Current Medical Imaging - Current Issue

Larger sample volume can be obtained in one needle pass using an aspiration-type semi-automatic cutting biopsy needle (STARCUT^® aspiration-type needle; TSK Laboratory, Tochigi, Japan) in comparison to the conventional semi-automatic cutting biopsy needle.

To evaluate and compare the safety and effectiveness of aspiration-type semi-automatic cutting biopsy needles and non-aspiration-type biopsy needles when performing computed tomography (CT)-guided core needle biopsies (CNBs).

A total of 106 patients underwent CT-guided CNB for chest lesions between June 2013 and March 2020 at our hospital. Non-aspiration-type cutting biopsy needles were used in 47 of these patients, while aspiration-type needles were used in the remaining 59 patients. All needles used were 18- or 20-gauge biopsy needles. Parameters, like forced expiratory volume in 1-second percent (FEV1.0%), the maximum size of the target lesion, puncture pathway distance in the lung, number of needle passes, procedure time, diagnostic accuracy, and incidence of complications, were measured. Comparisons were made between the needle-type groups.

No significant difference was observed in terms of diagnostic accuracy. However, the procedure time was shorter and a lesser number of needle passes were required with the aspiration-type cutting biopsy needle compared to the non-aspiration-type needle. Pneumothorax and pulmonary hemorrhage were the complications encountered, however, their incidence was not significantly different between the two types of needles.

The aspiration-type semi-automatic cutting biopsy needle had similar diagnostic accuracy as the non-aspiration-type biopsy needle, with added advantages of a lesser number of needle passes and shorter procedure time.

Chronic kidney disease (CKD) is a long-term risk to one’s health that can result in kidney failure. CKD is one of today's most serious diseases, and early detection can aid in proper treatment. Machine learning techniques have proven to be reliable in the early medical diagnosis.

The paper aims to perform CKD prediction using machine learning classification approaches. The dataset used for the present study for detecting CKD was obtained from the machine learning repository at the University of California, Irvine (UCI).

In this study, twelve machine learning-based classification algorithms with full features were used. Since the CKD dataset had a class imbalance issue, the Synthetic Minority Over-Sampling technique (SMOTE) was used to alleviate the problem of class imbalance and review the performance based on machine learning classification models using the K fold cross-validation technique. The proposed work compares the results of twelve classifiers with and without the SMOTE technique, and then the top three classifiers with the highest accuracy, Support Vector Machine, Random Forest, and Adaptive Boosting classification algorithms were selected to use the ensemble technique to improve performance.

The accuracy achieved using a stacking classifier as an ensemble technique with cross-validation is 99.5%.

The study provides an ensemble learning approach in which the top three best-performing classifiers in terms of cross-validation results are stacked in an ensemble model after balancing the dataset using SMOTE. This proposed technique could be applied to other diseases in the future, making disease detection less intrusive and cost-effective.

Brain tumour detection and classification require trained radiologists for efficient diagnosis. The proposed work aims to build a Computer Aided Diagnosis (CAD) tool to automate brain tumour detection using Machine Learning (ML) and Deep Learning (DL) techniques.

Magnetic Resonance Image (MRI) collected from the publicly available Kaggle dataset is used for brain tumour detection and classification. Deep features extracted from the global pooling layer of Pretrained Resnet18 network are classified using 3 different ML Classifiers, such as Support vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT). The above classifiers are further hyperparameter optimised using Bayesian Algorithm (BA) to enhance the performance. Fusion of features extracted from shallow and deep layers of the pretrained Resnet18 network followed by BA-optimised ML classifiers is further used to enhance the detection and classification performance. The confusion matrix derived from the classifier model is used to evaluate the system's performance. Evaluation metrics, such as accuracy, sensitivity, specificity, precision, F1 score, Balance Classification Rate (BCR), Mathews Correlation Coefficient (MCC) and Kappa Coefficient (Kp), are calculated.

Maximum accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC, and Kp of 99.11%, 98.99%, 99.22%, 99.09%, 99.09%, 99.10%, 98.21%, 98.21%, respectively, were obtained for detection using fusion of shallow and deep features of Resnet18 pretrained network classified by BA optimized SVM classifier. Feature fusion performs better for classification task with accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC and Kp of 97.31%, 97.30%, 98.65%, 97.37%, 97.34%, 97.97%, 95.99%, 93.95%, respectively.

The proposed brain tumour detection and classification framework using deep feature extraction from Resnet 18 pretrained network in conjunction with feature fusion and optimised ML classifiers can improve the system performance. Henceforth, the proposed work can be used as an assistive tool to aid the radiologist in automated brain tumour analysis and treatment.

In recent years, various deep learning algorithms have exhibited remarkable performance in various data-rich applications, like health care, medical imaging, as well as in computer vision. COVID-19, which is a rapidly spreading virus, has affected people of all ages both socially and economically. Early detection of this virus is therefore important in order to prevent its further spread.

COVID-19 crisis has also galvanized researchers to adopt various machine learning as well as deep learning techniques in order to combat the pandemic. Lung images can be used in the diagnosis of COVID-19.

In this paper, we have analysed the COVID-19 chest CT image classification efficiency using multilayer perceptron with different imaging filters, like edge histogram filter, colour histogram equalization filter, color-layout filter, and Garbo filter in the WEKA environment.

The performance of CT image classification has also been compared comprehensively with the deep learning classifier Dl4jMlp. It was observed that the multilayer perceptron with edge histogram filter outperformed other classifiers compared in this paper with 89.6% of correctly classified instances.

Hepatic arterial infusion chemotherapy (HAIC) has been popular for treating unresectable hepatocellular carcinoma (HCC). However, there are few reports comparing the transradial approach (TRA) and transfemoral approach (TFA) in HAIC.

This study aimed to compare the duration of the hepatic artery catheterization, fluoroscopy time (FT), radiation exposure, safety, and quality of life associated with the procedure in patients undergoing HAIC via TRA and TFA.

This prospective, single-center, randomized, controlled study included 120 patients with unresectable HCC undergoing HAIC procedures. Patients were randomly assigned to group A (n = 60, TRA-HAIC) or group B (n = 60, TFA-HAIC). The hepatic artery catheterization time, FT, entrance surface dose (ESD), dose area product (DAP), procedure-related complications, and quality of life associated with the procedure were assessed between the two groups. Independent-sample t-test and analysis of variance (ANOVA) were used to assess differences. Statistical significance was set at P < 0.05.

HAIC procedures were successfully performed in both groups. The hepatic artery catheterization time (19.35 ± 5.84 vs. 18.93 ± 5.62 minutes, P = 0.837), FT (2.35 ± 2.23 vs. 2.25 ± 2.16 minutes, P = 0.901), ESD (259.32 ± 167.46 vs. 250.56 ± 170.58 mGy, P = 0.449), and DAP (125.37 ± 60.65 vs. 120.56 ± 64.33 Gy.cm³, P = 0.566) were comparable between the two groups. The incidence of artery occlusion (10.0% vs. 0%, P < 0.001) in the TRA group was significantly higher than that in the TFA group. TRA was associated with a statistically significant (P < 0.05) improvement in the quality of life.

TRA to HAIC was associated with greater improvement in the quality of life associated with the procedure compared with TFA. Both approaches to HAIC had similar efficiency, safety, radiation exposure, and procedure duration.

COVID-19 is a global pandemic. Currently, the predominant strain is SARS-CoV-2 Omicron subvariant BA.2 in many countries. Understanding its infection characteristics can facilitate clinical management.

This study aimed to characterize the clinical, laboratory, and high-resolution computed tomography (HRCT) findings in patients with mild or moderate infection from SARS-CoV-2 Omicron subvariant BA.2.

We performed a retrospective study on patients infected with SARS-CoV-2 Omicron subvariant BA.2 between April 4^th and April 17^th, 2022. The clinical characteristics, laboratory features, and HRCT images were reviewed.

A total of 805 patients were included (411 males and 394 females, median age 33 years old). The infection was mild, moderate, severe, and asymptomatic in 490 (60.9%), 37 (4.6%), 0 (0.0%), and 278 (34.5%) patients, respectively. Notably, 186 (23.1%), 96 (11.9%), 265 (32.9%), 11 (3.4%), 7 (0.9%), and 398 (49.4%) patients had fever, cough, throat discomfort, stuffy or runny nose, fatigue, and no complaint, respectively. Furthermore, 162 (20.1%), 332 (41.2%), and 289 (35.9%) patients had decreased white blood cell counts, reduced lymphocytes, and elevated C-reactive protein levels, respectively. HRCT revealed pneumonia in 53 (6.6%) patients. The majority of the lung involvements were ground-glass opacity (50, 94.3%) mostly in the subpleural area. The grade of lung injury was mainly mild (90.6%). Short-term follow-ups showed that most patients with pneumonia recovered.

Most patients with mild or moderate infection from SARS-CoV-2 Omicron subvariant BA.2 were adults, with fever and upper respiratory symptoms as the main clinical presentations. Lower respiratory infection was mild, with ground-glass opacity in the subpleural area as the main finding.

This study aimed to explore the imaging features and risk factors of PCLs complicating AIP, and investigate its prognosis through continuous imaging follow-up.

Patients who were diagnosed with AIP from January 2014 to December 2020 in our hospital were recruited. We analyzed the CT and MRI features of PCLs complicating AIP, and investigated its prognosis through imaging follow-up. We also compared subjects with and without PCLs using clinical, laboratory, and imaging data; the related risk factors associated with PCLs were investigated in a multivariate logistic regression analysis.

In this group, 16 patients had PCLs and 86 did not. A total of 43 PCLs larger than 5mm were found in 15 patients. Among these PCLs, 35 showed homogeneous signal (density); one, bleeding; three, linear separation; and four, small focal low signal on T2WI. Eight patients with 23 PCLs appeared for the follow-up after steroid treatment. Short-term follow-up showed that 11 PCLs disappeared, nine reduced, one unchanged and two enlarged. Of the 12 PCLs that did not disappear, 10 PCLs disappeared at long-term follow-up, except for two reduced PCLs were not re-examined. Logistic regression analysis showed that drinking history was an independent risk factor, age ≥ 65 years was an independent protective factor for PCLs complicating AIP.

The imaging features of PCLs complicating AIP are various, which can be single or multiple, most of them are homogeneous, and some lesions may be accompanied by hemorrhage, separation and necrosis. Age ≥ 65 years and avoiding drinking may help to reduce the occurrence of these lesions.

Xanthogranulomatous endometritis (XGE) is a rare inflammatory disease, which can easily misdiagnose as cancer in imaging diagnosis. Diagnosis of XGE relies on histopathological examination and immunohistochemistry.

In this study, a case of a 72-year-old female with XGE and elevated CA125 is presented, which was misdiagnosed as endometrial cancer in transvaginal ultrasonography and ovarian cystadenocarcinoma in CT. However, the features of XGE on the contrast-enhanced ultrasound (CEUS) were different from that of endometrial cancer. The patient finally underwent laparoscopic hysterectomy and bilateral adnexectomy.

The histopathological examination and immunohistochemistry suggested xanthogranulomatous endometritis (histiocytic endometritis). This case report manifests that CEUS may be a new noninvasive diagnostic method for XGE, which may reduce extensive tissue sampling and unnecessary hysterectomies for patients.

It has been reported in the literature that the complication rate of percutaneous compression plate (PCCP) is the lowest among the new internal fixators for the treatment of femoral neck fracture (FNS). However, no multicenter studies of PCCP for FNS have been reported. This study aimed to evaluate the med-term effectiveness of PCCP in a multicenter mainly through radiology.

265 patients with FNF treated with PCCP fixation in our five hospitals between January 2011 and December 2020 were retrospectively analyzed. 140 men and 125 women; aged 19–79 (mean 51.6) years. The follow-up time was 2-5 years (mean 3.1). Radiological evaluation of the therapeutic effect was the main outcome, and the function was the secondary outcome.

One case of screw cutting out, 3 cases of screw back out, 25 cases of neck shortening, 2 cases of nonunion, 8 cases of delayed healing, and 29 cases of avascular necrosis (AVN). Bivariate correlation showed that shortening healing was correlated with age, Singh index, and Garden alignment index, poor healing was correlated with garden alignment index, and AVN was correlated with Pauwels and Garden classifications and operation timing. Further pairwise comparison analysis showed that age of > 65 and Singh index IV were dangerous factors for neck shortening, and the operation timing > 3 days, Pauwels II and III, and Garden III and IV were dangerous factors for AVN. The excellent and good rate of function in 198 patients who were readmitted for internal fixator removal or other surgery was 90.9%.

PCCP for FNS has satisfactory med-term efficacy with a low complication rate. The main complication is AVN, which is prone to occur in patients with displaced Pauwels II or III FNF and operation timing > 3 days. Another main complication is shortening healing, which is prone to occur in patients with an age of > 65 and Singh index IV.

Mediastinal and hilar lesions may be benign or malignant. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is increasingly used for the diagnosis of these lesions as it is both minimally invasive and safe.

To investigate the clinical efficacy of EBUS-TBNA in the diagnosis and differential diagnosis of mediastinal and hilar lesions.

A retrospective observational study was undertaken to investigate patients diagnosed with mediastinal and hilar lymphadenopathy based on imaging at our hospital from 2020 to 2021. After evaluation, EBUS TBNA was used and data including the puncture site, postoperative pathology, and complications were recorded.

Data from 137 patients were included in the study, of which 135 underwent successful EBUS TBNA. A total of 149 lymph node punctures were performed, of which 90 punctures identified malignant lesions. The most common malignancies were small-cell lung carcinoma, adenocarcinoma, and squamous cell carcinoma. Forty-one benign lesions were identified, resulting from sarcoidosis, tuberculosis, and reactive lymphadenitis, amongst others. Follow-up findings showed that 4 cases were malignant tumors, with 1 case of pulmonary tuberculosis and 1 case of sarcoidosis). Four specimens where lymph node puncture was insufficient were subsequently confirmed by other means. The sensitivity of EBUS TBNA for malignant lesions, tuberculosis and sarcoidosis in mediastinal and hilar lesions was 94.7%, 71.4%, and 93.3%, respectively. Similarly, the negative predictive values (NPV) were 88.9%, 98.5%, and 99.2%, and the accuracy was 96.3%, 98.5%, and 99.3%.

EBUS TBNA is an effective and feasible approach for the diagnosis of mediastinal and hilar lesions that is minimally invasive and safe.

Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used machine learning algorithm for visual learning and image identification is CNN.

In this article, the convolutional neural network (CNN) technique is used. Augmentation of data and processing of images is used to classify scan imagery of brain MRI as malignant or benign. The performance of the proposed CNN model is compared with pre-trained models: VGG-16, ResNet-50, and Inceptionv3 using the technique which is transfer learning.

Even though the experiment was conducted on a relatively limited dataset, the experimental results reveal that the suggested scratched CNN model accuracy achieved is 94%, VGG-16 was extremely effective and had a very low complexity rate with an accuracy of 90%, whereas ResNet- 50 reached 86% and Inception v3 obtained 64% accuracy.

When compared to previous pre-trained models, the suggested model consumes significantly less processing resources and achieves significantly higher accuracy outcomes and a reduction in losses.

We aimed to evaluate the effectiveness of high-dose and low-dose use of radioactive material in intraoperative gamma probe application methods in patients diagnosed with primary hyperparathyroidism and planned for surgery.

47 patients with primary hyperparathyroidism underwent minimally-invasive parathyroid surgery (MIS) after preoperative imaging studies consisting of ultrasonography (USG) and sestamibi parathyroid scintigraphy (SPS) showed a possible primary parathyroid lesion (PPL). All patients received Tc-99 sestamibi on day-of-surgery imaging (DOSI) to help with the localization of a primary parathyroid lesion (PPL) via both DOSI and intraoperative gamma probe (IGP). Patients in Group I were administered 20-25 mCi Tc-99m sestamibi (methoxy isobutyl isonitrile) and images were obtained at the 20th and 120th minutes. Patients in Group II were administered 8-10 mCi doses and images were obtained at the 20th and 40th minutes. Two nuclear medicine specialists independently evaluated the images. Lesions in the localizations determined by DOSI and IGP were compared with the histopathological results of these lesions.

47 patients, 35 females, and 12 males were included in the study. The mean age of 28 patients in the first group given the high dose was 54 (41-60), and the mean age was 48 (42-57) in the second group given the low dose (p=0.011). In the group given low-dose radioactive material during intraoperative gamma probe application, the observer's sensitivity, specificity, positive, and negative predictive values for finding pathology were 61.1, 100, 100, and 12.5, respectively. In the group given high-dose radioactive material, the same values were 90.9, 33.3, 50, and 83.3, respectively. While the success of MIS increases with the use of DOSI and IGP in large lesions, the success decreases with the prolongation of the accumulation time of the given dose.

In the intraoperative gamma probe technique used in primary hyperparathyroidism patients, the method used with low-dose radioactive material has lower sensitivity but higher specificity in estimating the post-operative pathology compared to the high-dose technique.

The etiology of rotator cuff tears (RCTs) have been investigated for years and many underlying causes have been identified. Shoulder joint morphology is one of the extrinsic causes of RCTs.

Morphometric measurements on MRI sections determined which parameters are an important indicator of RCT in patients with shoulder pain. The aim of this study was to determine the risk factors in the etiology of RCTs by evaluating the shoulder joint morphology with the help of previously defined radiological parameters.

Between January 2019-December 2020, 408 patients (40-70 years old) who underwent shoulder MRI and met the criteria were included in the study. There were 202 patients in the RCT group and 206 patients in the control group. Acromion type, acromial index (AI), critical shoulder angle (CSA), acromiohumeral distance (AHD), lateral acromial angle (LAA), acromial angulation (AA), acromion-greater tuberosity impingement index (ATI), and glenoid version angle (GVA) were measured from the MRI images of the patients.

AI (0.64 vs. 0.60, p = 0.003) CSA (35.3° vs. 32.4°, p = 0.004), ATI (0.91 vs. 0.83, P < 0.001), and AA (13.6° vs. 11.9°, p = 0.011) values were higher in the RCT group than in the control group and the difference was significant. AHD (8.1 mm vs. 9.9 mm, P < 0.001), LAA (77.2° vs. 80.9°, p = 0.004) and GVA (-3.9° vs. -2.5°, P < 0.001) values were lower in the RCT group than in the control group, and again the difference was significant. According to the receiver operating characteristic curve analysis, the cutoff values were 0.623 for AI and 0.860 for ATI.

Acromion type, AI, CSA, AHD, LAA, AA, ATI, and GVA are suitable radiological parameters to evaluate shoulder joint morphology. High AI, CSA, AA, ATI, GVA and low AHD and LAA are risk factors for RCT.

The objective of this study was to describe the CT and MRI features of sclerosing angiomatoid nodular transformation (SANT) of the spleen with pathologic correlation.

Ten patients with surgically resected and pathologically confirmed SANTs were included. Clinical history was reviewed, and gross pathologic, histologic, and immunohistochemical findings were recorded. CT and MRI examinations were evaluated by two radiologists.

Patients included seven men and three women, with a mean age of 42.9±16.7 years. Pathologic features of SANTs involved multiple angiomatous nodules in a radiating pattern with a central stellate fibrous scar and evidence of hemosiderin deposition. 9 cases showed a lobulated demarcated margin, 8 cases a slight hypoattenuating, 1 isoattenuating, and 1 case with two lesions demonstrated a slight hyperattenuating margin, respectively. Multiple scattered punctate calcifications were involved in 2 cases. 5 cases manifested hypointensity on in-phase imaging, 1 iso-intensity, and 4 iso-hypointensity on out-of-phase imaging. Progressive and centripetal enhancement were exhibited in 10 cases, spoke-wheel pattern in 3 cases, and nodular enhancement in 4 cases, respectively. The central fibrous scar was identified in 8 cases during delayed enhancement.

Characteristics of SANTs on CT/MRI reflected the underlying pathology. Hypointensity on DWI and T2WI, and change of signal on T1 chemical-shift imaging were found to be due to hemosiderin deposition and fibrous tissue. Typical feature was a solitary, round, lobulated mass with a fibrous scar. Progressive and centripetal enhancement, spoke-wheel pattern, nodular enhancement, and delayed enhancement of central fibrous scar were observed.

The majority of the existing diagnostic reference levels (DRLs) that have been established for computed tomography (CT) are based on various anatomical locations, such as the head, chest, abdomen, etc. However, DRLs are initiated to improve radiation protection by conducting a comparison of similar examinations with similar objectives. The aim of this study was to explore the feasibility of establishing dose baselines based on common CT protocols for patients who underwent enhanced CT abdomen and pelvis exams.

Dose length product total (tDLPs), volumetric CT dose index (CTDIvol), size-specific dose estimate (SSDE), effective dose (E), and scan acquisition parameters for a total of 216 adult patients, who underwent an enhanced CT abdomen and pelvis exams over a one-year period, were obtained and retrospectively analyzed. Spearman coefficient and one-way ANOVA tests were used to check significant differences between dose metrics and the different CT protocols.

The data exhibited 9 different CT protocols to acquire an enhanced CT abdomen and pelvis exam at our institute. Out of these, 4 were found more common, i.e., CT protocols were acquired for a minimum of 10 cases. Triphasic liver demonstrated the highest mean and median tDLPs across all 4 CT protocols. Triphasic liver protocol registered the highest E followed by gastric sleeve protocol with a mean of 28.7 and 24.7 mSv, respectively. Significant differences (p < 0.0001) were found between the tDLPs of anatomical location and the CT protocol.

Evidently, wide variability exists across CT dose indices and patient dose metrics relying on anatomical-based dose baseline, i.e., DRLs. Patient dose optimizations require establishing dose baselines based on CT protocols rather than the anatomical location.

Pancreatic cancer is one of the most serious problems that has taken many lives worldwide. The diagnostic procedure using the traditional approaches was manual by visually analyzing the large volumes of the dataset, making it time-consuming and prone to subjective errors. Hence the need for the computer-aided diagnosis system (CADs) emerged that comprises the machine and deep learning approaches for denoising, segmentation and classification of pancreatic cancer.

There are different modalities used for the diagnosis of pancreatic cancer, such as Positron Emission Tomography/Computed Tomography (PET/CT), Magnetic Resonance Imaging (MRI), Multiparametric-MRI (Mp-MRI), Radiomics and Radio-genomics. Although these modalities gave remarkable results in diagnosis on the basis of different criteria. CT is the most commonly used modality that produces detailed and fine contrast images of internal organs of the body. However, it may also contain a certain amount of gaussian and rician noise that is necessary to be preprocessed before segmentation of the required region of interest (ROI) from the images and classification of cancer.

This paper analyzes different methodologies used for the complete diagnosis of pancreatic cancer, including the denoising, segmentation and classification, along with the challenges and future scope for the diagnosis of pancreatic cancer.

Various filters are used for denoising and image smoothening and filters as gaussian scale mixture process, non-local means, median filter, adaptive filter and average filter have been used more for better results.

In terms of segmentation, atlas based region-growing method proved to give better results as compared to the state of the art whereas, for the classification, deep learning approaches outperformed other methodologies to classify the images as cancerous and non- cancerous. These methodologies have proved that CAD systems have become a better solution to the ongoing research proposals for the detection of pancreatic cancer worldwide.

The aim of this study was to assess the value of 3.0T magnetic resonance (MR) Diffusion tensor imaging (DTI) in the diagnosis of lumbosacral nerve root compression.

The radiology reports, and clinical records of 34 patients with nerve root compression caused by lumbar disc herniation or bulging and 21 healthy volunteers who had undergone magnetic resonance imaging (MRI) and DTI scan were retrospectively reviewed. The differences in fractional anisotropy (FA) and apparent diffusion coefficient (ADC) between compressed and non-compressed nerve roots from patients and the normal nerve roots from healthy volunteers were compared. Meanwhile, the nerve root fiber bundles were observed and analyzed.

The average FA and ADC values of the compressed nerve roots were 0.254 ± 0.307 and 1.892 ± 0.346 10^-3mm²/s, respectively. The average FA and ADC values of the non-compressed nerve roots were 0.377 ± 0.659 and 1.353 ± 0.344 10^-3mm²/s, respectively. The FA value of compressed nerve roots was significantly lower than that of non-compressed nerve roots (P < 0.01). The ADC value of compressed nerve roots was significantly higher than that of non-compressed nerve roots. There were no significant differences between the left and right nerve roots of normal volunteers in FA and ADC values (P > 0.05). The nerve roots at different levels of L3-S1 had significantly different FA and ADC values (P < 0.01). Incomplete fiber bundles with extrusion deformation, displacement or partial defect were observed in the compressed nerve root fiber bundles. The real diagnosis of the clinical situation of the nerve can provide neuroscientists with an important computer tool to help them infer and understand the possible working mechanism from the experimental data of behavior and electrophysiology.

The compressed lumbosacral nerve roots can be accurately localized through 3.0T magnetic resonance DTI, which is instructive for accurate clinical diagnosis and preoperative localization.

To investigate the performance of a novel radiological-metabolic scoring (RM-S) system to predict mortality and intensive care unit (ICU) requirements among COVID-19 patients and to compare performance with the chest computed-tomography severity-scoring (C-CT-SS). The RM-S was created from scoring systems such as visual coronary-artery-calcification scoring (V-CAC-S), hepatic-steatosis scoring (HS-S) and pancreatic-steatosis scoring (PS-S).

Between May 2021 and January 2022, 397 patients with COVID-19 were included in this retrospective cohort study. All demographic, clinical and laboratory data and chest CT images of patients were retrospectively reviewed. RM-S, V-CAC-S, HS-S, PS-S and C-CT-SS scores were calculated, and their performance in predicting mortality and ICU requirement were evaluated by univariate and multivariable analyses.

A total of 32 (8.1%) patients died, and 77 (19.4%) patients required ICU admission. Mortality and ICU admission were both associated with older age (p < 0.001). Sex distribution was similar in the deceased vs. survivor and ICU vs. non-ICU comparisons (p = 0.974 and p = 0.626, respectively). Multiple logistic regression revealed that mortality was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe RM-S category (p = 0.010), while ICU requirement was independently associated with having a C-CT-SS score of ≥ 14 (p < 0.001) and severe V-CAC-S category (p = 0.010).

RM-S, C-CT-SS, and V-CAC-S are useful tools that can be used to predict patients with poor prognoses for COVID-19. Long-term prospective follow-up of patients with high RM-S scores can be useful for predicting long COVID.

To compare main lacrimal gland values through shear-wave elastography (SWE) in patients with low Schirmer value and unspecified Sjögren's syndrome (SS) with healthy controls.

Admitted to the ophthalmology department with Schirmer value <10 mm, randomly selected 46 eyes of 46 patients evaluated for Sjögren's syndrome (SS) in the rheumatology department between December 2022 and April 2023 were classified as low Schirmer group (LSG). Randomly selected 48 eyes of 48 patients at a similar age with Schirmer value >10 mm were included as controls. Main lacrimal gland SWE measurements in LSG and control groups were recorded and compared as meter/second (m/sec).

Mean SWE values of the main lacrimal gland were measured as 2.78 ± 0.66 m/sec and 2.26 ± 0.29 m/sec in LSG and controls. SWE measurements were significantly higher in LSG patients than in controls (p<0.001). No significant correlation was found in the analysis between the Schirmer and the main lacrimal gland SWE values in LSG patients (p=0.702, r=0.058). No significant correlation was also detected between the Schirmer and main lacrimal gland SWE values in controls (p=0.097, r=0.242). No significant relationship was also found between age, gender, body mass index (BMI), and SWE values (p=0.351, p=0.493, p=0.328, respectively).

Mean SWE value of the main lacrimal gland was determined as significantly higher in patients with aqueous lacrimal insufficiency without SS than in controls. We consider that SWE measurements may be an imaging method to support the diagnosis of aqueous lacrimal insufficiency and used in follow-ups of those with dry eye syndrome (DES) in the future.