Molecular imaging in oncology: Current impact and future directions.

The authors define molecular imaging, according to the Society of Nuclear Medicine and Molecular Imaging, as the visualization, characterization, and measurement of biological processes at the molecular and cellular levels in humans and other living systems. Although practiced for many years clinically in nuclear medicine, expansion to other imaging modalities began roughly 25 years ago and has accelerated since. That acceleration derives from the continual appearance of new and highly relevant animal models of human disease, increasingly sensitive imaging devices, high-throughput methods to discover and optimize affinity agents to key cellular targets, new ways to manipulate genetic material, and expanded use of cloud computing. Greater interest by scientists in allied fields, such as chemistry, biomedical engineering, and immunology, as well as increased attention by the pharmaceutical industry, have likewise contributed to the boom in activity in recent years. Whereas researchers and clinicians have applied molecular imaging to a variety of physiologic processes and disease states, here, the authors focus on oncology, arguably where it has made its greatest impact. The main purpose of imaging in oncology is early detection to enable interception if not prevention of full-blown disease, such as the appearance of metastases. Because biochemical changes occur before changes in anatomy, molecular imaging-particularly when combined with liquid biopsy for screening purposes-promises especially early localization of disease for optimum management. Here, the authors introduce the ways and indications in which molecular imaging can be undertaken, the tools used and under development, and near-term challenges and opportunities in oncology.

Gray matter volume of the feline cerebral cortex and structural plasticity following perinatal deafness.

In response to sensory deprivation, the brain adapts according to contemporary demands to efficiently navigate a modified perceptual environment. This reorganization may result in improved processing of the remaining senses-a phenomenon referred to as compensatory crossmodal plasticity. One approach to explore this neuroplasticity is to consider the macrostructural changes in neural tissue that mirror this functional optimization. The current study is the first of its kind to measure MRI-derived gray matter (GM) volumes of control felines (n=30), while additionally identifying volumetric differences in response to perinatal deafness (30 ototoxically-deafened cats). To accomplish this purpose, regional and morphometric methods were performed in parallel. The regional analysis evaluated volumetric alterations of global GM, as well as the volumes of 146 regions of interest (ROIs) and 12 functional subgroupings of these ROIs. Results revealed whole-brain GM preservation; however, somatosensory and visual cortices exhibited an overall increase in volume. On a smaller scale, this analysis uncovered two auditory ROIs (second auditory cortex, A2, and ventral auditory field, VAF) that decreased in volume alongside two visual regions (anteromedial lateral suprasylvian area, AMLS and splenial visual area, SVA) that increased-all localized within the right hemisphere. Comparatively, the findings of tensor-based morphometry (TBM) generally aligned with those of the ROI-based method, as this voxel-wise approach demonstrated clusters of expansion coincident with visual- and somatosensory-related loci; although, it failed to detect any GM reductions following deafness. As distinct differences were identified in each analysis, the current study highlights the importance of employing multiple methods when exploring MRI volumetry. Overall, this study proposes that volumetric alterations within sensory loci allude to a redistribution of cortical space arising from modified perceptual demands following auditory deprivation.

Kilogram-Scale Synthesis of Extremely Small Gadolinium Oxide Nanoparticles as a T1-Weighted Contrast Agent for Magnetic Resonance Imaging.

Magnetic resonance imaging contrast agents are frequently used in clinics to enhance the contrast between diseased and normal tissues. The previously reported poly(acrylic acid) stabilized exceedingly small gadolinium oxide nanoparticles (ES-GdON-PAA) overcame the problems of commercial Gd chelates, but limitations still exist, i.e., high r2/r1 ratio, long blood circulation half-life, and no data for large scale synthesis and formulation optimization. In this study, polymaleic acid (PMA) is found to be an ideal stabilizer to synthesize ES-GdONs. Compared with ES-GdON-PAA, the PMA-stabilized ES-GdON (ES-GdON-PMA) has a lower r2/r1 ratio (2.05, 7.0 T) and a lower blood circulation half-life (37.51 min). The optimized ES-GdON-PMA-9 has an exceedingly small particle size (2.1 nm), excellent water dispersibility, and stability. A facile, efficient, and environmental friendly synthetic method is developed for large-scale synthesis of the ES-GdONs-PMA. The weight of the optimized freeze-dried ES-GdON-PMA-26 synthesized in a 20 L of reactor reaches the kilogram level. The formulation optimization is also finished, and the concentrated ES-GdON-PMA-26 formulation (CGd = 100 mm) after high-pressure steam sterilization possesses eligible physicochemical properties (i.e., pH value, osmolality, viscosity, and density) for investigational new drug application.

MRI radiomics to monitor therapeutic outcome of sorafenib plus IHA transcatheter NK cell combination therapy in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common liver malignancy with limited treatment options. Previous studies expressed the potential synergy of sorafenib and NK cell immunotherapy as a promising approach against HCC. MRI is commonly used to assess response of HCC to therapy. However, traditional MRI-based metrics for treatment efficacy are inadequate for capturing complex changes in the tumor microenvironment, especially with immunotherapy. In this study, we investigated potent MRI radiomics analysis to non-invasively assess early responses to combined sorafenib and NK cell therapy in a HCC rat model, aiming to predict multiple treatment outcomes and optimize HCC treatment evaluations. METHODS: Sprague Dawley (SD) rats underwent tumor implantation with the N1-S1 cell line. Tumor progression and treatment efficacy were assessed using MRI following NK cell immunotherapy and sorafenib administration. Radiomics features were extracted, processed, and selected from both T1w and T2w MRI images. The quantitative models were developed to predict treatment outcomes and their performances were evaluated with area under the receiver operating characteristic (AUROC) curve. Additionally, multivariable linear regression models were constructed to determine the correlation between MRI radiomics and histology, aiming for a noninvasive evaluation of tumor biomarkers. These models were evaluated using root-mean-squared-error (RMSE) and the Spearman correlation coefficient. RESULTS: A total of 743 radiomics features were extracted from T1w and T2w MRI data separately. Subsequently, a feature selection process was conducted to identify a subset of five features for modeling. For therapeutic prediction, four classification models were developed. Support vector machine (SVM) model, utilizing combined T1w + T2w MRI data, achieved 96% accuracy and an AUROC of 1.00 in differentiating the control and treatment groups. For multi-class treatment outcome prediction, Linear regression model attained 85% accuracy and an AUC of 0.93. Histological analysis showed that combination therapy of NK cell and sorafenib had the lowest tumor cell viability and the highest NK cell activity. Correlation analyses between MRI features and histological biomarkers indicated robust relationships (r = 0.94). CONCLUSIONS: Our study underscored the significant potential of texture-based MRI imaging features in the early assessment of multiple HCC treatment outcomes.

Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction.

PURPOSE: To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction. METHODS: For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects. RESULTS: In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones. CONCLUSION: The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.

Utility of MRI-based vertebral bone quality scores and CT-based Hounsfield unit values in vertebral bone mineral density assessment for patients with diffuse idiopathic skeletal hyperostosis.

This study investigated bone mineral density assessment for patients with DISH. DXA-based T-scores overestimated bone quality, while MRI-based VBQ scores and CT-based HU values provided accurate assessments, particularly for advanced degenerative cases. This enhances accurate evaluation of BMD, crucial for clinical decision-making. PURPOSE: To investigate the diagnostic effectiveness of DXA, MRI, and CT in assessing bone mineral density (BMD) for diffuse idiopathic skeletal hyperostosis (DISH) patients. METHODS: Retrospective analysis of 105 DISH patients and 116 age-matched controls with lumbar spinal stenosis was conducted. BMD was evaluated using DXA-based T-scores, MRI-based vertebral bone quality (VBQ) scores, and CT-based Hounsfield unit (HU) values. Patients were categorized into three BMD subgroups. Lumbar osteophyte categories were determined by Mata score. Demographics, clinical data, T-scores, VBQ scores, and HU values were collected. Receiver operating characteristic (ROC) analysis identified VBQ and HU thresholds for diagnosing normal BMD using DXA in controls. Correlations between VBQ, HU, and lumbar T-score were analyzed. RESULTS: Age, gender, and BMI showed no significant differences between DISH and control groups. DISH patients had higher T-score (L1-4), the lowest T-score, and Mata scores. VBQ and HU did not significantly differ between groups. In controls, VBQ and HU effectively diagnosed normal BMD (AUC = 0.857 and 0.910, respectively) with cutoffs of 3.0 for VBQ and 104.3 for HU. DISH had higher normal BMD prevalence using T-scores (69.5% vs. 58.6%, P < 0.05), but no significant differences using VBQ (57.1% vs. 56.2%, P > 0.05) and HU (58.1% vs. 57.8%, P > 0.05). Correlations revealed moderate correlations between HU and T-scores (L1-4) in DISH (r = 0.642, P < 0.001) and strong in controls (r = 0.846, P < 0.001). Moderate negative correlations were observed between VBQ and T-scores (L1-4) in DISH (r = - 0.450, P < 0.001) and strong in controls (r = - 0.813, P < 0.001). CONCLUSION: DXA-based T-scores may overestimate BMD in DISH. VBQ scores and HU values could effectively complement BMD assessment, particularly in DISH patients or those with advanced lumbar degeneration.

Managing Patients With Unlabeled Passive Implants on MR Systems Operating Below 1.5 T.

The standard of care for managing a patient with an implant is to identify the item and to assess the relative safety of scanning the patient. Because the 1.5 T MR system is the most prevalent scanner in the world and 3 T is the highest field strength in widespread use, implants typically have "MR Conditional" (i.e., an item with demonstrated safety in the MR environment within defined conditions) labeling at 1.5 and/or 3 T only. This presents challenges for a facility that has a scanner operating at a field strength below 1.5 T when encountering a patient with an implant, because scanning the patient is considered "off-label." In this case, the supervising physician is responsible for deciding whether to scan the patient based on the risks associated with the implant and the benefit of magnetic resonance imaging (MRI). For a passive implant, the MRI safety-related concerns are static magnetic field interactions (i.e., force and torque) and radiofrequency (RF) field-induced heating. The worldwide utilization of scanners operating below 1.5 T combined with the increasing incidence of patients with implants that need MRI creates circumstances that include patients potentially being subjected to unsafe imaging conditions or being denied access to MRI because physicians often lack the knowledge to perform an assessment of risk vs. benefit. Thus, physicians must have a complete understanding of the MRI-related safety issues that impact passive implants when managing patients with these products on scanners operating below 1.5 T. This monograph provides an overview of the various clinical MR systems operating below 1.5 T and discusses the MRI-related factors that influence safety for passive implants. Suggestions are provided for the management of patients with passive implants labeled MR Conditional at 1.5 and/or 3 T, referred to scanners operating below 1.5 T. The purpose of this information is to empower supervising physicians with the essential knowledge to perform MRI exams confidently and safely in patients with passive implants. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Use of non-invasive diagnostic tools for metabolic dysfunction-associated steatohepatitis: A qualitative exploration of challenges and barriers.

BACKGROUND AND AIMS: Non-invasive tests (NITs) are underutilized for diagnosis and risk stratification in metabolic dysfunction-associated steatotic liver disease (MASLD), despite good accuracy. This study aimed to identify challenges and barriers to the use of NITs in clinical practice. METHODS: We conducted a qualitative exploratory study in Germany, Italy, United Kingdom and United States. Phase 1 participants (primary care physicians, hepatologists, diabetologists, researchers, healthcare administrators, payers and patient advocates; n = 29) were interviewed. Phase 2 participants (experts in MASLD; n = 8) took part in a group discussion to validate and expand on Phase 1 findings. Finally, we triangulated perspectives in a hybrid deductive/inductive thematic analysis. RESULTS: Four themes hindering the use of NITs emerged: (1) limited knowledge and awareness; (2) unclear referral pathways for patients affected by liver conditions; (3) uncertainty over the value of NITs in monitoring and managing liver diseases; and (4) challenges justifying system-level reimbursement. Through these themes, participants perceived a stigma associated with liver diseases, and primary care physicians generally lacked awareness, adequate knowledge and skills to use recommended NITs. We identified uncertainties over the results of NITs, specifically to guide lifestyle intervention or to identify patients that should be referred to a specialist. Participants indicated an ongoing need for research and development to improve the prognostic value of NITs and communicating their cost-effectiveness to payers. CONCLUSIONS: This qualitative study suggests that use of NITs for MASLD is limited due to several individual and system-level barriers. Multi-level interventions are likely required to address these barriers.

Development and external validation of MRI-based RAS mutation status prediction model for liver metastases of colorectal cancer.

BACKGROUND: The mutation status of rat sarcoma viral oncogene homolog (RAS) has prognostic significance and serves as a key predictive biomarker for the effectiveness of antiepidermal growth factor receptor (EGFR) therapy. However, there remains a lack of effective models for predicting RAS mutation status in colorectal liver metastases (CRLMs). This study aimed to construct and validate a diagnostic model for predicting RAS mutation status among patients undergoing hepatic resection for CRLMs. METHODS: A diagnostic multivariate prediction model was developed and validated in patients with CRLMs who had undergone hepatectomy between 2014 and 2020. Patients from Institution A were assigned to the model development group (i.e., Development Cohort), while patients from Institutions B and C were assigned to the external validation groups (i.e., Validation Cohort_1 and Validation Cohort_2). The presence of CRLMs was determined by examination of surgical specimens. RAS mutation status was determined by genetic testing. The final predictors, identified by a group of oncologists and radiologists, included several key clinical, demographic, and radiographic characteristics derived from magnetic resonance images. Multiple imputation was performed to estimate the values of missing non-outcome data. A penalized logistic regression model using the adaptive least absolute shrinkage and selection operator penalty was implemented to select appropriate variables for the development of the model. A single nomogram was constructed from the model. The performance of the prediction model, discrimination, and calibration were estimated and reported by the area under the receiver operating characteristic curve (AUC) and calibration plots. Internal validation with a bootstrapping procedure and external validation of the nomogram were assessed. Finally, decision curve analyses were used to characterize the clinical outcomes of the Development and Validation Cohorts. RESULTS: A total of 173 patients were enrolled in this study between January 2014 and May 2020. Of the 173 patients, 117 patients from Institution A were assigned to the Model Development group, while 56 patients (33 from Institution B and 23 from Institution C) were assigned to the Model Validation groups. Forty-six (39.3%) patients harbored RAS mutations in the Development Cohort compared to 14 (42.4%) in Validation Cohort_1 and 8 (34.8%) in Validation Cohort_2. The final model contained the following predictor variables: time of occurrence of CRLMs, location of primary lesion, type of intratumoral necrosis, and early enhancement of liver parenchyma. The diagnostic model based on clinical and MRI data demonstrated satisfactory predictive performance in distinguishing between mutated and wild-type RAS, with AUCs of 0.742 (95% confidence interval [CI]: 0.651─0.834), 0.741 (95% CI: 0.649─0.836), 0.703 (95% CI: 0.514─0.892), and 0.708 (95% CI: 0.452─0.964) in the Development Cohort, bootstrapping internal validation, external Validation Cohort_1 and Validation Cohort_2, respectively. The Hosmer-Lemeshow goodness-of-fit values for the Development Cohort, Validation Cohort_1 and Validation Cohort_2 were 2.868 (p = 0.942), 4.616 (p = 0.465), and 6.297 (p = 0.391), respectively. CONCLUSIONS: Integrating clinical, demographic, and radiographic modalities with a magnetic resonance imaging-based approach may accurately predict the RAS mutation status of CRLMs, thereby aiding in triage and possibly reducing the time taken to perform diagnostic and life-saving procedures. Our diagnostic multivariate prediction model may serve as a foundation for prognostic stratification and therapeutic decision-making.

The Impact of MRI-Based Advanced Neuroimaging on Neurooncologists' Clinical Decision-Making in Patients With Posttreatment High-Grade Glioma: A Prospective Survey-Based Study.

Background: Advanced MRI-based neuroimaging techniques, such as perfusion and spectroscopy, have been increasingly incorporated into routine follow-up protocols in patients treated for high-grade glioma (HGG), to help differentiate tumor progression from treatment effect. However, these techniques' influence on clinical management remains poorly understood. Objective: To evaluate the impact of MRI-based advanced neuroimaging on clinical decision-making in patients with HGG in the posttreatment setting. Methods: This prospective study, performed at a comprehensive cancer center from March 1, 2017, to October 31, 2020, included adult patients treated by chemoradiation for WHO grade 4 diffuse glioma who underwent MRIbased advanced neuroimaging (comprising multiple perfusion imaging sequences and spectroscopy) to further evaluate findings on conventional MRI equivocal for tumor progression versus treatment effect. The ordering neuro-oncologists completed surveys before and after each advanced neuroimaging session. The percent of care episodes with a change between the intended and actual management plan on the surveys conducted before and after advanced neuroimaging, respectively, was computed and compared with a previously published percent using the Wald test for independent samples proportions. Results: The study included 63 patients (mean age, 55±13 years; 36 women, 27 men) who underwent 70 advanced neuroimaging sessions. Ordering neuro-oncologists' intended and actual management plans on the surveys completed before and after advanced neuroimaging, respectively, differed in 44% (31/70, [95% CI: 33-56%]) of episodes, which differed from the previously published frequency of 8.5% (5/59) (p<.001). These management plan changes included selection of a different plan for 6/8 episodes with an intended plan to enroll patients in a clinical trial, 12/19 episodes with an intended plan to change chemotherapeutic agents, 4/8 episodes with an intended plan of surgical intervention, and 1/2 episodes with an intended plan of re-irradiation. The ordering neuro-oncologists found advanced neuroimaging to be helpful in 93% (95% CI: 87%-99%) (65/70) of episodes. Conclusion: Neuro-oncologists' management plans changed in a substantial fraction of adult patients with HGG who underwent advanced neuroimaging to further evaluate conventional MRI findings equivocal for tumor progression versus treatment effect. Clinical Impact: The findings support incorporation of advanced neuroimaging into HGG posttreatment monitoring protocols.

Safety of magnetic resonance imaging in patients with cardiac implantable electronic devices and abandoned or epicardial leads: a systematic review and meta-analysis.

AIMS: Persistent reluctance to perform magnetic resonance imaging (MRI) in patients with abandoned and/or epicardial leads of cardiac implantable electronic devices is related to in vitro studies reporting tip heating. While there is a plethora of data on the safety of MRI in conditional and non-conditional implantable devices, there is a clear lack of safety data in patients with abandoned and/or epicardial leads. METHODS AND RESULTS: Relevant literature was identified in Medline and CINAHL using the key terms 'magnetic resonance imaging' AND 'abandoned leads' OR 'epicardial leads'. Secondary literature and cross-references were supplemented. For reporting guidance, the Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 was used. International Prospective Register of Systematic Reviews (PROSPERO) registration number 465530. Twenty-one publications with a total of 656 patients with 854 abandoned and/or epicardial leads and 929 MRI scans of different anatomical regions were included. No scan-related major adverse cardiac event was documented, although the possibility of under-reporting of critical events in the literature should be considered. Furthermore, no severe device dysfunction or severe arrhythmia was reported. Mainly transient lead parameter changes were observed in 2.8% in the subgroup of patients with functional epicardial leads. As a possible correlate of myocardial affection, subjective sensations occurred mainly in the subgroup with abandoned epicardial leads (4.0%), but no change in myocardial biomarkers was observed. CONCLUSION: Existing publications did not report any relevant adverse events for MRI in patients with abandoned and/or epicardial leads if performed according to strict safety guidelines. However, a more rigorous risk-benefit calculation should be made for patients with epicardial leads.

Cerebrovascular reactivity to hypercapnia in patients with migraine: A dual-echo arterial spin labeling MRI study.

OBJECTIVE: This study aimed to compare cerebrovascular reactivity between patients with migraine and controls using state-of-the-art magnetic resonance imaging (MRI) techniques. BACKGROUND: Migraine is associated with an increased risk of cerebrovascular disease, but the underlying mechanisms are still not fully understood. Impaired cerebrovascular reactivity has been proposed as a link. Previous studies have evaluated cerebrovascular reactivity with different methodologies and results are conflicting. METHODS: In this single-center, observational, case-control study, we included 31 interictal patients with migraine without aura (aged 19-66 years, 17 females) and 31 controls (aged 22-64 years, 18 females) with no history of vascular disease. Global and regional cerebrovascular reactivities were assessed with a dual-echo arterial spin labeling (ASL) 3.0 T MRI scan of the brain which measured the change in cerebral blood flow (CBF) and BOLD (blood oxygen level dependent) signal to inhalation of 5% carbon dioxide. RESULTS: When comparing patients with migraine to controls, cerebrovascular reactivity values were similar between the groups, including mean gray matter CBF-based cerebrovascular reactivity (3.2 ± 0.9 vs 3.4 ± 1% ΔCBF/mmHg CO2 ; p = 0.527), mean gray matter BOLD-based cerebrovascular reactivity (0.18 ± 0.04 vs 0.18 ± 0.04% ΔBOLD/mmHg CO2 ; p = 0.587), and mean white matter BOLD-based cerebrovascular reactivity (0.08 ± 0.03 vs 0.08 ± 0.02% ΔBOLD/mmHg CO2 ; p = 0.621).There was no association of cerebrovascular reactivity with monthly migraine days or migraine disease duration (all analyses p > 0.05). CONCLUSION: Cerebrovascular reactivity to carbon dioxide seems to be preserved in patients with migraine without aura.

Assessment of resting cerebral perfusion between methamphetamine-associated psychosis and schizophrenia through arterial spin labeling MRI.

OBJECTIVE: The clinical manifestations of methamphetamine (METH)-associated psychosis (MAP) and acute paranoid schizophrenia (SCZ) are similar. This study aims to assess regional cerebral blood flow (rCBF) in individuals who use METH and in those with SCZ using the MRI arterial spin labeling (ASL) technique. METHODS: We prospectively recruited 68 participants and divided them into four groups: MAP (N = 15), SCZ (N = 13), METH users with no psychosis (MNP; N = 22), and normal healthy controls (CRL; N = 18). We measured rCBF using an MRI three-dimensional pseudo-continuous ASL sequence. Clinical variables were assessed using the Positive and Negative Syndrome Scale (PANSS) and Brief Assessment of Cognition in Schizophrenia (BACS). Group-level rCBF differences were analyzed using a two-sample t-test. RESULTS: Decreased rCBF was found in the precuneus, premotor cortex, caudate nucleus, dorsolateral prefrontal cortex, and thalamus in the MNP group compared with the CRL group. The MAP group had significantly decreased rCBF in the precuneus, hippocampus, anterior insula, inferior temporal gyrus, inferior orbitofrontal gyrus, and superior occipital gyrus compared with the MNP group. Increased rCBF in the precuneus and premotor cortex was seen in the MAP group compared with the SCZ group. rCBF in the precuneus and premotor cortex significantly correlated negatively with the PANSS but correlated positively with BACS scores in the MAP and SCZ groups. CONCLUSION: METH exposure was associated with decreased rCBF in the precuneus and premotor cortex. Patients with MAP exhibited higher rCBF than those with SCZ, implying preserved insight and favorable outcomes. rCBF can therefore potentially serve as a diagnostic approach to differentiate patients with MAP from those with SCZ.

DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI.

A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. The input Magnetic Resonance Imaging (MRI) image is fed to the pre-processing stage. Tumour areas are segmented by utilizing SegNet in which the factors of SegNet are biased using DTDO. The image augmentation is carried out using eminent techniques, such as geometric transformation and colour space transformation. Here, features such as GIST descriptor, PCA-NGIST, statistical feature and Haralick features, SLBT feature, and CNN features are extricated. Finally, the categorization of the tumour is accomplished based on ZFNet, which is trained by utilizing DTDO. The devised DTDO is a consolidation of DTBO and CDDO. The comparison of proposed DTDO-ZFNet with the existing methods, which results in highest accuracy of 0.944, a positive predictive value (PPV) of 0.936, a true positive rate (TPR) of 0.939, a negative predictive value (NPV) of 0.937, and a minimal false-negative rate (FNR) of 0.061%.

Associations of 10-year atherosclerotic cardiovascular disease risk scores with cerebral small vessel disease: the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study.

BACKGROUND: 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores were useful for predicting large vessel disease, but the relationships between them and cerebral small vessel disease (CSVD) were unclear. Our study aimed to evaluate associations of 10-year ASCVD risk scores with CSVD and its magnetic resonance imaging (MRI) markers. METHODS: Community-dwelling residents from the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events study were included in this cross-sectional study. At baseline, we collected data related to the Framingham Risk Score (FRS), pooled cohort equation (PCE), prediction for ASCVD risk in China (China-PAR) and Systematic COronary Risk Evaluation model 2 (SCORE2), and classified participants into low, moderate and high groups. Participants underwent brain MRI scans. We evaluated white matter hyperintensity (WMH), lacunes, cerebral microbleeds (CMBs) and enlarged perivascular spaces in basal ganglia (BG-EPVS) according to criteria of Wardlaw and Rothwell, and calculated total CSVD score and modified total CSVD score. RESULTS: A total of 3063 participants were included, and 53.5% of them were female. A higher FRS was associated with higher total CSVD score (moderate vs. low: cOR 1.89, 95% CI 1.53-2.34; high vs. low: cOR 3.23, 95%CI 2.62-3.97), and the PCE, China-PAR or SCORE2 score was positively related to total CSVD score (P < 0.05). Moreover, higher 10-year ASCVD scores were associated with higher odds of WMH (P < 0.05), lacunes (P < 0.05), CMBs (P < 0.05) and BG-EPVS (P < 0.05). CONCLUSIONS: The 10-year ASCVD scores were positively associated with CSVD and its MRI markers. These scores provided a method of risk stratification in the population with CSVD.

Body composition markers are associated with changes in inflammatory markers but not vice versa: A bi-directional longitudinal analysis in a population-based sample.

BACKGROUND AND AIM: Growing body of evidence consistently link obesity and inflammation, Although the direction of the association is still unclear. We aimed to investigate longitudinal associations of body anthropometric, composition and fat distribution parameters with inflammatory markers and vice versa. METHOD AND RESULTS: We used data from 2464 individuals of the SHIP-TREND cohort with a median follow-up of 7 years. Linear regression models adjusted for confounders were used to analyze associations of standardized body composition markers derived from classic anthropometry, bioelectrical impedance analysis (BIA) and magnetic resonance imaging (MRI) at baseline with changes in inflammatory markers (C-reactive protein (CRP), white blood cell (WBC), fibrinogen) and vice versa. Higher level of anthropometric markers at baseline were associated with an increase in the change of inflammatory markers. A 13.5 cm higher waist circumference (WC), 16.0 kg body weight and 7.76 % relative fat mass (FM) at baseline was associated with a change in CRP of 0.52 mg/L (95 % confidence interval [CI]: 0.29 to 0.74), 0.51 mg/L (95 % CI: 0.29; 0.74) and 0.58 mg/L (95 % CI: 0.34; 0.82) respectively. Absolute FM showed the strongest association with changes in serum fibrinogen levels (ß for 8.69 kg higher FM: 0.07 g/L; 95 % CI: 0.05; 0.09). Baseline inflammatory markers were only associated with changes in hip circumference. CONCLUSION: Our study indicates the importance of anthropometric, body composition and fat distribution markers as a risk factor for the development of inflammation. To prevent inflammatory-related complications, important is to take measures against the development of obesity.

Kilogram scale facile synthesis and systematic characterization of a Gd-macrochelate as T1-weighted magnetic resonance imaging contrast agent.

To overcome the problems of commercial magnetic resonance imaging (MRI) contrast agents (CAs) (i.e., small molecule Gd chelates), we have proposed a new concept of Gd macrochelates based on the coordination of Gd3+ and macromolecules, e.g., poly(acrylic acid) (PAA). To further decrease the r2/r1 ratio of the reported Gd macrochelates that is an important factor for T1 imaging, in this study, a superior macromolecule hydrolyzed polymaleic anhydride (HPMA) was found to coordinate Gd3+. The synthesis conditions were optimized and the generated Gd-HPMA macrochelate was systematically characterized. The obtained Gd-HPMA29 synthesized in a 100 L of reactor has a r1 value of 16.35 mM-1 s-1 and r2/r1 ratio of 2.05 at 7.0 T, a high Gd yield of 92.7% and a high product weight (1074 g), which demonstrates the feasibility of kilogram scale facile synthesis. After optimization of excipients and sterilization at a high temperature, the obtained Gd-HPMA30 formulation has a pH value of 7.97, osmolality of 691 mOsmol/kg water, density of 1.145 g/mL, and viscosity of 2.2 cP at 20 â„ƒ or 1.8 cP at 37 â„ƒ, which meet all specifications and physicochemical criteria for clinical injections indicating the immense potential for clinical applications.

Reversible splenial lesion syndrome in Anti-N-methyl-D-aspartate receptor encephalitis.

Here we described an 18-year-old woman who were initially misdiagnosed as psychiatric disorders in a psychiatric institution. She was transferred to our neurological ward because of impaired consciousness. Neuronal antibody testing confirmed the diagnosis of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. Cerebral magnetic resonance imaging (MRI) revealed a concomitant disorder named reversible splenial lesion syndrome (RESLES). After administration of combined immunotherapy, the patient recovered completely 3 months after discharge. To our knowledge, co-occurrence of RESLES and anti-NMDAR encephalitis was only described in two patients with teratoma and we provide another case without teratoma. We highlight that anti-NMDAR antibodies can be added to the multiple causes of RESLES. It is therefore imperative for clinicians to detect anti-neuronal antibodies in patients with RESLES to avoid missed diagnosis.

Brain tumor detection and segmentation using deep learning.

OBJECTIVES: Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep learning-based algorithms are available for object detection; however, the performance of detection algorithms on brain tumor data has not been widely explored. Therefore, we aim to compare different object detection algorithms (Faster R-CNN, YOLO & SSD) for brain tumor detection on MRI data. Furthermore, the best-performing detection network is paired with a 2D U-Net for pixel-wise segmentation of abnormal tumor cells. MATERIALS AND METHODS: The proposed model was evaluated on the Brain Tumor Figshare (BTF) dataset, and the best-performing detection network cascaded with 2D U-Net for pixel-wise segmentation of tumors. The best-performing detection network was also fine-tuned on BRATS 2018 data to detect and classify the glioma tumor. RESULTS: For the detection of three tumor types, YOLOv5 achieved the highest mAP of 89.5% on test data compared to other networks. For segmentation, YOLOv5 combined with 2D U-Net achieved a higher DSC compared to the 2D U-Net alone (DSC: YOLOv5 + 2D U-Net = 88.1%; 2D U-Net = 80.5%). The proposed method was compared with the existing detection and segmentation network i.e. Mask R-CNN and achieved a higher mAP (YOLOv5 + 2D U-Net = 89.5%; Mask R-CNN = 67%) and DSC (YOLOv5 + 2D U-Net = 88.1%; Mask R-CNN = 44.2%). CONCLUSION: In this work, we propose a deep-learning-based method for multi-class tumor detection, classification and segmentation that combines YOLOv5 with 2D U-Net. The results show that the proposed method not only detects different types of brain tumors accurately but also delineates the tumor region precisely within the detected bounding box.

Multi-target field control for matrix gradient coils.

OBJECTIVE: Conventional single-target field control for matrix gradient coils will add control complexity in MRI spatial encoding, such as designing specialized fields and sequences. This complexity can be reduced by multi-target field control, which is realized by optimizing the coil structure according to target fields. METHODS: Based on the principle of multi-target field control, the X, Y and Z gradient fields can be set as target fields, and all coil elements can then be divided into three groups to generate these fields. An improved simulated annealing algorithm is proposed to optimize the coil element distribution of each group to generate the corresponding target field. In the improved simulated annealing process, two swapping modes are presented, and randomly selected with certain probabilities that are set to 0.25, 0.5 and 0.75, respectively. The flexibility of the final designed structure is demonstrated by a spherical harmonic basis up to the full second order with single-target field control. An experimental platform is built to measure the gradient fields generated by the designed structure with multi-target target control. RESULTS: With three probabilities of swapping modes, three similar coil element distributions are optimized, and their maximum magnetic field errors for generating X, Y and Z gradients are all below 5%. The structure selected for the final design is the one with a probability of 0.75, considering the coil performance and structural symmetry. The maximum error for all target fields generated by single-target field control is also below 5%. The experimental results show that the measured gradient fields along the axes have enough strength and high linearity. CONCLUSIONS: With the proposed improved simulated annealing algorithm and swapping modes, multi-target field control for matrix gradient coils is verified and achieved in this study by optimizing the coil element distribution. Moreover, this study provides a solution to simplify the complexity of controlling the matrix gradient coil in spatial encoding.