Overall Survival Prediction of Glioma Patients With Multiregional Radiomics.

Radiomics-guided prediction of overall survival (OS) in brain gliomas is seen as a significant problem in Neuro-oncology. The ultimate goal is to develop a robust MRI-based approach (i.e., a radiomics model) that can accurately classify a novel subject as a short-term survivor, a medium-term survivor, or a long-term survivor. The BraTS 2020 challenge provides radiological imaging and clinical data (178 subjects) to develop and validate radiomics-based methods for OS classification in brain gliomas. In this study, we empirically evaluated the efficacy of four multiregional radiomic models, for OS classification, and quantified the robustness of predictions to variations in automatic segmentation of brain tumor volume. More specifically, we evaluated four radiomic models, namely, the Whole Tumor (WT) radiomics model, the 3-subregions radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model. The 3-subregions radiomics model is based on a physiological segmentation of whole tumor volume (WT) into three non-overlapping subregions. The 6-subregions and 21-subregions radiomic models are based on an anatomical segmentation of the brain tumor into 6 and 21 anatomical regions, respectively. Moreover, we employed six segmentation schemes - five CNNs and one STAPLE-fusion method - to quantify the robustness of radiomic models. Our experiments revealed that the 3-subregions radiomics model had the best predictive performance (mean AUC = 0.73) but poor robustness (RSD = 1.99) and the 6-subregions and 21-subregions radiomics models were more robust (RSD  1.39) with lower predictive performance (mean AUC  0.71). The poor robustness of the 3-subregions radiomics model was associated with highly variable and inferior segmentation of tumor core and active tumor subregions as quantified by the Hausdorff distance metric (4.4-6.5mm) across six segmentation schemes. Failure analysis revealed that the WT radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model failed for the same subjects which is attributed to the common requirement of accurate segmentation of the WT volume. Moreover, short-term survivors were largely misclassified by the radiomic models and had large segmentation errors (average Hausdorff distance of 7.09mm). Lastly, we concluded that while STAPLE-fusion can reduce segmentation errors, it is not a solution to learning accurate and robust radiomic models.

Smartphone-Based Therapeutic Exercises for Men Affected by Premature Ejaculation: A Pilot Study.

INTRODUCTION: Smartphone-delivered healthcare interventions allow patients to access services on demand when needed, improving motivation and compliance. However, the use of mobile health apps has been scarcely explored in sexual medicine. AIM: To evaluate the effects of integrating psychological treatment for premature ejaculation (PE) with a mobile coaching app that offers therapeutic exercises on the patient's smartphone. METHODS: This study comprised 35 heterosexual men with primary psychogenic PE (mean age 34 years, standard deviation = 9.15). All patients entered a cycle of 15 sessions of psychodynamic psychotherapy integrating behavioral therapy, each lasting about 45 minutes. The patients were randomly assigned to 2 groups, each of which performed daily homework exercises (physiotherapy exercises for reinforcing the pelvic floor muscles and cognitive exercises for distancing from sexual failure.) The first group (15 patients) received verbal and printed instructions only (treatment as usual-TAU), whereas the second group (17 patients) experienced the exercises with guidance from the mobile app (app). In both groups, the exercises started after the seventh session. Patients were advised to perform the exercises 3 times a day for 3 months. MAIN OUTCOME MEASURES: The primary outcome measures were the Premature Ejaculation Diagnostic Tool and the Premature Ejaculation Profile. RESULTS: Analysis of the data revealed significant pre-post improvements in Premature Ejaculation Diagnostic Tool and Premature Ejaculation Profile scores for the app group compared with those of the TAU group (P < .01). The frequency of patients with no-PE condition for the app group after treatment was significantly higher than the frequency of patients with no-PE condition for the TAU group (P < .001). CONCLUSION: Results suggest that a mobile coaching app performs better than TAU in improving both the behavioral skills of ejaculatory delay and sexual self-confidence within a psychological treatment for PE. Future studies should collect follow-up data and explore the potential of mobile coaching apps in combined pharmacotherapy and psychotherapy interventions. Optale G, Burigat S, Chittaro L. et al. Smartphone-Based Therapeutic Exercises for Men Affected by Premature Ejaculation: A Pilot Study. J Sex Med 2020;8:461-471.

MotorBrain: A mobile app for the assessment of users' motor performance in neurology.

BACKGROUND AND OBJECTIVE: Human motor skills or impairments have been traditionally assessed by neurologists by means of paper-and-pencil tests or special hardware. More recently, technologies such as digitizing tablets and touchscreens have offered neurologists new assessment possibilities, but their use has been restricted to a specific medical condition, or to stylus-operated mobile devices. The objective of this paper is twofold. First, we propose a mobile app (MotorBrain) that offers six computerized versions of traditional motor tests, can be used directly by patients (with and without the supervision of a clinician), and aims at turning millions of smartphones and tablets available to the general public into data collection and assessment tools. Then, we carry out a study to determine whether the data collected by MotorBrain can be meaningful for describing aging in human motor performance. METHODS: A sample of healthy participants (N= 133) carried out the motor tests using MotorBrain on a smartphone. Participants were split into two groups (Young, Old) based on their age (less than or equal to 30 years, greater than or equal to 50 years, respectively). The data collected by the app characterizes accuracy, reaction times, and speed of movement. It was analyzed to investigate differences between the two groups. RESULTS: The app does allow measuring differences in neuromotor performance. Data collected by the app allowed us to assess performance differences due to the aging of the neuromuscular system. CONCLUSIONS: Data collected through MotorBrain is suitable to make meaningful distinctions among different kinds of performance, and allowed us to highlight performance differences associated to aging. MotorBrain supports the building of a large database of neuromotor data, which can be used for normative purposes in clinical use.