Evaluation of quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging in qualitative diagnosis of hepatic masses.

BACKGROUND: To explore the value of parameters of multiphase dynamic contrast-enhanced magnetic resonance imaging (MDCE-MRI) in the qualitative diagnosis of hepatic masses. METHODS: Eighty patients with hepatic masses were retrospectively analyzed. All the patients underwent MDCE-MRI at 3.0 T MR before treatment. Mean enhancement time (MET), positive enhancement integral (PEI), a maximum slope of increase (MSI), and a maximum slope of decrease (MSD) were measured. RESULTS: There were significant differences between benign and malignant hepatic masses with respect to MET, PEI, and MSI values. The PEI and MSI values between hemangiomas, hepatocellular carcinomas (HCCs), cholangiocarcinomas, and metastatic tumors had significant differences. The MSD value between metastatic tumors, HCCs, and hemangiomas were significantly different. The area under the curve (AUC) values of the receiver operator characteristic curves for MET, PEI, and MSI were 0.70, 0.72, and 0.80, respectively. The specificity of MET, PEI, and MSI were all 77%, and the sensitivities of MSI was the highest, of which was 82.40%. Logistic regression analysis showed the regression equation to be P = 1/[1 + e0.008 × 1 + 0.007 × 2-6.707], and taking the Youden index maximum points as a diagnostic point was 0.2946. CONCLUSION: Some parameters of MDCE-MRI have significant roles in differentiating hepatic masses.

A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma.

OBJECTIVE: The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome. METHODS: A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC. RESULTS: Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P < 0.050). Patients with high Nomo-score tumors were 2.669 times more likely to suffer recurrence than those with low Nomo-score tumors (HR, 2.669, P < 0.001). CONCLUSIONS: The CT-based RN performed well in predicting both the histologic grade and outcome of CS.

Corrigendum: Differences in resting-state brain networks and gray matter between APOE ϵ2 and APOE ϵ4 carriers in non-dementia elderly.

[This corrects the article DOI: 10.3389/fpsyt.2023.1197987.].

Differences in resting-state brain networks and gray matter between APOE ε2 and APOE ε4 carriers in non-dementia elderly.

Background: Apolipoprotein E (APOE) ε2 and APOE ε4 are the most distinct alleles among the three APOE alleles, both structurally and functionally. However, differences in cognition, brain function, and brain structure between the two alleles have not been comprehensively reported in the literature, especially in non-demented elderly individuals. Methods: A neuropsychological test battery was used to evaluate the differences in cognitive performance in five cognitive domains. Independent component analysis (ICA) and voxel-based morphometry (VBM) were used separately to analyze resting-state functional magnetic resonance imaging (rs-fMRI) data and the structure MRI data between the two groups. Finally, correlations between differential brain regions and neuropsychological tests were calculated. Results: APOE ε2 carriers had better cognitive performance in general cognitive, memory, attention, and executive function than APOE ε4 carriers (all p < 0.05). In ICA analyses of rs-fMRI data, the difference in the resting-state functional connectivity (rsFC) between two groups is shown in 7 brain networks. In addition, VBM analyses of the T1-weighted image revealed that APOE ε2 carriers had a larger thalamus and right postcentral gyrus volume and a smaller bilateral putamen volume than APOE ε4 carriers. Finally, differences in brain function and structure may be might be the reason that APOE ε2 carriers are better than APOE ε4 carriers in cognitive performance. Conclusion: These findings suggest that there are significant differences in brain function and structure between APOE ε2 carriers and APOE ε4 carriers, and these significant differences are closely related to their cognitive performance.

Ultrahigh-Frequency Surface Acoustic Wave Sensors with Giant Mass-Loading Effects on Electrodes.

Surface acoustic wave (SAW) devices are widely used for physical, chemical, and biological sensing applications, and their sensing mechanisms are generally based on frequency changes due to mass-loading effects at the acoustic wave propagation area between two interdigitated transducers (IDTs). In this paper, a new sensing mechanism has been proposed based on a significantly enhanced mass-loading effect generated directly on Au IDT electrodes, which enables significantly enhanced sensitivity, compared with that of conventional SAW devices. The fabricated ultrahigh-frequency SAW devices show a significant mass-loading effect on the electrodes. When the Au-electrode thickness increased from 12 to 25 nm, the Rayleigh mode resonant frequency decreased from 7.77 to 5.93 GHz, while that of the higher longitudinal leaky SAW decreased from 11.87 to 9.83 GHz. The corresponding mass sensitivity of 7309 MHz·mm2·µg-1 (Rayleigh mode) is ∼8.9 × 1011 times larger than that of a conventional quartz crystal balance (with a frequency of 5 MHz) and ∼1000 times higher than that of conventional SAW devices (with a frequency of 978 MHz). Trinitrotoluene concentration as low as 4.4 × 10-9 M (mol·L-1) can be detected using the fabricated SAW sensor, proving its giant mass-loading effect and ultrahigh sensitivity.

The Value of Diffusion-Weighted Magnetic Resonance Imaging in Predicting the Efficacy of Radiation and Chemotherapy in Cervical Cancer.

BACKGROUND: To analyze the application value of apparent diffusion coefficient (ADC) and exponent apparent diffusion coefficient (EADC) in evaluating the efficacy of radiation and chemotherapy in cervical cancer using pre- and posttreatment diffusion-weighted magnetic resonance imaging (DW-MRI) scans. METHODS: 52 patients with cervical cancer were administered radiation and chemotherapy. Both MRI and DW-MRI were obtained at different stages. The ADC and EADC values, as well as the maximum tumor diameter, were measured and analyzed. RESULTS: We found that the ADC value increased after treatment, and the EADC value decreased. Changes in the calculated ADC occurred earlier than the morphologic changes of the tumors. A negative correlation was detected between reduced rates in the maximum tumor diameter two months after treatment and pretreatment ADC value (r = -0.658, P < 0.05). An ROC curve and nonlinear regression analysis showed that the formula, y = (1525500.122x2 - 4689.962x + 3.482) × 100%, can be used to calculate the percentage of complete remission after treatment according to the pretreatment ADC value. CONCLUSION: Our data suggest that pretreatment ADC and EADC values are predictive of the efficacy of radiation and chemotherapy. Both ADC and EADC values during treatment were instrumental in early monitoring and dynamic observation.