NBS-SNI, an extension of the network-based statistic: Abnormal functional connections between important structural actors

Elucidating the coupling between the structure and the function of the brain and its development across maturation has attracted a lot of interest in the field of network neuroscience in the last 15 years. Mounting evidence supports the hypothesis that the onset of certain brain disorders is linked with the interplay between the structural architecture of the brain and its functional processes, often accompanied with unusual connectivity features. This paper introduces a method called the network-based statistic-simultaneous node investigation (NBS-SNI) that integrates both representations into a single framework, and identifies connectivity abnormalities in case-control studies. With this method, significance is given to the properties of the nodes, as well as to their connections. This approach builds on the well-established network-based statistic (NBS) proposed in 2010. We uncover and identify the regimes in which NBS-SNI offers a gain in statistical resolution to identify a contrast of interest using synthetic data. We also apply our method on two real case-control studies, one consisting of individuals diagnosed with autism and the other consisting of individuals diagnosed with early psychosis. Using NBS-SNI and node properties such as the closeness centrality and local information dimension, we found hypo- and hyperconnected subnetworks and show that our method can offer a 9 percentage points gain in prediction power over the standard NBS.

Vaginal stenosis in women with cervical or endometrial cancer after pelvic radiotherapy: a cross-sectional study of vaginal measurements, risk for sexual dysfunction and quality of life

INTRODUCTION AND HYPOTHESIS

Radiotherapy (RT) for cervical (CC) and endometrial cancer (EC) is known to lead to vaginal stenosis (VS), but the comparison between vaginal anatomical measurements and the risk of sexual dysfunction presents a wide variety of results among the literature. Thus, we sought to assess the prevalence of VS, vaginal measurements, sexual dysfunction and QOL in women with CC and EC submitted to pelvic RT with or without previous surgery.

METHODS

Cross-sectional study that included 61 women with CC and 69 with EC. VS was classified by the Common Terminology Criteria for Adverse Effects version 5.0 (CTCAE v5.0), sexual function by the validated Female Sexual Function Index (FSFI) and QOL by the validated World Health Organization questionnaire (WHOQOL-BREF). Acrylic cylinders were used for vaginal measurements. Uni-/multivariate analyses to address factors associated with VC in both groups were performed.

RESULTS

The prevalence of VS was 79% and 67% within patients with CC and EC, respectively. Vagina length was decreased in both groups without statistical difference (7.2 ± 1.7 vs. 6.6 ± 1.8;p = 0.072). Vaginal diameter was significantly higher (p = 0.047) in women with EC (25.4 ± 6.3) than in those with CC (23.1 ± 5.7). Sexual dysfunction was highly prevalent for both CC and EC (88% vs. 91%; p = 0.598). There was no difference in all WHOQOL-BREF domains between women with CC and EC.

CONCLUSIONS

VS is highly prevalent in CC and EC patients, with vaginal length decreased in both groups but with a higher vaginal diameter in those with EC. Nevertheless, sexual dysfunction is highly prevalent in both groups.

Towards safe and efficient preoperative planning of transcatheter mitral valve interventions

OBJECTIVE OF THE STUDY

Transcatheter mitral valve interventions are emerging as a viable alternative for patients at high risk. Two key aspects are crucial during the preoperative planning: left ventricular outflow tract assessment and anatomical analysis. Given that the manual anatomical analysis is time-consuming, an automated approach may introduce efficiency during preoperative planning. In this study, we present an automatic method to detect the mitral valve annulus and discuss possible implementation of this method in clinical practice.

PATIENTS

This retrospective study used the data of 71 patients collected from multiple centra. The mean age of this cohort was 74.2±13.1 years, and 56.1% of the patients were female and 43.9% male.

MATERIALS AND METHODS

We trained three deep learning models to segment the area around the mitral valve annulus. In a post-processing step, we extracted the mitral valve annulus from this segmentation. As a final step, clinically relevant measurements such as 2D perimeter, trigone-to-trigone (TT) distance, septal-to-lateral (SL) distance and commissure-to-commissure (IC) distance were derived from the predicted mitral valve annulus. The method was cross-validated with k-folding.

RESULTS

The predicted measurements showed excellent correlation with the manually obtained clinical measurements: 2D perimeter: R²=0.93, TT-distance: R²=0.86, SL-distance: R²=0.86 and IC-distance: R²=0.90. The total analysis time per patient of the automatic method was less than 1 second, which is an enormous speed-up as compared to the manual process (25minutes).

CONCLUSION

The efficiency and accuracy of the proposed method give the confidence to move towards implementation of this technology in clinical practice. We propose a possible implementation of this method in clinical practice, which, in our opinion, will facilitate safe and efficient preoperative planning of transcatheter mitral valve interventions.