METHODS: In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source.
RESULTS: The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively.
CONCLUSION: Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.
MATERIALS & METHODS: Data were obtained retrospectively from all patients who underwent both CT examinations - brain (frontal bone), thorax (T7), abdomen (L3), spine (T7 & L3) or pelvis (left hip) - and DXA between 2014 and 2018 in our centre. To ensure comparability, the period between CT and DXA studies must not exceed one year. Correlations between HU values and t-scores were calculated using Pearson's correlation. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was used to determine threshold HU values for predicting osteoporosis.
RESULTS: The inclusion criteria were met by 1043 CT examinations (136 head, 537 thorax, 159 lumbar and 151 left hip). The left hip consistently provided the most robust correlations (r = 0.664-0.708, p 0.05.
CONCLUSION: HU values derived from the hip, T7 and L3 provided a good to moderate correlation to t-scores with a good prediction for osteoporosis. The suggested optimal thresholds may be used in clinical settings after external validations are performed.
METHODS: A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included.
RESULTS: Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively.
CONCLUSION: Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.
MATERIALS AND METHODS: We searched PubMed and Scopus electronic databases to identify eligible studies according to PRISMA guidelines. Studies were extracted for information on demographics, DTI changes and associations to cognitive outcomes.
RESULTS: Six studies were selected for inclusion with 110 patients (median study size: 20). 5/6 studies found significant cognitive decline and analysed relationships to DTI changes. Decreased fractional anisotropy (FA) was consistently associated with cognitive decline. Associations clustered at specific regions of cingulum and corpus callosum. Only one study conducted multivariable analysis.
CONCLUSION: Fractional anisotropy is a clinically meaningful biomarker for radiotherapy-related cognitive decline. Studies accruing larger patient cohorts are needed to guide therapeutic changes that can abate the decline.
MATERIALS AND METHODS: The HVGICs evaluated were Zirconomer [ZR] (Shofu), Equia Forte [EQ] (GC) and Riva [RV] (SDI). Sixty specimens (12mm x 2mm x 2mm) of each material were fabricated using customized Teflon molds. After initial set, the specimens were removed from their molds, finished, measured and randomly divided into 3 groups of 20. Half the specimens in each group were left uncoated while the remaining half was covered with the respective manufacturers' resin coating. The specimens were subsequently conditioned in distilled water, artificial saliva or citric acid at 37°C for 7 days. The uncoated and coated specimens (n=10) were then subjected to dynamic mechanical testing in flexure mode at 37°C with a frequency of 0.1 to 10Hz. Storage modulus, loss modulus and loss tangent data were subjected to normality testing and statistical analysis using one-way ANOVA/Scheffe's post-hoc test and Ttest at significance level p<0.05.
RESULTS: Mean storage modulus ranged from 1.39 ± 0.36 to 10.80 ± 0.86 GPa while mean loss modulus varied from 0.13 ± 0.03 to 0.70 ± 0.14 GPa after conditioning in the different mediums. Values for loss tangent ranged from 39.4 ± 7.75 to 213.2 ± 20.11 (x10 -3 ). Significant differences in visco-elastic properties were observed between mediums and materials. When conditioned in distilled water and artificial saliva,storage modulus was significantly improved when ZR, EQ and RV were uncoated. Significantly higher values were, however, observed with resin coating when the materials were exposed to citric acid.
CONCLUSION: The visco-elastic properties of HVGICs were influenced by both resin coating and chemical environment.