The Current Diagnostic Performance of MRI-Based Radiomics for Glioma Grading: A Meta-Analysis.

BACKGROUND: Multiple radiomics models have been proposed for grading glioma using different algorithms, features, and sequences of magnetic resonance imaging. The research seeks to assess the present overall performance of radiomics for grading glioma. METHODS: A systematic literature review of the databases Ovid MEDLINE PubMed, and Ovid EMBASE for publications published on radiomics for glioma grading between 2012 and 2023 was performed. The systematic review was carried out following the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analysis. RESULTS: In the meta-analysis, a total of 7654 patients from 40 articles, were assessed. R-package mada was used for modeling the joint estimates of specificity (SPE) and sensitivity (SEN). Pooled event rates across studies were performed with a random-effects meta-analysis. The heterogeneity of SPE and SEN were based on the χ2 test. Overall values for SPE and SEN in the differentiation between high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were 84% and 91%, respectively. With regards to the discrimination between World Health Organization (WHO) grade 4 and WHO grade 3, the overall SPE was 81% and the SEN was 89%. The modern non-linear classifiers showed a better trend, whereas textural features tend to be the best-performing (29%) and the most used. CONCLUSIONS: Our findings confirm that present radiomics' diagnostic performance for glioma grading is superior in terms of SEN and SPE for the HGGs vs. LGGs discrimination task when compared to the WHO grade 4 vs. 3 task.

Long-Term Prognostic Value of Automated Measurements in Nuclear Cardiology: Comparisons with Expert Scoring.

Background and Objectives: Automated methods for the analysis of myocardial perfusion studies have been incorporated into clinical practice, but they are currently used as adjuncts to the visual interpretation. We aimed to investigate the role of automated measurements of summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) as long-term prognostic markers of morbidity and mortality, in comparison to the prognostic value of expert reading. Materials and Methods: The study was conducted at the Nuclear Medicine Laboratory of the University of Thessaly, in Larissa, Greece. A total of 378 consecutive patients with known or suspected coronary artery disease were enrolled in the study. All participants were referred to our laboratory for the performance of stress/rest myocardial perfusion single photon emission computed tomography. Automated measurements of SSS, SRS, and SDS were obtained by Emory Cardiac Toolbox (ECTb (Version 3.0), Emory University, Atlanta, GA, USA), Myovation (MYO, Xeleris version 3.05, GE Healthcare, Chicago, IL, USA), and Quantitative Perfusion SPECT (QPS (Version 4.0), Cedars-Sinai Medical Center, Los Angeles, CA, USA) software packages. Follow-up data were recorded after phone contacts, as well as through review of hospital records. Results: Expert scoring of SSS and SDS had significantly greater prognostic ability in comparison to all software packages (p < 0.001 for all comparisons). Similarly, ECTb-obtained SRS measurements had significantly lower prognostic ability in comparison to expert scoring (p < 0.001), while expert scoring of SRS showed significantly higher prognostic ability compared to MYO (p = 0.018) and QPS (p < 0.001). Conclusions: Despite the useful contribution of automated analyses in the interpretation of myocardial perfusion studies, expert reading should continue to have a crucial role, not only in clinical decision making, but also in the assessment of prognosis.

Combination of fusion guided multiparametric MRI-transrectal US with systematic biopsy of the prostate for the detection of clinically significant prostate cancer: A prospective single-center study.

PURPOSE: To investigate the diagnostic efficacy of fusion guided multiparametric MRI (mpMRI)-transrectal ultrasound (TRUS) biopsy versus systematic biopsy of the prostate in patients with suspicion of prostate cancer. METHODS: A total of 185 patients with PI-RADS 3 lesions or higher underwent fusion guided targeted and systematic prostate biopsy. Histology of samples was correlated with PI-RADS score and biopsy method for each patient. RESULTS: A total of 81/185 (43.8%) cases positive for cancer were detected; 23/81 (28.4%) cases with clinically insignificant prostate cancer-insPCa and 58/81 (71.6%) cases with clinically significant prostate cancer-csPCa. There was a statistically significant difference in the overall detection of adenocarcinomas between methods (p = .035, McNemar test). Moreover, there was a statistically significant difference in the detection of insPCa between the two methods (p = .004, McNemar test). Systematic biopsy detected 13 patients with insPCa more (14.4%) than the targeted biopsy method. However, there is no statistical difference in the detection rate of csPCa between the two methods (p = 1, McNemar test). When both techniques were combined more cases of csPCa were detected. CONCLUSION: The combined implementation of fusion-guided targeted mpMRI-TRUS and systematic biopsy of the prostate provides higher detection number of csPCa, compared to each method alone. The potential of fusion-guided mpMRI-TRUS biopsy of the prostate needs to be further assessed since each method has its limitations; therefore, systematic prostate biopsy still plays an important role in clinical practice.

Automated Analysis vs. Expert Reading in Nuclear Cardiology: Correlations with the Angiographic Score.

Background and Objectives: Myocardial perfusion imaging (MPI) has an important role in the non-invasive investigation of coronary artery disease. The interpretation of MPI studies is mainly based on the visual evaluation of the reconstructed images, while automated quantitation methods may add useful data for each patient. However, little evidence is currently available regarding the actual incremental clinical diagnostic performance of automated MPI analysis. In the present study, we aimed to assess the correlation between automated measurements of Summed Stress Score (SSS), Summed Rest Score (SRS) and Summed Difference Score (SDS), with the corresponding expert reading values, using coronary angiography as the gold standard. Materials and Methods: The study was conducted at the Nuclear Medicine Laboratory of the University Hospital of Larissa, Larissa, Greece, οver an one-year period (January 2019-January 2020). 306 patients, with known or suspected coronary artery disease, were enrolled in the study. Each participant underwent a coronary angiography, prior to or after the scintigraphic study (within a three-month period). Either symptom-limited treadmill test, or pharmacologic testing using adenosine or regadenoson, was performed in all participants, and the scintigraphic studies were carried out using technetium 99m (99mTc) tetrofosmin (one-day stress/rest protocol). Coronary angiographies were scored according to a 4-point scoring system (angiographic score; O: normal study, 1: one-vessel disease, 2: two-vessel disease, 3: three-vessel disease). Moreover, automated measurements of SSS, SRS and SDS were derived by three widely available software packages (Emory Cardiac Toolbox, Myovation, Quantitative Perfusion SPECT). Results: Interclass Correlation Coefficients of SSS, SRS and SDS between expert reading and software packages were moderate to excellent. Visually defined SSS, SRS and SDS were significantly correlated with the corresponding results of all software packages. However, visually defined SSS, SRS and SDS were more strongly correlated with the angiographic score, indicating a better performance of expert reading when compared to automated analysis. Conclusions: Based on our results, visual evaluation continues to have a crucial role for the interpretation of MPI images. Software packages can provide automated measurements of several parameters, particularly contributing to the investigation of cases with ambiguous scintigraphic findings.

Neurotransmitter receptor densities are associated with changes in regional Cerebral blood flow during clinical ongoing pain.

Arterial spin labelling (ASL) plays an increasingly important role in neuroimaging pain research but does not provide molecular insights regarding how regional cerebral blood flow (rCBF) relates to underlying neurotransmission. Here, we integrate ASL with positron emission tomography (PET) and brain transcriptome data to investigate the molecular substrates of rCBF underlying clinically relevant pain states. Two data sets, representing acute and chronic ongoing pain respectively, were utilised to quantify changes in rCBF; one examining pre-surgical versus post-surgical pain, and the second comparing patients with painful hand Osteoarthritis to a group of matched controls. We implemented a whole-brain spatial correlation analysis to explore associations between change in rCBF (ΔCBF) and neurotransmitter receptor distributions derived from normative PET templates. Additionally, we utilised transcriptomic data from the Allen Brain Atlas to inform distributions of receptor expression. Both datasets presented significant correlations of ΔCBF with the µ-opioid and dopamine-D2 receptor expressions, which play fundamental roles in brain activity associated with pain experiences. ΔCBF also correlated with the gene expression distributions of several receptors involved in pain processing. Overall, this is the first study illustrating the molecular basis of ongoing pain ASL indices and emphasises the potential of rCBF as a biomarker in pain research.

Anosognosia in Dementia: Evaluation of Perfusion Correlates Using 99mTc-HMPAO SPECT and Automated Brodmann Areas Analysis.

(1) Background: Considerable inconsistency exists regarding the neural substrates of anosognosia in dementia in previous neuroimaging studies. The purpose of this study was the evaluation of anosognosia perfusion correlates across various types of dementia using automated Brodmann areas (BAs) analysis and comparison with a database of normal subjects. (2) Methods: We studied 72 patients: 32 with Alzheimer's disease, 26 with frontotemporal dementia-FTD (12 behavioral FTD, 9 semantic FTD, 5 Progressive Non-Fluent Aphasia), 11 with corticobasal syndrome, and 3 with progressive supranuclear palsy. Addenbrook's Cognitive Examination-Revised (ACE-R) mean(±SD) was 55.6(±22.8). For anosognosia measurement, the Anosognosia Questionnaire-Dementia was used. Total anosognosia score mean(±SD) was 22.1(±17.9), cognitive anosognosia score mean(±SD) was 18.1(±15.1) and behavioral-mood anosognosia score mean(±SD) was 3.3(±4.7). (3) Results: Higher anosognosia total score was associated with hypoperfusion in the inferior temporal, anterior cingulate, and inferior frontal cortices of the right hemisphere (BAs 20R, 24R, 32R, 45R). Higher anosognosia cognitive score was correlated with hypoperfusion in the left middle and anterior temporal cortices, and right dorsal anterior cingulate cortex (BAs 21L, 22L, 32R). No association was found with behavioral-mood anosognosia. (4) Conclusions: Automated analysis of brain perfusion Single Photon Emission Computed Tomography could be useful for the investigation of anosognosia neural correlates in dementia.

Breast Cancer Classification on Multiparametric MRI - Increased Performance of Boosting Ensemble Methods.

Introduction: This study aims to assess the utility of Boosting ensemble classification methods for increasing the diagnostic performance of multiparametric Magnetic Resonance Imaging (mpMRI) radiomic models, in differentiating benign and malignant breast lesions. Methods: The dataset includes mpMR images of 140 female patients with mass-like breast lesions (70 benign and 70 malignant), consisting of Dynamic Contrast Enhanced (DCE) and T2-weighted sequences, and the Apparent Diffusion Coefficient (ADC) calculated from the Diffusion Weighted Imaging (DWI) sequence. Tumor masks were manually defined in all consecutive slices of the respective MRI volumes and 3D radiomic features were extracted with the Pyradiomics package. Feature dimensionality reduction was based on statistical tests and the Boruta wrapper. Hierarchical Clustering on Spearman's rank correlation coefficients between features and Random Forest classification for obtaining feature importance, were implemented for selecting the final feature subset. Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) classifiers, were trained and tested with bootstrap validation in differentiating breast lesions. A Support Vector Machine (SVM) classifier was also exploited for comparison. The Receiver Operator Characteristic (ROC) curves and DeLong's test were utilized to evaluate the classification performances. Results: The final feature subset consisted of 5 features derived from the lesion shape and the first order histogram of DCE and ADC images volumes. XGboost and LGBM achieved statistically significantly higher average classification performances [AUC = 0.95 and 0.94 respectively], followed by Adaboost [AUC = 0.90], GB [AUC = 0.89] and SVM [AUC = 0.88]. Conclusion: Overall, the integration of Ensemble Learning methods within mpMRI radiomic analysis can improve the performance of computer-assisted diagnosis of breast cancer lesions.

Differential contributions of serotonergic and dopaminergic functional connectivity to the phenomenology of LSD.

RATIONALE: LSD is the prototypical psychedelic. Despite a clear central role of the 5HT2a receptor in its mechanism of action, the contributions of additional receptors for which it shows affinity and agonist activity remain unclear. OBJECTIVES: We employed receptor-enriched analysis of functional connectivity by targets (REACT) to explore differences in functional connectivity (FC) associated with the distributions of the primary targets of LSD-the 5HT1a, 5HT1b, 5HT2a, D1 and D2 receptors. METHODS: We performed secondary analyses of an openly available dataset (N = 15) to estimate the LSD-induced alterations in receptor-enriched FC maps associated with these systems. Principal component analysis (PCA) was employed as a dimension reduction strategy for subjective experiences associated with LSD captured by the Altered States of Consciousness (ASC) questionnaire. Correlations between these principal components as well as VAS ratings of subjective effects with receptor-enriched FC were explored. RESULTS: Compared to placebo, LSD produced differences in FC when the analysis was enriched with each of the primary serotonergic and dopaminergic receptors. Altered receptor-enriched FC showed relationships with the subjective effects of LSD on conscious experience, with serotonergic and dopaminergic systems being predominantly associated with perceptual effects and perceived selfhood as well as cognition respectively. These relationships were dissociable, with different receptors showing the same relationships within, but not between, the serotonergic and dopaminergic systems. CONCLUSIONS: These exploratory findings provide new insights into the pharmacology of LSD and highlight the need for additional investigation of non-5HT2a-mediated mechanisms.

PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer.

BACKGROUND AND OBJECTIVE: Prostate cancer (PCa) is a severe public health issue and the most common cancer worldwide in men. Early diagnosis can lead to early treatment and long-term survival. The addition of the multiparametric magnetic resonance imaging in combination with ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate cancer detection. Use of both tools gradually increases in every day urological practice. Furthermore, advances in the area of information technology and artificial intelligence have led to the development of software platforms able to support clinical diagnosis and decision-making using patient data from personalized medicine. METHODS: We investigated the current aspects of implementation, architecture, and design of a health care information system able to handle and store a large number of clinical examination data along with medical images, and produce a risk calculator in a seamless and secure manner complying with data security/accuracy and personal data protection directives and standards simultaneously. Furthermore, we took into account interoperability support and connectivity to legacy and other information management systems. The platform was implemented using open source, modern frameworks, and development tools. RESULTS: The application showed that software platforms supporting patient follow-up monitoring can be effective, productive, and of extreme value, while at the same time, aiding toward the betterment medicine clinical workflows. Furthermore, it removes access barriers and restrictions to specialized care, especially for rural areas, providing the exchange of medical images and patient data, among hospitals and physicians. CONCLUSION: This platform handles data to estimate the risk of prostate cancer detection using current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary and intersectoral approaches. This work offers the research community an open architecture framework that encourages the broader adoption of more robust and comprehensive systems in standard clinical practice.

Differences of apathy perfusion correlates between Alzheimer's disease and frontotemporal dementia. A 99mTc-HMPAO SPECT study with automated Brodmann areas analysis.

OBJECTIVES: To explore differences of apathy perfusion correlates between Alzheimer's disease (AD) and Frontotemporal dementia (FTD) using perfusion SPECT. METHODS: We studied 75 FTD and 66 AD patients. We evaluated apathy using Neuropsychiatric Inventory (NPI). We compared perfusion of BAs on left (L) and right (R) hemisphere in AD and FTD. RESULTS: Apathy in AD was significantly and negatively correlated with dorsolateral prefrontal cortex bilaterally, right anterior prefrontal cortex, inferior frontal cortex bilaterally, especially on the right, orbital part of inferior frontal gyrus bilaterally, left dorsal anterior cingulate cortex, right primary and secondary visual cortex, and with bilateral anterior and dorsolateral prefrontal cortex, inferior frontal cortex and orbital part of inferior frontal gyrus, bilaterally, bilateral anterior -ventral and dorsal- cingulate cortex, left posterior ventral cingulate cortex, right inferior, middle and anterior temporal gyri, entorhinal and parahippocampal cortex in FTD. CONCLUSIONS: Significant overlapping of apathy perfusion correlates between AD and FTD is seen in frontal areas and anterior cingulate. Right occipital cortex is also involved in AD, while right temporal cortex and left posterior cingulate are involved in FTD. Nuclear imaging could be a useful biomarker for revealing apathy underlying mechanisms, resulting in directed treatments.KEYPOINTSUnderlying neural networks and clinical manifestation of apathy may differ between AD and FTD.Apathy in AD is correlated with hypoperfusion in bilateral frontal areas, more prominent on the right, left anterior cingulate and right occipital cortex.Apathy in FTD is correlated with hypoperfusion in bilateral frontal areas, bilateral anterior cingulate, left posterior cingulate and right temporal cortex.Brain perfusion SPECT with automated BAs analysis and comparison with normal healthy subjects may provide significant information for apathy mechanisms in neurodegenerative disorders, affecting patients' treatment.

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review.

Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

Incorporating diffusion-weighted imaging in a diagnostic algorithm for multiparametric MR mammography.

BACKGROUND: Apparent diffusion coefficient (ADC) measurements are not incorporated in BI-RADS classification. PURPOSE: To assess the probability of malignancy of breast lesions at magnetic resonance mammography (MRM) at 3 T, by combining ADC measurements with the BI-RADS score, in order to improve the specificity of MRM. MATERIAL AND METHODS: A total of 296 biopsy-proven breast lesions were included in this prospective study. MRM was performed at 3 T, using a standard protocol with dynamic sequence (DCE-MRI) and an extra echo-planar diffusion-weighted sequence. A freehand region of interest was drawn inside the lesion, and ADC values were calculated. Each lesion was categorized according to the BI-RADS classification. Logistic regression analysis was employed to predict the probability of malignancy of a lesion. The model combined the BI-RADS classification and the ADC value. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated. RESULTS: In total, 153 malignant and 143 benign lesions were analyzed; 257 lesions were masses and 39 lesions were non-mass-like enhancements. The sensitivity and specificity of the combined method were 96% and 86%, respectively, in contrast to 95% and 81% with BI-RADS classification alone. CONCLUSION: We propose a method of assessing the probability of malignancy in breast lesions by combining BI-RADS score and ADC values into a single formula, increasing sensitivity and specificity compared to BI-RADS classification alone.

Correlation of Neuropsychiatric Symptoms in Dementia with Brain Perfusion: A 99mTc-SPECT-HMPAO Study with Brodmann Areas Analysis.

BACKGROUND: Neuropsychiatric symptoms (NPSs) are common in dementia. Their evaluation is based on Neuropsychiatric Inventory (NPI). Neuroimaging studies have tried to elucidate the underlying neural circuits either in isolated NPSs or in specific forms of dementia. OBJECTIVE: The objective of this study is to evaluate the correlation of NPS in the NPI with Brodmann areas (BAs) perfusion, for revealing BAs involved in the pathogenesis of NPSs in dementia of various etiologies. METHODS: We studied 201 patients (82 with Alzheimer's disease, 75 with Frontotemporal dementia, 27 with Corticobasal Syndrome, 17 with Parkinson Disease/Lewy Body Dementia). Exploratory factor analysis was carried out to evaluate underlying groups of BAs, and Principal Component analysis was chosen as extraction method using Varimax rotation. Partial correlation coefficients were computed to explore the association of factors obtained from analysis and NPI items controlling for age, educational yeas, and ACE-R. RESULTS: We found 6 BAs Factors(F); F1 (BAs 8,9,10,11,24,32,44,45,46,47, bilaterally), F2 (BAs 4,5,6,7,23,31, bilaterally), F3 (BAs 19,21,22,37,39,40, bilaterally), F4 (BAs 20,28,36,38, bilaterally), F5 (BAs 25, bilaterally) and F6 (BAs 17,18, bilaterally). Significant and negative correlation was found between NPI1 (delusions) and F3,F6, NPI2 (hallucinations) and F6, NPI7 (apathy) and F1,F4,F5, NPI3 (agitation) - NPI10 (aberrant motor behavior) - NPI12 (eating disorders) and F1. We did not find any significant correlation for NPI4,5,6,8,9,11 (depression, anxiety, euphoria, disinhibition, irritability, sleep disorders, respectively). CONCLUSION: Several NPSs share the same BAs among different types of dementia, while the manifestation of the rest may be attributed to different neural networks. These findings may have an impact on patients' treatment.

A facile approach to prepare silica hybrid, spin-crossover water-soluble nanoparticles as potential candidates for thermally responsive MRI agents.

A reverse micelle method was used for the synthesis of water-soluble silica hybrid, spin-crossover (SCO) nanoparticles (NPs). MRI experiments provided temperature dependent T2 values, indicating their potential use as smart MRI agents, while lyophilization of NP dispersions in water yielded powders with a preserved but modified thermal hysteretic magnetic profile.

Eating Disorders in Frontotemporal Dementia and Alzheimer's Disease: Evaluation of Brain Perfusion Correlates Using 99mTc-HMPAO SPECT with Brodmann Areas Analysis.

BACKGROUND: Eating disorders (ED) in dementia represent a significant impairment affecting patients' and caregivers' lives. In frontotemporal dementia (FTD), ED include overeating, sweet food preference, stereotypical eating, and hyperorality, while in Alzheimer's disease (AD), anorexia and appetite loss are the most common ED. OBJECTIVE: The aim of our study was to highlight Brodmann areas (BAs) implicated specifically in the appearance of ED in FTD and AD. METHODS: We studied 141 patients, 75 with FTD and 66 with AD. We used the NeuroGamTM software on the reconstructed single photon emission computed tomography-SPECT data for the automated comparison of BAs perfusion on the left (L) and right (R) hemisphere with perfusion in corresponding BAs of a normal database. RESULTS: The FTD group included 27 men and 48 women, age (mean±SD) 65.8±8.5 years, duration of disease 3.4±3.3 years, Mini-Mental State Examination (MMSE) 17.9±8.6, ED score on Neuropsychiatric Inventory (NPI) 4.7±8.5. ED in FTD were correlated with hypoperfusion in right anterior and dorsolateral prefrontal cortices (BAs 10R, 46R), left orbitofrontal cortex (BA 12L), orbital part of the right inferior frontal gyrus (BA 47R), and left parahippocampal gyrus (BA 36L). The AD group included 21 men and 45 women, age (mean±SD) 70.2±8.0 years, duration of disease 3.3±2.4 years, MMSE 20.2±6, ED-NPI score 2.7±3.9. ED in AD were correlated with hypoperfusion in left inferior temporal cortex (BA 20L). CONCLUSION: SPECT imaging with automated mapping of brain cortex could contribute to the understanding of the neural networks involved in the manifestation of ED in dementia.