Many cancer diagnosis and treatment modalities have been developed over time, among which radioisotopes have been widely used. Recently, with the application of magnetic nanoparticles, a wide range ...of non-invasive diagnosis and treatment methods using magnetic techniques have attracted attention. This Special Issue introduces a lymph node biopsy method with a magnetic probe for the treatment of cancer, rapid immunostaining using magnetic nanoparticles in pathological diagnosis, cancer imaging with MRI (magnetic resonance imaging)/MPI (magnetic particle imaging), magnetic hyperthermia for cancer treatment, and the development of magnetic nanoparticles.
Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions
...(for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping
(the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations
. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.
Good practice in food-related neuroimaging Smeets, Paul AM; Dagher, Alain; Hare, Todd A ...
The American journal of clinical nutrition,
03/2019, Letnik:
109, Številka:
3
Journal Article
Recenzirano
Odprti dostop
The use of neuroimaging tools, especially functional magnetic resonance imaging, in nutritional research has increased substantially over the past 2 decades. Neuroimaging is a research tool with ...great potential impact on the field of nutrition, but to achieve that potential, appropriate use of techniques and interpretation of neuroimaging results is necessary. In this article, we present guidelines for good methodological practice in functional magnetic resonance imaging studies and flag specific limitations in the hope of helping researchers to make the most of neuroimaging tools and avoid potential pitfalls. We highlight specific considerations for food-related studies, such as how to adjust statistically for common confounders, like, for example, hunger state, menstrual phase, and BMI, as well as how to optimally match different types of food stimuli. Finally, we summarize current research needs and future directions, such as the use of prospective designs and more realistic paradigms for studying eating behavior.
Objectives
To investigate the diagnostic value of functional MRI to assess renal interstitial fibrosis in patients with chronic kidney disease (CKD).
Methods
We prospectively recruited 80 CKD ...patients who underwent renal biopsies and 16 healthy volunteers to undergo multiparametric functional MRI examinations. The Oxford MEST-C classification was used to score the interstitial fibrosis. The diagnostic performance of functional MRI to discriminate interstitial fibrosis was evaluated by calculating the area under the receiver operating characteristic (ROC) curves.
Results
IgA nephropathy (60%) accounted for the majority of pathologic type in the CKD patients. Apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) was correlated with interstitial fibrosis (rho = −0.73). Decreased renal blood flow (RBF) derived from arterial spin labeling (rho = −0.78) and decreased perfusion fraction (f) derived from DWI (rho = −0.70) were accompanied by increased interstitial fibrosis. The T1 value from T1 mapping correlated with interstitial fibrosis (rho = 0.67) (all
p
< 0.01). The areas under the ROC curve for the discrimination of ≤ 25% vs. > 25% and ≤ 50% vs. > 50% interstitial fibrosis were 0.87 (95% confidence interval, 0.78 to 0.94) and 0.93 (0.86 to 0.98) by ADC, 0.84 (0.74 to 0.91) and 0.94 (0.86 to 0.98) by f, 0.93 (0.85 to 0.98) and 0.90 (0.82 to 0.96) by RBF, and 0.91 (0.83 to 0.96) and 0.77 (0.66 to 0.85) by T1, respectively.
Conclusions
Functional MRI parameters were strongly correlated with the interstitial fibrosis of CKD. Therefore, it might a powerful tool to assess interstitial fibrosis of CKD noninvasively.
Key Points
• In CKD patients, the renal cortical ADC value decreased and T1 value increased significantly compared with healthy volunteers.
• Functional MRI revealed significantly decreased renal perfusion in CKD patients compared with healthy volunteers.
• The renal cortical ADC, f, RBF, and T1 values were strongly correlated with the interstitial fibrosis of CKD.
Time‐invariant resting‐state functional connectivity studies have illuminated the crucial role of the right anterior insula (rAI) in prominent social impairments of autism spectrum disorder (ASD). ...However, a recent dynamic connectivity study demonstrated that rather than being stationary, functional connectivity patterns of the rAI vary significantly across time. The present study aimed to explore the differences in functional connectivity in dynamic states of the rAI between individuals with ASD and typically developing controls (TD). Resting‐state functional magnetic resonance imaging data obtained from a publicly available database were analyzed in 209 individuals with ASD and 298 demographically matched controls. A k‐means clustering algorithm was utilized to obtain five dynamic states of functional connectivity of the rAI. The temporal properties, frequency properties, and meta‐analytic decoding were first identified in TD group to obtain the characteristics of each rAI dynamic state. Multivariate analysis of variance was then performed to compare the functional connectivity patterns of the rAI between ASD and TD groups in obtained states. Significantly impaired connectivity was observed in ASD in the ventral medial prefrontal cortex and posterior cingulate cortex, which are two critical hubs of the default mode network (DMN). States in which ASD showed decreased connectivity between the rAI and these regions were those more relevant to socio‐cognitive processing. From a dynamic perspective, these findings demonstrate partially impaired resting‐state functional connectivity patterns between the rAI and DMN across states in ASD, and provide novel insights into the neural mechanisms underlying social impairments in individuals with ASD.
This book is highly focused on computational aspects of Bayesian data analysis of photon-limited data acquired in tomographic measurements in nuclear imaging. Basic Bayesian statistical concepts, ...elements of Bayesian decision theory, and counting statistics are discussed in the first chapters. Monte Carlo methods and Markov chains in posterior analysis are discussed next along with an introduction to nuclear imaging and applications such as PET and SPECT. The final chapter includes illustrative examples of statistical computing based on Poisson-multinomial statistics. Examples include calculation of Bayes factors and risks, and Bayesian decision making and hypothesis testing. C++ code used in the final chapter is also provided.
Objectives
The primary objective was to compare the performance of 3 different abbreviated MRI (AMRI) sets extracted from a complete gadoxetate-enhanced MRI obtained for hepatocellular carcinoma ...(HCC) screening. Secondary objective was to perform a preliminary cost-effectiveness analysis, comparing each AMRI set to published ultrasound performance for HCC screening in the USA.
Methods
This retrospective study included 237 consecutive patients (M/F, 146/91; mean age, 58 years) with chronic liver disease who underwent a complete gadoxetate-enhanced MRI for HCC screening in 2017 in a single institution. Two radiologists independently reviewed 3 AMRI sets extracted from the complete exam: non-contrast (NC-AMRI: T2-weighted imaging (T2wi)+diffusion-weighted imaging (DWI)), dynamic-AMRI (Dyn-AMRI: T2wi+DWI+dynamic T1wi), and hepatobiliary phase AMRI (HBP-AMRI: T2wi+DWI+T1wi during the HBP). Each patient was classified as HCC-positive/HCC-negative based on the reference standard, which consisted in all available patient data. Diagnostic performance for HCC detection was compared between sets. Estimated set characteristics, including historical ultrasound data, were incorporated into a microsimulation model for cost-effectiveness analysis.
Results
The reference standard identified 13/237 patients with HCC (prevalence, 5.5%; mean size, 33.7 ± 30 mm). Pooled sensitivities were 61.5% for NC-AMRI (95% confidence intervals, 34.4–83%), 84.6% for Dyn-AMRI (60.8–95.1%), and 80.8% for HBP-AMRI (53.6–93.9%), without difference between sets (
p
range, 0.06–0.16). Pooled specificities were 95.5% (92.4–97.4%), 99.8% (98.4–100%), and 94.9% (91.6–96.9%), respectively, with a significant difference between Dyn-AMRI and the other sets (
p
< 0.01). All AMRI methods were effective compared with ultrasound, with life-year gain of 3–12 months against incremental costs of US$ < 12,000.
Conclusions
NC-AMRI has limited sensitivity for HCC detection, while HBP-AMRI and Dyn-AMRI showed excellent sensitivity and specificity, the latter being slightly higher for Dyn-AMRI. Cost-effectiveness estimates showed that AMRI is effective compared with ultrasound.
Key Points
• Comparison of different abbreviated MRI (AMRI) sets reconstructed from a complete gadoxetate MRI demonstrated that non-contrast AMRI has low sensitivity (61.5%) compared with contrast-enhanced AMRI (80.8% for hepatobiliary phase AMRI and 84.6% for dynamic AMRI), with all sets having high specificity.
• Non-contrast and hepatobiliary phase AMRI can be performed in less than 14 min (including set-up time), while dynamic AMRI can be performed in less than 17 min.
• All AMRI sets were cost-effective for HCC screening in at-risk population in comparison with ultrasound.
Magnetic resonance imaging is a key diagnostic tool in modern healthcare, yet it can be cost-prohibitive given the high installation, maintenance and operation costs of the machinery. There are ...approximately seven scanners per million inhabitants and over 90% are concentrated in high-income countries. We describe an ultra-low-field brain MRI scanner that operates using a standard AC power outlet and is low cost to build. Using a permanent 0.055 Tesla Samarium-cobalt magnet and deep learning for cancellation of electromagnetic interference, it requires neither magnetic nor radiofrequency shielding cages. The scanner is compact, mobile, and acoustically quiet during scanning. We implement four standard clinical neuroimaging protocols (T1- and T2-weighted, fluid-attenuated inversion recovery like, and diffusion-weighted imaging) on this system, and demonstrate preliminary feasibility in diagnosing brain tumor and stroke. Such technology has the potential to meet clinical needs at point of care or in low and middle income countries.
Objectives
T o evaluate the value of multiparametric MRI (mpMRI) for the prediction of prostate cancer (PCA) aggressiveness.
Methods
In this single center cohort study, consecutive patients with ...histologically confirmed PCA were retrospectively enrolled. Four different ISUP grade groups (1, 2, 3, 4–5) were defined and fifty patients per group were included. Several clinical (age, PSA, PSAD, percentage of PCA infiltration) and mpMRI parameters (ADC value, signal increase on high b-value images, diameter, extraprostatic extension EPE, cross-zonal growth) were evaluated and correlated within the four groups. Based on combined descriptors, MRI grading groups (mG1–mG3) were defined to predict PCA aggressiveness.
Results
In total, 200 patients (mean age 68 years, median PSA value 8.1 ng/ml) were analyzed. Between the four groups, statistically significant differences could be shown for age, PSA, PSAD, and for MRI parameters cross-zonal growth, high b-value signal increase, EPE, and ADC (
p
< 0.01). All examined parameters revealed a significant correlation with the histopathologic biopsy ISUP grade groups (
p
< 0.01), except PCA diameter (
p
= 0.09). A mixed linear model demonstrated the strongest prediction of the respective ISUP grade group for the MRI grading system (
p
< 0.01) compared to single parameters.
Conclusions
MpMRI yields relevant pre-biopsy information about PCA aggressiveness. A combination of quantitative and qualitative parameters (MRI grading groups) provided the best prediction of the biopsy ISUP grade group and may improve clinical pathway and treatment planning, adding useful information beyond PI-RADS assessment category. Due to the high prevalence of higher grade PCA in patients within mG3, an early re-biopsy seems indicated in cases of negative or post-biopsy low-grade PCA.
Key Points
•
MpMRI yields relevant pre-biopsy information about prostate cancer aggressiveness.
•
MRI grading in addition to PI-RADS classification seems to be helpful for a size independent early prediction of clinically significant PCA.
•
MRI grading groups may help urologists in clinical pathway and treatment planning, especially when to consider an early re-biopsy.
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