelastic evaluation

Acoustic radiation force (ARF) is widely used to induce shear waves for evaluating the mechanical properties of biological tissues. Two shear waves can be generated when exciting with ARF: a transverse shear wave, also simply called shear wave (SW), and a longitudinal shear wave (LSW). Shear waves (SWs) have been broadly used to assess the mechanical properties. Some articles have reported that the LSW can be used to evaluate mechanical properties locally. However, existing LSW studies are mainly focused on the group velocity evaluation using optical coherence tomography (OCT). Here, we report that a LSW generated with ARF can be used to probe viscoelastic properties, including shear modulus and viscosity, using ultrasound. We took advantage of the surface boundary effect to reflect the LSW, named RLSW, to address the energy deficiency of LSW induced by ARF. We systematically evaluated the experiments with tissue-mimicking viscoelastic phantoms and validated by numerical simulations. Phase velocity and dispersion comparison between the results induced by a RLSW and a SW exhibit good agreement in both the numerical simulations and experimental results. The Kelvin-Voigt (KV) model was used to determine the shear modulus and viscosity. RLSW shows great potential to evaluate localized viscoelastic properties, which could benefit various biomedical applications such as evaluating the viscoelasticity of heterogeneous materials or microscopic lesions of tissues.

Aggregated Pattern Classification Method for improving neural disorder stage detection

BACKGROUND

Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages.

METHOD

The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data.

RESULTS

The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm.

CONCLUSION

The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

Ultrasound Elastography in Benign Gynecology: A Scoping Review

OBJECTIVE

To perform a scoping review of the literature in which ultrasound elastography (UE) has been used in benign gynecology and identify avenues for its use in future research and clinical implementations.

METHODS

A structured search of EMBASE, Medline and Cochrane databases was conducted (last search date April 15th, 2022). Eligible studies included adult participants with female pelvic anatomy. English language papers focusing on the utility of ultrasound elastography applied to benign gynecology were included. Narrative reviews, conference abstracts, and letters to the editor were excluded. Two independent reviewers screened titles and abstracts for inclusion, a third reviewer was consulted in cases of disagreement. Study quality was assessed by a checklist for study implementation and elastography technique. Extracted data included elastography technology, gynecologic application, opportunities for clinical implementation, and strengths and limitations.

RESULTS

The search returned 2026 studies. A total of 40 studies, published between 2013 and 2022, were retained for data extraction. Studies most frequently used shear wave elastography as the method of UE (n = 23), followed by strain elastography (n = 13) and acoustic radiation force impulse (n = 4). Most common clinical applications for UE were the diagnosis of adenomyosis and uterine fibroids (27.5%), assessment of pelvic floor muscle function (22.5%), and describing the elastic properties of polycystic ovaries (17.5%) and the uterine cervix (15.0%). Limitations of the technology were identified as the lack of published reference values for gynecologic organs and difficulties in assessing tissues deep to the transducer.

CONCLUSION

Future research is needed to validate the use of ultrasound elastography in gynecology under both normal and pathologic conditions.

Neural basis underlying the association between thought control ability and happiness: The moderating role of the amygdala

Thought control ability (TCA) plays an important role in individuals' health and happiness. Previous studies demonstrated that TCA was closely conceptually associated with happiness. However, empirical research supporting this relationship was limited. In addition, the neural basis underlying TCA and how this neural basis influences the relationship between TCA and happiness remain unexplored. In the present study, the voxel-based morphometry (VBM) method was adopted to investigate the neuroanatomical basis of TCA in 314 healthy subjects. The behavioral results revealed a significant positive association between TCA and happiness. On the neural level, there was a significant negative correlation between TCA and the gray matter density (GMD) of the bilateral amygdala. Split-half validation analysis revealed similar results, further confirming the stability of the VBM analysis findings. Furthermore, gray matter covariance network and graph theoretical analyses showed positive association between TCA and both the node degree and node strength of the amygdala. Moderation analysis revealed that the GMD of the amygdala moderated the relationship between TCA and happiness. Specifically, the positive association between TCA and self-perceived happiness was stronger in subjects with a lower GMD of the amygdala. The present study indicated the neural basis underlying the association between TCA and happiness and offered a method of improving individual well-being.

Attitudes and Perceptions of Dentists and Dental Residents Practicing in the Navi Mumbai Region Toward the Use of Artificial Intelligence in Dentistry: A Descriptive Survey

Introduction Artificial intelligence (AI) has been gaining considerable attention in recent years within the healthcare field. It has established a presence in various aspects of health sciences, including accurate diagnosis and precise, streamlined treatment. This study aimed to assess the attitudes of dental residents and dentists in the Navi Mumbai region toward the use of AI in dentistry. Methods An online questionnaire-based survey was conducted, inviting 130 dental residents and dentists from the Navi Mumbai region. The collected data were compiled on a worksheet and subjected to descriptive statistical tests, which were expressed in numbers and frequencies. Results A total of 100 responses were received. Sixty-eight percent of individuals agreed that AI helps enhance diagnosis and treatment planning in the dental field. Sixty-five percent of the respondents stated that they are most likely to incorporate AI tools into their practice within the next five years. Conclusion From the present study, it can be inferred that AI is a promising and essential subsidiary tool in dentistry as well as in healthcare as a whole. However, major concerns such as extensive, in-depth training, data security, and cybercrime must be addressed before the full-scale incorporation of AI in the health sciences.

Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification

OBJECTIVES

To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD).

MATERIALS AND METHODS

Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index.

RESULTS

In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively (p <0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively (p < 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9-97.7%) and 94.3% (89.5-97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%).

CONCLUSIONS

FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events.

CLINICAL RELEVANCE STATEMENT

Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events.

KEY POINTS

• Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. • Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. • Full automation can facilitate the application of CCTA in early detection of coronary artery disease.

AVD-YOLOv5: a new lightweight network architecture for high-speed aortic valve detection from a new and large echocardiography dataset

Heart disease detection is currently gaining widespread attention as a means to enhance the accuracy of cardiologists' diagnoses from cardiac images and reduce diagnosis time. Although high-resolution computed tomography (CT) images are typically favored for heart disease detection, the drawbacks of cost and radiation exposure to patients necessitate alternative approaches. In this context, utilizing ultrasound images becomes pivotal to mitigate radiation risks and maintain cost-effectiveness. In this paper, we propose a novel lightweight model, AVD-YOLOv5, designed for automated aortic valve detection on echocardiography images. This model incorporates several enhancements to the YOLOv5 architecture. Notably, the depth-wise separable convolution significantly contributes to the model's lightweight design by reducing the number of parameters while maintaining precision. We have also created a new and larger dataset comprising 260 echocardiography images specifically for aortic valve detection. Experimental results indicate that the precision value of the modified ADV-YOLOv5 model stands at 94.3%, with a recall value of 86.8%. The model also demonstrates a notable 67% reduction in inference time compared to the original YOLOv5 model. Although there is a marginal reduction in precision by 0.94%, the model's efficiency is significantly increased. The proposed system can be used by cardiologists for more efficient and reliable diagnosis.

Unsupervised denoising of photoacoustic images based on the Noise2Noise network

In this study, we implemented an unsupervised deep learning method, the Noise2Noise network, for the improvement of linear-array-based photoacoustic (PA) imaging. Unlike supervised learning, which requires a noise-free ground truth, the Noise2Noise network can learn noise patterns from a pair of noisy images. This is particularly important for in vivo PA imaging, where the ground truth is not available. In this study, we developed a method to generate noise pairs from a single set of PA images and verified our approach through simulation and experimental studies. Our results reveal that the method can effectively remove noise, improve signal-to-noise ratio, and enhance vascular structures at deeper depths. The denoised images show clear and detailed vascular structure at different depths, providing valuable insights for preclinical research and potential clinical applications.

TAJ-Net: a two-stage clustered cell segmentation network with adaptive joint learning of spatial and spectral information

Pulmonary adenocarcinoma is the primary cause of cancer-related death worldwide and pathological diagnosis is the "golden standard" based on the regional distribution of cells. Thus, regional cell segmentation is a key step while it is challenging due to the following reasons: 1) It is hard for pure semantic and instance segmentation methods to obtain a high-quality regional cell segmentation result; 2) Since the spatial appearances of pulmonary cells are very similar which even confuse pathologists, annotation errors are usually inevitable. Considering these challenges, we propose a two-stage 3D adaptive joint training framework (TAJ-Net) to segment-then-classify cells with extra spectral information as the supplementary information of spatial information. Firstly, we propose to leverage a few-shot method with limited data for cell mask acquisition to avoid the disturbance of cluttered backgrounds. Secondly, we introduce an adaptive joint training strategy to remove noisy samples through two 3D networks and one 1D network for cell type classification rather than segmentation. Subsequently, we propose a patch mapping method to map classification results to the original images to obtain regional segmentation results. In order to verify the effectiveness of TAJ-Net, we build two 3D hyperspectral datasets, i.e., pulmonary adenocarcinoma (3,660 images) and thyroid carcinoma (4623 images) with 40 bands. The first dataset will be released for further research. Experiments show that TAJ-Net achieves much better performance in clustered cell segmentation, and it can regionally segment different kinds of cells with high overlap and blurred edges, which is a difficult task for the state-of-the-art methods. Compared to 2D models, the hyperspectral image-based 3D model reports a significant improvement of up to 11.5% in terms of the Dice similarity coefficient in the pulmonary adenocarcinoma dataset.

Efficacy of Whole-Blood Model of Gadolinium-Based Contrast Agent Relaxivity in Predicting Vascular MR Signal Intensity In Vivo

BACKGROUND

Previous in vitro studies have described sub-linear longitudinal and heightened transverse H2O relaxivities of gadolinium-based contrast agents (GBCAs) in blood due to their extracellular nature. However, in vivo validation is lacking.

PURPOSE

Validate theory describing blood behavior of R1 and R2* in an animal model.

STUDY TYPE

Prospective, animal.

ANIMAL MODEL

Seven swine (54-65 kg).

FIELD STRENGTH/SEQUENCE

1.5 T; time-resolved 3D spoiled gradient-recalled echo (SPGR) and quantitative Look-Locker and multi-echo fast field echo sequences.

ASSESSMENT

Seven swine were each injected three times with 0.1 mmol/kg intravenous doses of one of three GBCAs: gadoteridol, gadobutrol, and gadobenate dimeglumine. Injections were randomized for rate (1, 2, and 3 mL/s) and order, during which time-resolved aortic 3D SPGR imaging was performed concurrently with aortic blood sampling via an indwelling catheter. Time-varying [GBCA] was measured by mass spectrometry of sampled blood. Predicted signal intensity (SI) was determined from a model incorporating sub-linear R1 and R2* effects (whole-blood model) and simpler models incorporating linear R1, with and without R2* effects. Predicted SIs were compared to measured aortic SI.

STATISTICAL TESTS

Linear correlation (coefficient of determination, R2) and mean errors were compared across the SI prediction models.

RESULTS

There was an excellent correlation between predicted and measured SI across all injections and swine when accounting for the non-linear dependence of R1 and high blood R2* (regression slopes 0.91-1.04, R2 ≥ 0.91). Simplified models (linear R1 with and without R2* effects) showed poorer correlation (slopes 0.67-0.85 and 0.54-0.64 respectively, both R2 ≥ 0.89) and higher averaged mean absolute and mean square errors (128.4 and 177.4 vs. 42.0, respectively, and 5506 and 11,419 vs. 699, respectively).

DATA CONCLUSION

Incorporating sub-linear R1 and high first-pass R2* effects in arterial blood models allows accurate SPGR SI prediction in an in vivo animal model, and might be utilized when modeling MR blood SI.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 1.

Ultrasonic synthesis of green lipid nanocarriers loaded with Scutellaria barbata extract: a sustainable approach for enhanced anticancer and antibacterial therapy

Ultrasonic manufacturing has emerged as a promising eco-friendly approach to synthesize lipid-based nanocarriers for targeted drug delivery. This study presents the novel ultrasonic preparation of lipid nanocarriers loaded with Scutellaria barbata extract, repurposed for anticancer and antibacterial use. High-frequency ultrasonic waves enabled the precise self-assembly of DSPE-PEG, Span 40, and cholesterol to form nanocarriers encapsulating the therapeutic extract without the use of toxic solvents, exemplifying green nanotechnology. Leveraging the inherent anticancer and antibacterial properties of Scutellaria barbata, the study demonstrates that lipid encapsulation enhances the bioavailability and controlled release of the extract, which is vital for its therapeutic efficacy. Dynamic light scattering and transmission electron microscopy analyses confirmed the increase in size and successful encapsulation post-loading, along with an augmented negative zeta potential indicating enhanced stability. A high encapsulation efficiency of 91.93% was achieved, and in vitro assays revealed the loaded nanocarriers' optimized release kinetics and improved antimicrobial potency against Pseudomonas aeruginosa, compared to the free extract. The combination of ultrasonic synthesis and Scutellaria barbata in an eco-friendly manufacturing process not only advances green nanotechnology but also contributes to sustainable practices in pharmaceutical manufacturing. The data suggest that this innovative nanocarrier system could provide a robust platform for the development of nanotechnology-based therapeutics, enhancing drug delivery efficacy while aligning with environmental sustainability.

Role of Noncontrast Computed Tomography Parameters in Predicting the Outcome of Extracorporeal Shock Wave Lithotripsy for Upper Urinary Stones Cases: A Meta-analysis

RATIONALE

Extracorporeal shock wave lithotripsy (ESWL) is widely considered the primary approach for managing urinary tract stones. This study aimed to assess the predictive factors associated with non-contrast computed tomography (NCCT)-based parameters of upper urinary stones in relation to the outcomes of ESWL.

MATERIALS AND METHODS

A systematic search was conducted in PubMed, ScienceDirect, Web of Science, and Cochrane Library to identify all relevant studies published up to June 3, 2023. Several NCCT-based parameters to predict ESWL outcomes, comprised of mean stone density (MSD), skin-to-stone distance (SSD), and stone size, were extracted and analyzed using Review Manager software.

RESULTS

Out of 979 publications screened, a total of 39 publications, involving 7869 patients, were enrolled in the analysis. The pooled estimate demonstrated significant differences between MSD, and stone size between successful and failure of stone fragmentation groups, in which lower values of these parameters are associated with successful ESWL outcomes.

CONCLUSION

The results from the current study suggested that lower NCCT parameters, notably MSD, SSD, and stone size, are significantly associated with successful ESWL outcome. However, additional large-scale prospective studies are required to utilize these parameters effectively, and the optimal cutoff value should be determined.

Application of 18F-FDG PET/CT imaging in a primary angiomatoid fibrous histiocytoma of pulmonary bronchus: case report and literature review

Background

Angiomatoid fibrous histiocytoma (AFH) is a clinically rare, low-grade malignant soft tissue tumor that occasionally metastasizes. It accounts for 0.3% of all soft tissue tumors and most frequently occurs in the extremities, followed by the trunk, and the head and neck. Primary angiomatoid fibrous histiocytoma (PAFH) of the pulmonary bronchus is rare. In this paper, the clinical and imaging data of a case of PAFH of the pulmonary bronchus are reported, and the literature is reviewed.

Case description

A 57-year-old female patient presented with a six-month history of cough without apparent cause, characterized by paroxysmal dry cough, chest tightness, and shortness of breath, which worsened with activity. She did not experience fever, chills, chest pain, hemoptysis, or night sweats. Laboratory tests revealed an elevated C-reactive protein and ferritin levels, while tumor markers such as AFP, CEA, CA199, CA125, CA50, and T-SPOT were negative. A chest CT scan showed bronchial obstruction, atelectasis, and a soft tissue density in the right middle lobe of the lung. The enhanced scan demonstrated uneven enhancement of endobronchial nodules. An 18F-FDG PET/CT scan revealed a nodular soft tissue density shadow in the right lung bronchus with uneven density, clear boundaries, and increased 18F-FDG uptake, with a maximum standard uptake value (SUVmax) of 11.2. Bronchoscopy revealed a nodular or polypoid mass that was yellow and tough. Based on imaging findings, the preoperative diagnosis favored lung cancer. However, the postoperative pathological diagnosis confirmed primary angiomatoid fibrous histiocytoma (PAFH) of the pulmonary bronchus.

Conclusion

The incidence of primary angiomatoid fibrous histiocytoma (PAFH) is very low, and its clinical manifestations and imaging findings lack specificity, with the final diagnosis relying on pathology. PET/CT imaging has a certain value in the diagnosis of PAFH and holds significant application value in preoperative staging, postoperative efficacy evaluation, and follow-up monitoring. In conclusion, this case report further expands the spectrum of lung and bronchial tumors.

Customization of Computed Tomography Radio-Opacity in 3D-Printed Contrast-Injectable Tumor Phantoms

Medical Imaging Phantoms (MIPs) calibrate imaging devices, train medical professionals, and can help procedural planning. Traditional MIPs are costly and limited in customization. Additive manufacturing allows for customizable, patient-specific phantoms. This study examines the CT attenuation characteristics of contrast-injectable, chambered 3D-printed phantoms to optimize tissue-mimicking capabilities. A MIP was constructed from a CT of a complex pelvic tumor near the iliac bifurcation. A 3D reconstruction of these structures composed of three chambers (aorta, inferior vena cava, tumor) with ports for contrast injection was 3D printed. Desired attenuations were 200 HU (arterial I), 150 HU (venous I), 40 HU (tumor I), 150 HU (arterial II), 90 HU (venous II), and 400 HU (tumor II). Solutions of Optiray 350 and water were injected, and the phantom was scanned on CT. Attenuations were measured using ROIs. Mean attenuation for the six phases was as follows: 37.49 HU for tumor I, 200.50 HU for venous I, 227.92 HU for arterial I, 326.20 HU for tumor II, 91.32 HU for venous II, and 132.08 HU for arterial II. Although the percent differences between observed and goal attenuation were high, the observed relative HU differences between phases were similar to goal HU differences. The observed attenuations reflected the relative concentrations of contrast solutions used, exhibiting a strong positive correlation with contrast concentration. The contrast-injectable tumor phantom exhibited a useful physiologic range of attenuation values, enabling the modification of tissue-mimicking 3D-printed phantoms even after the manufacturing process.

An efficient multi-level thresholding method for breast thermograms analysis based on an improved BWO algorithm

Breast cancer is a prevalent disease and the second leading cause of death in women globally. Various imaging techniques, including mammography, ultrasonography, X-ray, and magnetic resonance, are employed for detection. Thermography shows significant promise for early breast disease detection, offering advantages such as being non-ionizing, non-invasive, cost-effective, and providing real-time results. Medical image segmentation is crucial in image analysis, and this study introduces a thermographic image segmentation algorithm using the improved Black Widow Optimization Algorithm (IBWOA). While the standard BWOA is effective for complex optimization problems, it has issues with stagnation and balancing exploration and exploitation. The proposed method enhances exploration with Levy flights and improves exploitation with quasi-opposition-based learning. Comparing IBWOA with other algorithms like Harris Hawks Optimization (HHO), Linear Success-History based Adaptive Differential Evolution (LSHADE), and the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and black widow optimization (BWO) using otsu and Kapur's entropy method. Results show IBWOA delivers superior performance in both qualitative and quantitative analyses including visual inspection and metrics such as fitness value, threshold values, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental results demonstrate the outperformance of the proposed IBWOA, validating its effectiveness and superiority.

A multibranch and multiscale neural network based on semantic perception for multimodal medical image fusion

Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI and CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information from multiple modalities, often suffer from poor image quality and loss of crucial details due to inadequate handling of semantic information and limited feature extraction capabilities. This paper introduces a novel medical image fusion technique leveraging unsupervised image segmentation to enhance the semantic understanding of the fusion process. The proposed method, named DUSMIF, employs a multi-branch, multi-scale deep learning architecture that integrates advanced attention mechanisms to refine the feature extraction and fusion processes. An innovative approach that utilizes unsupervised image segmentation to extract semantic information is introduced, which is then integrated into the fusion process. This not only enhances the semantic relevance of the fused images but also improves the overall fusion quality. The paper proposes a sophisticated network structure that extracts and fuses features at multiple scales and across multiple branches. This structure is designed to capture a comprehensive range of image details and contextual information, significantly improving the fusion outcomes. Multiple attention mechanisms are incorporated to selectively emphasize important features and integrate them effectively across different modalities and scales. This approach ensures that the fused images maintain high quality and detail fidelity. A joint loss function combining content loss, structural similarity loss, and semantic loss is formulated. This function not only guides the network in preserving image brightness and texture but also ensures that the fused image closely resembles the source images in both content and structure. The proposed method demonstrates superior performance over existing fusion techniques in objective assessments and subjective evaluations, confirming its effectiveness in enhancing the diagnostic utility of fused medical images.

Electroacupuncture protects against cerebral ischemia-reperfusion injury through mitochondrial dynamics

Background

Electroacupuncture (EA) has been shown to promote functional recovery after cerebral ischemia-reperfusion (I/R) injury. However, the contribution of mitochondrial dynamics to recovery remains unclear. The aim of this study was to investigate whether mitochondrial dynamics are involved in the effects of EA on cerebral I/R injury.

Methods

The rats with cerebral I/R injury were established by the middle cerebral artery occlusion/reperfusion. Subsequently, EA was applied to Baihui (GV20) and Dazhui (GV14) acupoints, with 2 Hz/5 Hz in frequency, 1.0 mA in intensity, 20 min each time, once a day for seven consecutive days. The therapeutic outcomes were assessed by modified neurological severity score (mNSS), 2,3,5-Triphenyte-trazolium chloride (TTC) staining, and hematoxylin-eosin (HE) staining. Mitochondrial morphology was observed under transmission electron microscopy. Adenosine triphosphate (ATP) content and ATP synthases (ATPases) activity were evaluated to measure mitochondrial function using ELISA. Finally, mitochondrial dynamics-related molecules, including dynamin-related protein 1 (Drp1), fission 1 (Fis1), mitofusin 1 (Mfn1), mitofusin 2 (Mfn2), and optic atrophy 1 (OPA1), were detected by Western blot and immunofluorescence staining.

Results

Cerebral I/R injury induced neurological dysfunction, cerebral infarction and neuronal injury, all of which were ameliorated by EA. And EA improved mitochondrial morphology and function. Moreover, EA altered the balance of mitochondrial dynamics. Specifically, the data showed a significant decrease in the expression of Drp1 and Fis1, leading to the inhibition of mitochondrial fission. Additionally, Mfn1, Mfn2 and Opa1, which are related to mitochondrial fusion, were effectively promoted after EA treatment. However, sham EA did not show any neuroprotective effects in rats with cerebral I/R injury.

Conclusions

In summary, our study indicates that the balance of mitochondrial dynamics is crucial for EA therapy to treat cerebral I/R injury.

Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer

PURPOSE

A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study.

METHODS

A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis.

RESULTS

The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis.

CONCLUSION

This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

Special Types of Breast Cancer: Clinical Behavior and Radiological Appearance

Breast cancer is a complex disease that includes entities with different characteristics, behaviors, and responses to treatment. Breast cancers are categorized into subgroups based on histological type and grade, and these subgroups affect clinical presentation and oncological outcomes. The subgroup of "special types" encompasses all those breast cancers with insufficient features to belong to the subgroup "invasive ductal carcinoma not otherwise specified". These cancers account for around 25% of all cases, some of them having a relatively good prognosis despite high histological grade. The purpose of this paper is to review and illustrate the radiological appearance of each special type, highlighting insights and pitfalls to guide breast radiologists in their routine work.

Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review

BACKGROUND

Metastasis commonly occur in the bone tissue. Artificial intelligence (AI) has become increasingly prevalent in the medical sector as support in decision-making, diagnosis, and treatment processes. The objective of this systematic review was to assess the reliability of AI systems in clinical, radiological, and pathological aspects of bone metastases.

METHODS

We included studies that evaluated the use of AI applications in patients affected by bone metastases. Two reviewers performed a digital search on 31 December 2023 on PubMed, Scopus, and Cochrane library and extracted authors, AI method, interest area, main modalities used, and main objectives from the included studies.

RESULTS

We included 59 studies that analyzed the contribution of computational intelligence in diagnosing or forecasting outcomes in patients with bone metastasis. Six studies were specific for spine metastasis. The study involved nuclear medicine (44.1%), clinical research (28.8%), radiology (20.4%), or molecular biology (6.8%). When a primary tumor was reported, prostate cancer was the most common, followed by lung, breast, and kidney.

CONCLUSIONS

Appropriately trained AI models may be very useful in merging information to achieve an overall improved diagnostic accuracy and treatment for metastasis in the bone. Nevertheless, there are still concerns with the use of AI systems in medical settings. Ethical considerations and legal issues must be addressed to facilitate the safe and regulated adoption of AI technologies. The limitations of the study comprise a stronger emphasis on early detection rather than tumor management and prognosis as well as a high heterogeneity for type of tumor, AI technology and radiological techniques, pathology, or laboratory samples involved.

Low bone mineral density: a primer for the spine surgeon

Within spinal surgery, low bone mineral density is associated with several postoperative complications, such as proximal junctional kyphosis, pseudoarthrosis, and screw loosening. Although modalities such as CT and MRI can be utilized to assess bone quality, DEXA scans, the "Gold Standard" for diagnosing osteoporosis, is not routinely included in preoperative workup. With an increasing prevalence of osteoporosis in an aging population, it is critical for spine surgeons to understand the importance of evaluating bone mineral density preoperatively to optimize postoperative outcomes. The purpose of this state-of-the-art review is to provide surgeons a summary of the evaluation, treatment, and implications of low bone mineral density in patients who are candidates for spine surgery.

Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.

Separation of ventilation and perfusion of electrical impedance tomography image streams using multi-dimensional ensemble empirical mode decomposition

Objective.In the future, thoracic electrical impedance tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as ventilation-related and cardiac-related signals is missing.Approach.This study analyses the potential ofMulti-dimensional Ensemble Empirical Mode Decompositionby application of theComplete Ensemble Empirical Mode Decomposition with Adaptive Noiseand a novel frequency-based combination criterion for detrending, denoising and source separation of EIT image streams, collected from nine healthy male test subjects with similar age and constitution.Main results.In this paper, a novel approach to estimate the lung, the heart and the perfused regions of an EIT image is proposed, which is based on theRoot Mean Square Errorbetween the index of maximal respiratory and cardiac variation to their surroundings. The summation of the indexes of the respective regions reveals physiologically meaningful time signals, separated into the physiological bandwidths of ventilation and heart activity at rest. Moreover, the respective regions were compared with the relative thorax movement and photoplethysmogram (PPG) signal. In linear regression analysis and in the Bland-Altman plot, the beat-to-beat time course of both the ventilation-related signal and the cardiac-related signal showed a high similarity with the respective reference signal.Significance.Analysis of the data reveals a fair separation of ventilatory and cardiac activity realizing the aimed source separation, with optional detrending and denoising. For all performed analyses, a feasible correlation of 0.587 to 0.905 was found between the cardiac-related signal and the PPG signal.

A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data

Unexpected seizures significantly decrease the quality of life in epileptic patients. Seizure attacks are caused by hyperexcitability and anatomical lesions of special regions of the brain, and cognitive impairments and memory deficits are their most common concomitant effects. In addition to seizure reduction treatments, medical rehabilitation involving brain-computer interfaces and neurofeedback can improve cognition and quality of life in patients with focal epilepsy in most cases, in particular when resective epilepsy surgery has been considered treatment in drug-resistant epilepsy. Source estimation and precise localization of epileptic foci can improve such rehabilitation and treatment. Electroencephalography (EEG) monitoring and multimodal noninvasive neuroimaging techniques such as ictal/interictal single-photon emission computerized tomography (SPECT) imaging and structural magnetic resonance imaging are common practices for the localization of epileptic foci and have been studied in several kinds of researches. In this article, we review the most recent research on EEG-based localization of seizure foci and discuss various methods, their advantages, limitations, and challenges with a focus on model-based data processing and machine learning algorithms. In addition, we survey whether combined analysis of EEG monitoring and neuroimaging techniques, which is known as multimodal brain data fusion, can potentially increase the precision of the seizure foci localization. To this end, we further review and summarize the key parameters and challenges of processing, fusion, and analysis of multiple source data, in the framework of model-based signal processing, for the development of a multimodal brain data analyzing system. This article has the potential to be used as a valuable resource for neuroscience researchers for the development of EEG-based rehabilitation systems based on multimodal data analysis related to focal epilepsy.

Advancements in Interventional Pulmonology: Harnessing Ultrasound Techniques for Precision Diagnosis and Treatment

Medical ultrasound has emerged as an indispensable tool within interventional pulmonology, revolutionizing diagnostic and procedural practices through its non-invasive nature and real-time visualization capabilities. By harnessing the principles of sound waves and employing a variety of transducer types, ultrasound facilitates enhanced accuracy and safety in procedures such as transthoracic needle aspiration and pleural effusion drainage, consequently leading to improved patient outcomes. Understanding the fundamentals of ultrasound physics is paramount for clinicians, as it forms the basis for interpreting imaging results and optimizing interventions. Thoracic ultrasound plays a pivotal role in diagnosing conditions like pleural effusions and pneumothorax, while also optimizing procedures such as thoracentesis and biopsy by providing precise guidance. Advanced ultrasound techniques, including endobronchial ultrasound, has transformed the evaluation and biopsy of lymph nodes, bolstered by innovative features like elastography, which contribute to increased procedural efficacy and patient safety. Peripheral ultrasound techniques, notably radial endobronchial ultrasound (rEBUS), have become essential for assessing pulmonary nodules and evaluating airway structures, offering clinicians valuable insights into disease localization and severity. Neck ultrasound serves as a crucial tool in guiding supraclavicular lymph node biopsy and percutaneous dilatational tracheostomy procedures, ensuring safe placement and minimizing associated complications. Ultrasound technology is suited for further advancement through the integration of artificial intelligence, miniaturization, and the development of portable devices. These advancements hold the promise of not only improving diagnostic accuracy but also enhancing the accessibility of ultrasound imaging in diverse healthcare settings, ultimately expanding its utility and impact on patient care. Additionally, the integration of enhanced techniques such as contrast-enhanced ultrasound and 3D imaging is anticipated to revolutionize personalized medicine by providing clinicians with a more comprehensive understanding of anatomical structures and pathological processes. The transformative potential of medical ultrasound in interventional pulmonology extends beyond mere technological advancements; it represents a paradigm shift in healthcare delivery, empowering clinicians with unprecedented capabilities to diagnose and treat pulmonary conditions with precision and efficacy. By leveraging the latest innovations in ultrasound technology, clinicians can navigate complex anatomical structures with confidence, leading to more informed decision-making and ultimately improving patient outcomes. Moreover, the portability and versatility of modern ultrasound devices enable their deployment in various clinical settings, from traditional hospital environments to remote or resource-limited areas, thereby bridging gaps in healthcare access and equity.

Continuum topological derivative - a novel application tool for denoising CT and MRI medical images

BACKGROUND

CT and MRI modalities are important diagnostics tools for exploring the anatomical and tissue properties, respectively of the human beings. Several advancements like HRCT, FLAIR and Propeller have advantages in diagnosing the diseases very accurately, but still have enough space for improvements due to the presence of inherent and instrument noises. In the case of CT and MRI, the quantum mottle and the Gaussian and Rayleigh noises, respectively are still present in their advanced modalities of imaging. This paper addresses the denoising problem with continuum topological derivative technique and proved its trustworthiness based on the comparative study with other traditional filtration methods such as spatial, adaptive, frequency and transformation techniques using measures like visual inspection and performance metrics.

METHODS

This research study focuses on identifying a novel method for denoising by testing different filters on HRCT (High-Resolution Computed Tomography) and MR (Magnetic Resonance) images. The images were acquired from the Image Art Radiological Scan Centre using the SOMATOM CT and SIGNA Explorer (operating at 1.5 Tesla) machines. To compare the performance of the proposed CTD (Continuum Topological Derivative) method, various filters were tested on both HRCT and MR images. The filters tested for comparison were Gaussian (2D convolution operator), Wiener (deconvolution operator), Laplacian and Laplacian diagonal (2nd order partial differential operator), Average, Minimum, and Median (ordinary spatial operators), PMAD (Anisotropic diffusion operator), Kuan (statistical operator), Frost (exponential convolution operator), and HAAR Wavelet (time-frequency operator). The purpose of the study was to evaluate the effectiveness of the CTD method in removing noise compared to the other filters. The performance metrics were analyzed to assess the diligence of noise removal achieved by the CTD method. The primary outcome of the study was the removal of quantum mottle noise in HRCT images, while the secondary outcome focused on removing Gaussian (foreground) and Rayleigh (background) noise in MR images. The study aimed to observe the dynamics of noise removal by examining the values of the performance metrics. In summary, this study aimed to assess the denoising ability of various filters in HRCT and MR images, with the CTD method being the proposed approach. The study evaluated the performance of each filter using specific metrics and compared the results to determine the effectiveness of the CTD method in removing noise from the images.

RESULTS

Based on the calculated performance metric values, it has been observed that the CTD method successfully removed quantum mottle noise in HRCT images and Gaussian as well as Rayleigh noise in MRI. This can be evidenced by the PSNR (Peak Signal-to-Noise Ratio) metric, which consistently exhibited values ranging from 50 to 65 for all the tested images. Additionally, the CTD method demonstrated remarkably low residual values, typically on the order of e-09, which is a distinctive characteristic across all the images. Furthermore, the performance metrics of the CTD method consistently outperformed those of the other tested methods. Consequently, the results of this study have significant implications for the quality, structural similarity, and contrast of HRCT and MR images, enabling clinicians to obtain finer details for diagnostic purposes.

CONCLUSION

Continuum topological derivative algorithm is found to be constructive in removing prominent noises in both CT and MRI images and can serve as a potential tool for recognition of anatomical details in case of diseased and normal ones. The results obtained from this research work are highly inspiring and offer great promise in obtaining accurate diagnostic information for critical cases such as Thoracic Cavity Carina, Brain SPI Globe Lens 4th Ventricle, Brain-Middle Cerebral Artery, Brain-Middle Cerebral Artery and neoplastic lesions. These findings lay the foundation for implementing the proposed CTD technique in routine clinical diagnosis.

Neoadjuvant treatment of rectal cancer: Where we are and where we are going

Locally advanced rectal cancer requires a multidisciplinary approach based on total neoadjuvant treatment with radiotherapy (RT) and chemotherapy (ChT), followed by deferred surgery. Currently, alternatives to the standard total neoadjuvant therapy (TNT) are being explored, such as new ChT regimens or the introduction of immunotherapy. With standard TNT, up to a third of patients may achieve a complete pathological response (CPR), potentially avoiding surgery. However, as of now, we lack predictive markers of response that would allow us to define criteria for a conservative organ strategy. The presence of mutations, genes, or new imaging tests is helping to define these criteria. An example of this is the diffusion coefficient in the diffusion-weighted sequence of magnetic resonance imaging and the integration of this imaging technique into RT treatment. This allows for the monitoring of the evolution of this coefficient over successive RT sessions, helping to determine which patients will achieve CPR or those who may require intensification of neoadjuvant therapy.

Beyond stiffness: deciphering the role of viscoelasticity in cancer evolution and treatment response

There is increasing evidence that cancer progression is linked to tissue viscoelasticity, which challenges the commonly accepted notion that stiffness is the main mechanical hallmark of cancer. However, this new insight has not reached widespread clinical use, as most clinical trials focus on the application of tissue elasticity and stiffness in diagnostic, therapeutic, and surgical planning. Therefore, there is a need to advance the fundamental understanding of the effect of viscoelasticity on cancer progression, to develop novel mechanical biomarkers of clinical significance. Tissue viscoelasticity is largely determined by the extracellular matrix (ECM), which can be simulatedin vitrousing hydrogel-based platforms. Since the mechanical properties of hydrogels can be easily adjusted by changing parameters such as molecular weight and crosslinking type, they provide a platform to systematically study the relationship between ECM viscoelasticity and cancer progression. This review begins with an overview of cancer viscoelasticity, describing how tumor cells interact with biophysical signals in their environment, how they contribute to tumor viscoelasticity, and how this translates into cancer progression. Next, an overview of clinical trials focused on measuring biomechanical properties of tumors is presented, highlighting the biomechanical properties utilized for cancer diagnosis and monitoring. Finally, this review examines the use of biofabricated tumor models for studying the impact of ECM viscoelasticity on cancer behavior and progression and it explores potential avenues for future research on the production of more sophisticated and biomimetic tumor models, as well as their mechanical evaluation.

Evaluating stereotactic accuracy with patient-specific MRI distortion corrections for frame-based radiosurgery

PURPOSE

This study examines how MRI distortions affect frame-based SRS treatments and assesses the need for clinical distortion corrections.

METHODS

The study included 18 patients with 80 total brain targets treated using frame-based radiosurgery. Distortion within patients' MRIs were corrected using Cranial Distortion Correction (CDC) software, which utilizes the patient's CT to alter planning MRIs to reduce inherent intra-cranial distortion. Distortion was evaluated by comparing the original planning target volumes (PTVORIG) to targets contoured on corrected MRIs (PTVCORR). To provide an internal control, targets were also re-contoured on uncorrected (PTVRECON) MRIs. Additional analysis was done to assess if 1 mm expansions to PTVORIG targets would compensate for patient-specific distortions. Changes in target volumes, DICE and JACCARD similarity coefficients, minimum PTV dose (Dmin), dose to 95% of the PTV (D95%), and normal tissue receiving 12 Gy (V12Gy), 10 Gy (V10Gy), and 5 Gy (V5Gy) were calculated and evaluated. Student's t-tests were used to determine if changes in PTVCORR were significantly different than intra-contouring variability quantified by PTVRECON.

RESULTS

PTVRECON and PTVCORR relative changes in volume were 6.19% ± 10.95% and 1.48% ± 32.92%. PTVRECON and PTVCORR similarity coefficients were 0.90 ± 0.08 and 0.73 ± 0.16 for DICE and 0.82 ± 0.12 and 0.60 ± 0.18 for JACCARD. PTVRECON and PTVCORR changes in Dmin were -0.88% ± 8.77% and -12.9 ± 17.3%. PTVRECON and PTVCORR changes in D95% were -0.34% ± 5.89 and -8.68% ± 13.21%. The 1 mm expanded PTVORIG targets did not entirely cover 14 of the 80 PTVCORR targets. Normal tissue changes (V12Gy, V10Gy, V5Gy) calculated with PTVRECON were (-0.09% ± 7.39%, -0.38% ± 5.67%, -0.08% ± 2.04%) and PTVCORR were (-2.14% ± 7.34%, -1.42% ± 5.45%, -0.61% ± 1.93%). Except for V10Gy, all PTVCORR changes were significantly different (p < 0.05) than PTVRECON.

CONCLUSION

MRIs used for SRS target delineation exhibit notable geometric distortions that may compromise optimal dosimetric accuracy. A uniform 1 mm expansion may result in geometric misses; however, the CDC algorithm provides a feasible solution for rectifying distortions, thereby enhancing treatment precision.

Alveolar Soft Tissue Sarcoma: Correlation of MRI Features With Histological Grading and Patient Prognosis

BACKGROUND

Alveolar Soft Part Sarcoma (ASPS) is a rare, aggressive cancer whose diagnosis and treatment depend on histological grading. However, tumor variability can lead to underestimation, affecting treatment, and patient survival.

OBJECTIVE

To evaluate MRI features associated with Grade III ASPS and to determine the relationship between MRI features and patient prognosis.

STUDY TYPE

Retrospective analysis.

SUBJECTS

Sixty-seven patients with ASPS were included with 37 males and 30 females (M/F = 1.23) follow-up and survival analysis on 50 patients.

FIELD STRENGTH/SEQUENCE

A 3.0 T, T1WI-FSE, T2WI-FSE, DWI-EPI, DCE-MRI (gradient echo).

ASSESSMENT

MRI features (margin, peritumoral oedema, peritumoral enhancement, necrosis, vascular flow void signal, heterogeneous signal intensity [SI] at T1WI and T2WI, ADCmean, time-intensity curve [TIC] type, distant metastasis, and bone invasion) and histological grading were independently evaluated by three radiologists and two pathologists, with Grade III considered high-grade.

STATISTICAL TESTS

The chi-square or Fisher's exact test was used to assess the correlation between MRI features and histological grading. Multivariable binary logistic regression identified independent factors associated with high-grade tumors. The Kaplan-Meier method and Cox proportional hazards model were used to calculate hazard ratios for MRI features.

RESULTS

Tumor necrosis, heterogeneous SI at T2WI ≥50%, and ADCmean were associated with high-grade ASPS. Tumor necrosis was an independent factors associated with local relapse-free survival (odds ratio [OR], 3.88). TIC type was associated with 5-year survival rate (OR, 2.80) and local relapse-free survival (OR, 2.69). Heterogeneous SI at T2WI ≥50% was associated with 5-year survival (OR, 4.00), local relapse-free survival (OR, 5.58), and local relapse-free survival (OR, 4.84).

DATA CONCLUSION

MRI features including tumor necrosis, heterogeneity of SI at T2WI, ADCmean, and TIC type aid in assessing ASPS grading and prognosis.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 5.

Native and Gd-EOB-DTPA-Enhanced T1 mapping for Assessment of Liver Fibrosis in NAFLD: Comparative Analysis of Modified Look-Locker Inversion Recovery and Water-specific T1 mapping

RATIONALE AND OBJECTIVES

To investigate the diagnostic performance of water-specific T1 mapping for staging liver fibrosis in a non-alcoholic fatty liver disease (NAFLD) rabbit model, in comparison to Modified Look-Locker Inversion recovery (MOLLI) T1 mapping.

MATERIALS AND METHODS

60 rabbits were randomly divided into the control group (12 rabbits) and NAFLD model groups (eight rabbits per subgroup) corresponding to different durations of high-fat high cholesterol diet feeding. All rabbits underwent MRI examination including MOLLI T1 mapping and 3D multi-echo variable flip angle (VFAME- GRE) sequences were acquired before and 20 min after the administration of Gd- EOB-DTPA. Histological assessments were performed to evaluate steatosis, inflammation, ballooning, and fibrosis. Statistical analysis included the intraclass correlation coefficient, analysis of variance, spearman correlation, multiple linear regression, and receiver operating characteristic curve.

RESULTS

A moderate correlation was observed between conventional native T1 and MRI-PDFF (r = -0.513, P < 0.001), as well as between conventional native T1 and liver steatosis grades (r = -0.319, P = 0.016). However, no significant correlation was found between the native wT1 and PDFF (r = 0.137, P = 0.314), or between the native wT1 and steatosis grades (r = 0.106, P = 0.435). In the multiple regression analysis, liver fibrosis, and hepatocellular ballooning were identified as independent factors influencing native wT1 in this study (R2 =0.545, P < 0.05), while steatosis was independently associated with conventional native T1 (R2 =0.321, P < 0.05). The AUC values for native T1, native wT1, HBP T1, and HBP wT1 were 0.549(0.410-0.682), 0.811(0.684-0.903), 0.775(0.644-0.876), and 0.752(0.619-0.858) for F1 or higher, 0.581(0.441-0.711), 0.828(0.704-0.916), 0.832(0.708-0.919), and 0.854(0.734-0.934) for F2 or higher, respectively.

CONCLUSION

The native wT1 may provide a more reliable assessment of early liver fibrosis in the context of NAFLD compared to conventional native T1.

Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19

PURPOSE

 The prevalent coronavirus disease 2019 (COVID-19) pandemic has spread throughout the world and is considered a serious threat to global health. The prognostic role of thoracic lymphadenopathy in COVID-19 is unclear. The aim of the present meta-analysis was to analyze the prognostic role of thoracic lymphadenopathy for the prediction of 30-day mortality in patients with COVID-19.

MATERIALS AND METHODS

 The MEDLINE library, Cochrane, and SCOPUS databases were screened for associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 21 studies were included in the present analysis. The quality of the included studies was assessed by the Newcastle-Ottawa Scale. The meta-analysis was performed using RevMan 5.3. Heterogeneity was calculated by means of the inconsistency index I2. DerSimonian and Laird random-effect models with inverse variance weights were performed without any further correction.

RESULTS

 The included studies comprised 4621 patients. The prevalence of thoracic lymphadenopathy varied between 1 % and 73.4 %. The pooled prevalence was 16.7 %, 95 % CI = (15.6 %; 17.8 %). The hospital mortality was higher in patients with thoracic lymphadenopathy (34.7 %) than in patients without (20.0 %). The pooled odds ratio for the influence of thoracic lymphadenopathy on mortality was 2.13 (95 % CI = [1.80-2.52], p < 0.001).

CONCLUSION

 The prevalence of thoracic lymphadenopathy in COVID-19 is 16.7 %. The presence of thoracic lymphadenopathy is associated with an approximately twofold increase in the risk for hospital mortality in COVID-19.

KEY POINTS

  · The prevalence of lymphadenopathy in COVID-19 is 16.7 %.. · Patients with lymphadenopathy in COVID-19 have a higher risk of mortality during hospitalization.. · Lymphadenopathy nearly doubles mortality and plays an important prognostic role..

CITATION FORMAT

· Bucher AM, Sieren M, Meinel F et al. Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2293-8132.

Comparison of Left Ventricular Function Derived from Subject-Specific Inverse Finite Element Modeling Based on 3D ECHO and Magnetic Resonance Images

Three-dimensional echocardiography (3D ECHO) and magnetic resonance (MR) imaging are frequently used in patients and animals to evaluate heart functions. Inverse finite element (FE) modeling is increasingly applied to MR images to quantify left ventricular (LV) function and estimate myocardial contractility and other cardiac biomarkers. It remains unclear, however, as to whether myocardial contractility derived from the inverse FE model based on 3D ECHO images is comparable to that derived from MR images. To address this issue, we developed a subject-specific inverse FE model based on 3D ECHO and MR images acquired from seven healthy swine models to investigate if there are differences in myocardial contractility and LV geometrical features derived using these two imaging modalities. We showed that end-systolic and end-diastolic volumes derived from 3D ECHO images are comparable to those derived from MR images (R2=0.805 and 0.969, respectively). As a result, ejection fraction from 3D ECHO and MR images are linearly correlated (R2=0.977) with the limit of agreement (LOA) ranging from -17.95% to 45.89%. Using an inverse FE modeling to fit pressure and volume waveforms in subject-specific LV geometry reconstructed from 3D ECHO and MR images, we found that myocardial contractility derived from these two imaging modalities are linearly correlated with an R2 value of 0.989, a gradient of 0.895, and LOA ranging from -6.11% to 36.66%. This finding supports using 3D ECHO images in image-based inverse FE modeling to estimate myocardial contractility.

Multimodal abnormalities of brain structures in adolescents and young adults with major depressive disorder: An activation likelihood estimation meta-analysis

BACKGROUND

Major depressive disorder (MDD) in adolescents and young adults contributes significantly to global morbidity, with inconsistent findings on brain structural changes from structural magnetic resonance imaging studies. Activation likelihood estimation (ALE) offers a method to synthesize these diverse findings and identify consistent brain anomalies.

AIM

To identify consistent brain structural changes in adolescents and young adults with MDD using ALE meta-analysis.

METHODS

We performed a comprehensive literature search in PubMed, Web of Science, Embase, and Chinese National Knowledge Infrastructure databases for neuroimaging studies on MDD among adolescents and young adults published up to November 19, 2023. Two independent researchers performed the study selection, quality assessment, and data extraction. The ALE technique was employed to synthesize findings on localized brain function anomalies in MDD patients, which was supplemented by sensitivity analyses.

RESULTS

Twenty-two studies comprising fourteen diffusion tensor imaging (DTI) studies and eight voxel-based morphometry (VBM) studies, and involving 451 MDD patients and 465 healthy controls (HCs) for DTI and 664 MDD patients and 946 HCs for VBM, were included. DTI-based ALE demonstrated significant reductions in fractional anisotropy (FA) values in the right caudate head, right insula, and right lentiform nucleus putamen in adolescents and young adults with MDD compared to HCs, with no regions exhibiting increased FA values. VBM-based ALE did not demonstrate significant alterations in gray matter volume. Sensitivity analyses highlighted consistent findings in the right caudate head (11 of 14 analyses), right insula (10 of 14 analyses), and right lentiform nucleus putamen (11 of 14 analyses).

CONCLUSION

Structural alterations in the right caudate head, right insula, and right lentiform nucleus putamen in young MDD patients may contribute to its recurrent nature, offering insights for targeted therapies.

Effects of perioperative hydrogen inhalation on brain edema and prognosis in patients with glioma: a single-center, randomized controlled study

Introduction

Brain edema is a life-threatening complication that occurs after glioma surgery. There are no noninvasive and specific treatment methods for brain edema. Hydrogen is an anti-inflammatory and antioxidant gas that has demonstrated therapeutic and preventative effects on several diseases, particularly in the nervous system. This study aimed to determine the therapeutic effects of hydrogen administration on brain edema following glioma surgery and elucidate its mechanism.

Methods

A single-center, randomized controlled clinical trial of hydrogen inhalation was conducted (China Clinical Trial Registry [ChiCTR-2300074362]). Participants in hydrogen (H) group that inhaled hydrogen experienced quicker alleviation of postoperative brain edema compared with participants in control (C) group that inhaled oxygen.

Results

The volume of brain edema before discharge was significantly lower in the H group (p < 0.05). Additionally, the regression rate of brain edema was higher in the H group than in the C group, which was statistically significant (p < 0.05). Furthermore, 3 days after surgery, the H group had longer total sleep duration, improved sleep efficiency, shorter sleep latency, and lower numerical rating scale (NRS) scores (p < 0.05).

Discussion

In conclusion, hydrogen/oxygen inhalation effectively reduced postoperative brain edema in glioma patients. Further research is necessary to understand the underlying mechanisms of hydrogen's therapeutic effects. Hydrogen is expected to become a new target for future adjuvant therapy for brain edema.

Biomechanical effects of posterior lumbar interbody fusion with vertical placement of pedicle screws compared to traditional placement

BACKGROUND

The pedicle screw technique is widely employed for vertebral body fixation in the treatment of spinal disorders. However, traditional screw placement methods require the dissection of paraspinal muscles and the insertion of pedicle screws at specific transverse section angles (TSA). Larger TSA angles require more force to pull the muscle tissue, which can increase the risk of surgical trauma and ischemic injury to the lumbar muscles.

AIM

To study the feasibility of zero-degree TSA vertical pedicle screw technique in the lumbosacral segment.

METHODS

Finite element models of vertebral bodies and pedicle screw-rod systems were established for the L4-S1 spinal segments. A standard axial load of 500 N and a rotational torque of 10 N/m were applied. Simulated screw pull-out experiment was conducted to observe pedicle screw resistance to pull-out, maximum stress, load-displacement ratio, maximum stress in vertebral bodies, load-displacement ratio in vertebral bodies, and the stress distribution in pedicle screws and vertebral bodies. Differences between the 0-degree and 17-degree TSA were compared.

RESULTS

At 0-degree TSA, the screw pull-out force decreased by 11.35% compared to that at 17-degree TSA (P < 0.05). At 0-degree and 17-degree TSA, the stress range in the screw-rod system was 335.1-657.5 MPa and 242.8-648.5 MPa, separately, which were below the fracture threshold for the screw-rod system (924 MPa). At 0-degree and 17-degree TSA, the stress range in the vertebral bodies was 68.45-78.91 MPa and 39.08-72.73 MPa, separately, which were below the typical bone yield stress range for vertebral bodies (110-125 MPa). At 0-degree TSA, the load-displacement ratio for the vertebral bodies and pedicle screws was slightly lower compared to that at 17-degree TSA, indicating slightly lower stability (P < 0.05).

CONCLUSION

The safety and stability of 0-degree TSA are slightly lower, but the risks of screw-rod system fracture, vertebral body fracture, and rupture are within acceptable limits.

Post-discharge spirometry evaluation in patients recovering from moderate-to-critical COVID-19: a cross-sectional study

Understanding the prevalence of abnormal lung function and its associated factors among patients recovering from COVID-19 is crucial for enhancing post-COVID care strategies. This study primarily aimed to determine the prevalence and types of spirometry abnormalities among post-COVID-19 patients in Malaysia, with a secondary objective of identifying its associated factors. Conducted at the COVID-19 Research Clinic, Faculty of Medicine, University Technology MARA, from March 2021 to December 2022, this study included patients at least three months post-discharge from hospitals following moderate-to-critical COVID-19. Of 408 patients studied, abnormal spirometry was found in 46.8%, with 28.4% exhibiting a restrictive pattern, 17.4% showing preserved ratio impaired spirometry (PRISm), and 1.0% displaying an obstructive pattern. Factors independently associated with abnormal spirometry included consolidation on chest X-ray (OR 8.1, 95% CI 1.75-37.42, p = 0.008), underlying cardiovascular disease (OR 3.5, 95% CI 1.19-10.47, p = 0.023), ground-glass opacity on chest X-ray (OR 2.6, 95% CI 1.52-4.30, p < 0.001), and oxygen desaturation during the 6-min walk test (OR 1.9, 95% CI 1.20-3.06, p = 0.007). This study highlights that patients recovering from moderate-to-critical COVID-19 often exhibit abnormal spirometry, notably a restrictive pattern and PRISm. Routine spirometry screening for high-risk patients is recommended.

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Objective

Radio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when the tissue and probe are fixed. We previously proposed the innovative RFTSDP method in which the RF data are recorded while stimulating the tissue. Applying stimulation can unveil the mechanical characteristics of the tissue in RF echo.

Materials and methods

In this study, an apparatus was developed to induce vibrations at different frequencies to the medium. Data were collected from four PVA phantoms simulating the nonlinear behaviors of healthy, fibroadenoma, cyst, and cancerous breast tissues. Raw focused, raw, and beamformed ultrafast data were collected under conditions of no stimulation, constant force, and various vibrational stimulations using the Supersonic Imagine Aixplorer clinical/research ultrasound imaging system. Time domain (TD), spectral, and nonlinear features were extracted from each RF TS. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms were employed for classification.

Results

The optimal outcome was achieved using the SVM classifier considering 19 features extracted from beamformed ultrafast data recorded while applying vibration at the frequency of 65 Hz. The classification accuracy, specificity, and precision were 98.44 ± 0.20 %, 99.49 ± 0.01 %, and 98.53 ± 0.04 %, respectively. Applying RFTSDP, a notable 24.45 % improvement in accuracy was observed compared to the case of fixed probe assessing the recorded raw focused data.

Conclusions

External vibration at an appropriate frequency, as applied in RFTSDP, incorporates beneficial information about the medium and its dynamic characteristics into the RF TS, which can improve tissue characterization.

The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and prevalence of urinary stones in US adults: a cross-sectional NHANES study

BACKGROUND

This study examines the association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and urinary stones in American adults.

METHODS

We conducted a cross-sectional study utilizing the 2007-2018 National Health and Nutrition Examination Survey (NHANES) data set. The prevalence of urinary stones was determined based on patient-reported experiences of renal colic. We converted NHHR to natural logarithm (ln-NHHR) to align it better with our statistical analyses. Our analysis methods included weighted multivariate logistic regression, generalized additive model (GAM), and application of smoothed curves to better elucidate the association between ln-NHHR and the prevalence of urinary stones. In addition, we conducted subgroup analyses and employed multiple imputation for sensitivity analyses.

RESULTS

This study involved a total of 30,903 participants, with a 9.97% prevalence of urinary stones and reported colic experience. Elevated ln-NHHR levels were linked with a higher likelihood of urinary stones (OR = 1.20, 95% CI 1.07-1.35). Smooth curve fitting revealed an inverted U-shaped relationship, pinpointing a significant increase in urinary stone risk at ln-NHHR levels below 1.43 (OR = 1.40, 95% CI 1.19-1.64, p < 0.001). Notably, this correlation was stronger among Non-Hispanic Whites and those married or living with a partner. Multiple imputation analyses strengthened the confidence in our results.

CONCLUSIONS

Our findings suggest a reverse U-shaped association between urinary stone occurrence and NHHR level, with a positive association at ln-NHHR < 1.43. This correlation was more pronounced in the Non-Hispanic White population and among those married or living with a partner.

Design and experimental validation of a metamaterial-based sensor for microwave imaging in breast, lung, and brain cancer detection

This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial (MTM) layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC's permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI.

Visceral Adiposity and Its Impact on Nephrolithiasis: A Narrative Review

Kidney stones represent a serious medical problem, resulting from several factors such as diet, genetics, and certain medical conditions. Visceral adipose tissue has been shown in recent research to play a significant role in kidney stone formation, making it a more precise indicator than traditional obesity indicators such as body mass index. The main aim of this review is to summarize studies on visceral obesity as a predictive marker for nephrolithiasis and to highlight new mechanistic pathways such as adipokine-mediated inflammation and its impact on kidney stone formation. This review emphasizes the importance of considering visceral fat in the prevention and management of kidney stones, suggesting that targeted strategies to reduce visceral fat could decrease the incidence of kidney stones and their management costs. Further prospective studies are needed to validate these findings and propose preventive strategies based on visceral adiposity assessments.

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

Radiomics incorporating deep features for predicting Parkinson's disease in 123I-Ioflupane SPECT

PURPOSE

123I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson's disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using 123I-Ioflupane SPECT images at year 0.

METHODS

In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and 123I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models.

RESULTS

For the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models.

CONCLUSION

The combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using 123I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features.

Investigating the Use of Serious Games for Cancer Control Among Children and Adolescents: Scoping Review

BACKGROUND

Effective health care services that meet the diverse needs of children and adolescents with cancer are required to alleviate their physical, psychological, and social challenges and improve their quality of life. Previous studies showed that serious games help promote people's health. However, the potential for serious games to be used for successful cancer control for children and adolescents has received less attention.

OBJECTIVE

This scoping review aimed to map the use of serious games in cancer prevention and cancer care for children and adolescents, and provide future directions for serious games' development and implementation within the context of cancer control for children and adolescents.

METHODS

This study followed a combination of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and the JBI (Joanna Briggs Institute) framework for the conduct of scoping reviews. PubMed, CINAHL Plus Full Text, Scopus, Web of Science Core Collection, and American Psychological Association (APA) PsycINFO databases were used for the search.

RESULTS

From the initial 2750 search results, 63 papers were included in the review, with 28 quantitative, 14 qualitative, and 21 mixed method studies. Most of the studies were cancer care serious game papers (55/63, 87%) and a small number of studies were cancer prevention serious game papers (8/63, 13%). The majority of the included studies were published between 2019 and 2023 (cancer prevention: 5/8, 63%; cancer care: 35/55, 64%). The majority of the studies were conducted in Europe (cancer prevention: 3/8, 38%; cancer care: 24/55, 44%) and North America (cancer prevention: 4/8, 50%; cancer care: 17/55, 31%). Adolescents were the most represented age group in the studies' participants (cancer prevention: 8/8, 100%; cancer care: 46/55, 84%). All (8/8, 100%) cancer prevention serious game papers included healthy people as participants, and 45 out of 55 (82%) cancer care serious game papers included patients with cancer. The majority of cancer prevention serious game papers addressed game preference as a target outcome (4/8, 50%). The majority of cancer care serious game papers addressed symptom management as a target outcome (28/55, 51%). Of the cancer care studies examining serious games for symptom management, the majority of the studies were conducted to treat psychological (13/55, 24%) and physical symptoms (10/55, 18%).

CONCLUSIONS

This review shows both the growth of interest in the use of serious games for cancer control among children and adolescents and the potential for bias in the relevant literature. The diverse characteristics of the included papers suggest that serious games can be used in various ways for cancer control among children and adolescents while highlighting the need to develop and implement serious games in underrepresented areas.

A rotating beam-blocker method for cone beam CT scatter correction

BACKGROUND

Cone beam CT (CBCT) is widely utilized in clinics. However, the scatter artifact degrades the CBCT image quality, hampering the expansion of CBCT applications. Recently, beam-blocker methods have been used for CBCT scatter correction and proved their high cost-effectiveness.

PURPOSE

A rotating beam-blocker (RBB) method for CBCT scatter correction was proposed to complete scatter correction and image reconstruction within a single scan in both full- and half-fan scan scenarios.

METHODS

The RBB consisted of two open regions and two blocked regions, and was designed as a centrosymmetric structure. The open and blocked projections could be alternatively obtained within one single rotation. The open projections were corrected with the scatter signal calculated from the blocked projections, and then used to reconstruct the 3D image via the Feldkamp-Davis-Kress algorithm. The performance of the RBB method was evaluated on head and pelvis phantoms in scenarios with and without a bowtie filter. The images obtained from nine repeated scans in each scenario were used to calculate the evaluation metrics including the CT number error, spatial nonuniformity (SNU) and contrast-to-noise ratio (CNR).

RESULTS

For the head phantom, the CT number error was decreased to <5 after scatter correction from >200 HU before correction when scanned without a bowtie filter, and to <4 from >160 HU when scanned with a full bowtie filter. For the pelvis phantom, the CT number error was reduced to <12 after scatter correction from >250 HU before correction when scanned without a bowtie filter, and to <10 from >190 HU when scanned with a half bowtie filter. After scatter correction, the uniformity and contrast were both improved, resulting in an SNU of >79% decrease and CNR of >2 times increase, respectively.

CONCLUSIONS

High-quality CBCT images could be obtained in a single scan after using the proposed RBB method for scatter correction, enabling more accurate image guidance for surgery and radiation therapy applications. With almost no time delay between the successive open and blocked projections, the RBB method could eliminate the motion-induced anatomical mismatches between the corresponding open and blocked projections and could find particular usefulness in thoracic and abdominal imaging.

Hepatic artery pseudoaneurysm: three case reports and literature review

Laparoscopic surgery is extensively applied in the treatment of hepatobiliary diseases. Hepatic artery pseudoaneurysm (HAP) is a rare complication following hepatic biliary surgery through laparoscopy. The clinical manifestations of HAP are diverse and can be fatal. Given its severity, rapid assessment and management are crucial to ensuring a good prognosis. Here, we report three cases of HAP; two underwent laparoscopic surgery due to cholelithiasis, and another caused by trauma. The first case exhibited a pseudoaneurysm involving the distal portion of the right hepatic artery main trunk. The second patient had a pseudoaneurysm at the bifurcation of the left and right hepatic arteries. The third case involved a patient with a pseudoaneurysm involving a branch of the right hepatic artery. The main clinical manifestations of all three cases were bleeding from the biliary tract (the first two cases showed postoperative bleeding in the T-tube, while the third case exhibited gastrointestinal bleeding). The final diagnosis was obtained through digital subtraction angiography. The three patients underwent successful transcatheter arterial embolization operation and a follow-up revealed they were disease-free and alive. This article aims to highlight a rare complication of laparoscopic hepatobiliary surgery and share our experience in early diagnosis and treatment of HAP.

Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model

BACKGROUND

Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field.

OBJECTIVE

Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances.

METHODS

In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features.

RESULTS

The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness.

CONCLUSIONS

The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.