Improved diagnostic accuracy in differentiating malignant and benign lesions using single-voxel proton MRS of the breast at 3 T MRI

AIM: To investigate the diagnostic accuracy of single-voxel proton magnetic resonance spectroscopy (SV (1)H MRS) by quantifying total choline-containing compounds (tCho) in differentiating malignant from benign lesions, and subsequently, to analyse the relationship of tCho levels in malignant breast lesions with their histopathological subtypes. MATERIALS AND METHODS: A prospective study of SV 1H MRS was performed following dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in 61 women using a 3 T MR system. All lesions (n = 57) were analysed for characteristics of morphology, contrast-enhancement kinetics, and tCho peak heights at SV (1)H MRS that were two-times above baseline. Subsequently, the tCho in selected lesions (n = 32) was quantified by calculating the area under the curve, and a tCho concentration equal to or greater than the cut-off value was considered to represent malignancy. The relationship between tCho in invasive ductal carcinomas (IDCs) and their Bloom & Richardson grading of malignancy was assessed. RESULTS: Fifty-two patients (57 lesions; 42 malignant and 15 benign) were analysed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), of predicting malignancy were 100, 73.3, 91.3, and 100%, respectively, using DCE-MRI and 95.2, 93.3, 97.6, and 87.5%, respectively, using SV (1)H MRS. The tCho cut-off for receiver operating characteristic (ROC) curve was 0.33 mmol/l. The relationship between tCho levels in malignant breast lesions with their histopathological subtypes was not statistically significant (p = 0.3). CONCLUSION: Good correlation between tCho peaks and malignancy, enables SV (1)H MRS to be used as a clinically applicable, simple, yet non-invasive tool for improved specificity and diagnostic accuracy in detecting breast cancer

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Shear wave elastography in the evaluation of renal parenchymal stiffness in patients with chronic kidney disease

objective: To investigate the use of shear wave elastography (SWE)-derived estimates of Young's modulus (YM) as an indicator to detect abnormal renal tissue diagnosed by estimated glomerular filtration rate (eGFR). methods: The study comprised 106 chronic kidney disease (CKD) patients and 203 control subjects. Conventional ultrasound was performed to measure the kidney length and cortical thickness. SWE imaging was performed to measure renal parenchymal stiffness. Diagnostic performance of SWE and conventional ultrasound were correlated with serum creatinine, urea levels and eGFR. results: Pearson's correlation coefficient revealed a negative correlation between YM measurements and eGFR (r = −0.576, p < 0.0001). Positive correlations between YM measurements and age (r = 0.321, p < 0.05), serum creatinine (r = 0.375, p < 0.0001) and urea (r = 0.287, p < 0.0001) were also observed. The area under the receiver operating characteristic curve for SWE (0.87) was superior to conventional ultrasound alone (0.35-0.37). The cut-off value of less or equal to 4.31 kPa suggested a non-diseased kidney (80.3% sensitivity, 79.5% specificity). conclusion: SWE was superior to renal length and cortical thickness in detecting CKD. A value of 4.31 kPa or less showed good accuracy in determining whether a kidney was diseased or not

Evaluation of Therapeutic Efficacy and Imaging Capabilities of ¹⁵³Sm₂O₃-Loaded Polystyrene Microspheres for Intra-Tumoural Radionuclide Therapy of Liver Cancer Using Sprague-Dawley Rat Model

Introduction: Neutron-activated samarium-153-oxide-loaded polystyrene ([153Sm]Sm2O3-PS) microspheres has been developed in previous study as a potential theranostic agent for hepatic radioembolization. In this study, the therapeutic efficacy and diagnostic imaging capabilities of the formulation was assessed using liver cancer Sprague-Dawley (SD) rat model. Methods: Twelve male SD rats (150–200 g) that implanted with N1-S1 hepatoma cell line orthotopically were divided into two groups (study versus control) to monitor the tumour growth along 60 days of treatment. The study group received an intra-tumoural injection of approximately 37 MBq of [153Sm]Sm2O3-PS microspheres, while control group received an intra-tumoural injection of 0.1 mL of saline solution. A clinical single photon emission computed tomography/computed tomography (SPECT/CT) system was used to scan the rats at Day 5 post-injection to investigate the diagnostic imaging capabilities of the microspheres. All rats were monitored for change in tumour volume using a portable ultrasound system throughout the study period. Histopathological examination (HPE) was performed after the rats were euthanized at Day 60. Results: At Day 60, no tumour was observed on the ultrasound images of all rats in the study group. In contrast, the tumour volumes in the control group were 24-fold larger compared to baseline. Statistically significant difference was observed in tumour volumes between the study and control groups (p 153Sm]Sm2O3-PS in the liver tumour of all rats at Day 5 post-injection. Additionally, the [153Sm]Sm2O3-PS microspheres was visible on the CT images and this has added to the benefits of 153Sm as a CT contrast agent. The HPE results showed that the [153Sm]Sm2O3-PS microspheres remained concentrated at the injection site with no tumour cells observed in the study group. Conclusions: Neutron-activated [153Sm]Sm2O3-PS microspheres demonstrated excellent therapeutic and diagnostic imaging capabilities for theranostic treatment of liver cancer in a SD rat model. Further studies with different animal and tumour models are planned to validate this finding

Marrow fat content and composition in β‐Thalassemia: a study using 1H‐MRS

Background: β-thalassemia is a genetic disease that causes abnormal production of red blood cells (ineffective erythropoiesis, IE). IE is a condition known to change bone marrow composition. Purpose: To evaluate the effect of IE on the marrow fat content and fat unsaturation levels in the proximal femur using 1 H-MRS. Study Type: Prospective. Subjects: Twenty-three subjects were included in this study, seven control and 16 β-thalassemia subjects. Field Strength/Sequence: 3.0T; stimulated echo acquisition Mode (STEAM); magnetic resonance spectroscopy (MRS) sequence. Assessment: Multiecho MRS scans were performed in four regions of the proximal left femur of each subject, that is, diaphysis, femoral neck, femoral head, and greater trochanter. The examined regions were grouped into red (diaphysis and femoral neck) and yellow marrow regions (femoral head and greater trochanter). Statistical Tests: The Jonckheere–Terpstra test was used to evaluate the impact of increasing disease severity on bone marrow fat fraction (BMFF), marrow conversion index, and fat unsaturation index (UI). Pairwise comparison analysis was performed when a significant trend (P < 0.05) was found. K-means clustering analysis was used to examine the clusters observed when BMFF in the red and yellow regions were studied (diaphysis against greater trochanter). Results: BMFF showed a significant decreasing trend with increasing disease severity in both red (TJT = 109.00, z = –4.414, P < 0.05) and yellow marrow regions (TJT = 108.00, z = –4.438, P < 0.05). The opposite trend was observed in UI in both bone marrow regions (red marrow: TJT = 180.5, z = 3.515, P < 0.05; yellow marrow: TJT = 155.0, z = 2.282, P = 0.05). Three distinct forms of marrow adipogenesis were found when plotting BMFF diaphysis against BMFF greater trochanter: 1) normal (centroid: 80.4%, 66.6%), 2) partial disruption (centroid: 51.1%, 16.6%), and 3) total disruption (centroid: 2.6%, 1.6%). Data Conclusion: β-thalassemia is associated with decreased marrow fat, and increased marrow fat unsaturation level. Level of Evidence: 2 Technical Efficacy Stage: 3

Bone marrow fat distribution in patients with β-thalassemia: a study using chemical shift-based water-fat MRI

Rationale and Objectives: Molecular studies have shown the changes in bone marrow fat in relation to altered hematopoiesis. This study aims to investigate the changes in the bone marrow fat in patients affected by β-thalassemia by using chemical shift-encoded (CSE)–MRI. Materials and Methods: Twenty-three subjects, comprising of six healthy (17-31 years old) and 17 β-thalassemia subjects (19-39 years old), were scanned using a multiecho fast low angle shot sequence (0.94 × 0.94 × 3.00 mm3) and a stimulated echo acquisition mode sequence using 3T MRI. Bone marrow proton density fat fraction (PDFF) was quantified in the left femur of each subject. Regression and Bland-Altman analysis were used to analyze agreement between CSE-MRI and 1H-MRS. PDFF distribution was analyzed using Hartigan's dip test and the computed Wasserstein distances. Jonckheere-Terpstra trend analysis was performed to evaluate the effect of disease severity on PDFF distribution. Results: An excellent agreement was found between PDFF measured using CSE-MRI with 1H-MRS (R2 = 0.91; bias =-1.41%). Healthy subjects showed left-skewed or bimodal PDFF distribution while β-thalassemia subjects showed bimodal, normal or right-skewed distribution. Jonckheere-Terpstra test shows that PDFF distribution was increasingly different from the norm as disease severity increased (TJT = 166.0, z = 3.806, p < 0.05). Increase in variability of PDFF distribution within each subject group was also seen with increasing disease severity (TJT = 169.0, z = 3.971, p < 0.05). Conclusion: CSE-MRI is a promising tool to demonstrate spatial changes and variability in marrow fat distribution, resulting from ineffective erythropoiesis

Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts

This study aims to introduce a hand-crafted machine learning method to classify ischemic and hemorrhagic strokes with satisfactory performance. In the first step of this work, a new CT brain for images dataset was collected for stroke patients. A highly accurate handcrafted machine learning method is developed and tested for these cases. This model uses preprocessing, feature creation using a novel pooling method (it is named P9), a local phase quantization (LPQ) operator, and a Chi2 -based selector responsible for selecting the most significant features. After that, classification is done using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation (CV). The novel aspect of this model is the P9 pooling method. The inspiration for this pooling method was drawn from the deep learning models, where features are extracted with multiple layers using a convolution operator applied to the pooling method. However, pooling decompositions have a routing problem.The P9 pooling function creates nine decomposed models, hence the name. The LPQ feature extractor is applied to images to generate sub-bands for feature generation. The Chi2 selector is then employed to select the most significant features from the created feature vector, and these features are utilized for the classification using the k-nearest neighbor algorithm (kNN). The introduced P9-LPQ feature extraction-based learning model attained over 98% classification accuracy in all cases. The results obtained in this paper show that the proposed method can successfully classify stroke types. For this reason, the developed model can pre-diagnose stroke types in the future