Study of T2 mapping in quantifying and discriminating uterine lesions under different magnetic field strengths: 1.5 T vs. 3.0 T

Intravoxel incoherent motion (IVIM)-derived perfusion fraction mapping for the visual evaluation of MR-guided high intensity focused ultrasound (MR-HIFU) ablation of uterine fibroids

BACKGROUND

A method for periprocedural contrast agent-free visualization of uterine fibroid perfusion could potentially shorten magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) treatment times and improve outcomes. Our goal was to test feasibility of perfusion fraction mapping by intravoxel incoherent motion (IVIM) modeling using diffusion-weighted MRI as method for visual evaluation of MR-HIFU treatment progression.

METHODS

Conventional and T2-corrected IVIM-derived perfusion fraction maps were retrospectively calculated by applying two fitting methods to diffusion-weighted MRI data (b = 0, 50, 100, 200, 400, 600 and 800 s/mm2 at 1.5 T) from forty-four premenopausal women who underwent MR-HIFU ablation treatment of uterine fibroids. Contrast in perfusion fraction maps between areas with low perfusion fraction and surrounding tissue in the target uterine fibroid immediately following MR-HIFU treatment was evaluated. Additionally, the Dice similarity coefficient (DSC) was calculated between delineated areas with low IVIM-derived perfusion fraction and hypoperfusion based on CE-T1w.

RESULTS

Average perfusion fraction ranged between 0.068 and 0.083 in areas with low perfusion fraction based on visual assessment, and between 0.256 and 0.335 in surrounding tissues (all p < 0.001). DSCs ranged from 0.714 to 0.734 between areas with low perfusion fraction and the CE-T1w derived non-perfused areas, with excellent intraobserver reliability of the delineated areas (ICC 0.97).

CONCLUSION

The MR-HIFU treatment effect in uterine fibroids can be visualized using IVIM perfusion fraction mapping, in moderate concordance with contrast enhanced MRI. IVIM perfusion fraction mapping has therefore the potential to serve as a contrast agent-free imaging method to visualize the MR-HIFU treatment progression in uterine fibroids.

PaCMAP-embedded convolutional neural network for multi-omics data integration

Aims

The multi-omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential for enhancing predictive models. The main motivation behind this study stems from the imperative need to advance prognostic methodologies in cancer diagnosis, an area where precision is pivotal for effective clinical decision-making. In this context, the present study introduces an innovative methodology that integrates copy number alteration (CNA), DNA methylation, and gene expression data.

Methods

The three omics data were successfully merged into a two-dimensional (2D) map using the PaCMAP dimensionality reduction technique. Utilizing the RGB coloring scheme, a visual representation of the integration was produced utilizing the values of the three omics of each sample. Then, the colored 2D maps were fed into a convolutional neural network (CNN) to forecast the Gleason score.

Results

Our proposed model outperforms the cutting-edge i-SOM-GSN model by integrating multi-omics data and the CNN architecture with an accuracy of 98.89, and AUC of 0.9996.

Conclusion

This study demonstrates the effectiveness of multi-omics data integration in predicting health outcomes. The proposed methodology, combining PaCMAP for dimensionality reduction, RGB coloring for visualization, and CNN for prediction, offers a comprehensive framework for integrating heterogeneous omics data and improving predictive accuracy. These findings contribute to the advancement of personalized medicine and have the potential to aid in clinical decision-making for prostate cancer patients.

Detection and Quantitative Assessment of Arthroscopically Proven Long Biceps Tendon Pathologies Using T2 Mapping

This study evaluates how far T2 mapping can identify arthroscopically confirmed pathologies in the long biceps tendon (LBT) and quantify the T2 values in healthy and pathological tendon substance. This study comprised eighteen patients experiencing serious shoulder discomfort, all of whom underwent magnetic resonance imaging, including T2 mapping sequences, followed by shoulder joint arthroscopy. Regions of interest were meticulously positioned on their respective T2 maps, capturing the sulcal portion of the LBT and allowing for the quantification of the average T2 values. Subsequent analyses included the calculation of diagnostic cut-off values, sensitivities, and specificities for the detection of tendon pathologies, and the calculation of inter-reader correlation coefficients (ICCs) involving two independent radiologists. The average T2 value for healthy subjects was measured at 23.3 ± 4.6 ms, while patients with tendinopathy displayed a markedly higher value, at 47.9 ± 7.8 ms. Of note, the maximum T2 value identified in healthy tendons (29.6 ms) proved to be lower than the minimal value measured in pathological tendons (33.8 ms), resulting in a sensitivity and specificity of 100% (95% confidence interval 63.1-100) across all cut-off values ranging from 29.6 to 33.8 ms. The ICCs were found to range from 0.93 to 0.99. In conclusion, T2 mapping is able to assess and quantify healthy LBTs and can distinguish them from tendon pathology. T2 mapping may provide information on the (ultra-)structural integrity of tendinous tissue, facilitating early diagnosis, prompt therapeutic intervention, and quantitative monitoring after conservative or surgical treatments of LBT.

The Role of Native T1 and T2 Mapping Times in Identifying PD-L1 Expression and the Histological Subtype of NSCLCs

We investigated the association of T1/T2 mapping values with programmed death-ligand 1 protein (PD-L1) expression in lung cancer and their potential in distinguishing between different histological subtypes of non-small cell lung cancers (NSCLCs). Thirty-five patients diagnosed with stage III NSCLC from April 2021 to December 2022 were included. Conventional MRI sequences were acquired with a 1.5 T system. Mean T1 and T2 mapping values were computed for six manually traced ROIs on different areas of the tumor. Data were analyzed through RStudio. Correlation between T1/T2 mapping values and PD-L1 expression was studied with a Wilcoxon-Mann-Whitney test. A Kruskal-Wallis test with a post-hoc Dunn test was used to study the correlation between T1/T2 mapping values and the histological subtypes: squamocellular carcinoma (SCC), adenocarcinoma (ADK), and poorly differentiated NSCLC (PD). There was no statistically significant correlation between T1/T2 mapping values and PD-L1 expression in NSCLC. We found statistically significant differences in T1 mapping values between ADK and SCC for the periphery ROI (p-value 0.004), the core ROI (p-value 0.01), and the whole tumor ROI (p-value 0.02). No differences were found concerning the PD NSCLCs.