Enhancing Renal Tumor Detection: Leveraging Artificial Neural Networks in Computed Tomography Analysis.

BACKGROUND Renal cell carcinoma is one of the most common cancers in Europe, with a total incidence rate of 18.4 cases per 100 000 population. There is currently significant overdiagnosis (11% to 30.9%) at times of planned surgery based on radiological studies. The purpose of this study was to create an artificial neural network (ANN) solution based on computed tomography (CT) images as an additional tool to improve the differentiation of malignant and benign renal tumors and to aid active surveillance. MATERIAL AND METHODS A retrospective study based on CT images was conducted. Axial CT images of 357 renal tumor cases were collected. There were 265 (74.2%) cases histologically proven to be malignant, while 34 (9.5%) cases were benign. Radiologists diagnosed 58 (16.3%) cases as angiomyolipoma (AML), based on characteristic appearance, not confirmed histopathologically. For ANN training, the arterial CT phase images were used. A total of 7207 arterial-phase images were collected, then cropped and added to the database with the associated diagnosis. For the test dataset (ANN validation), 38 cases (10 benign, 28 malignant) were chosen by subgroup randomization to correspond to statistical tumor type distribution. The VGG-16 ANN architecture was used in this study. RESULTS Trained ANN correctly classified 23 out of 28 malignant tumors and 8 out of 10 benign tumors. Accuracy was 81.6% (95% confidence interval, 65.7-92.3%), sensitivity was 82.1% (63.1-93.9%), specificity was 80.0% (44.4-97.5%), and F1 score was 86.8% (74.7-94.5%). CONCLUSIONS The created ANN achieved promising accuracy in differentiating benign vs malignant renal tumors.

Psychophysiological strategies for enhancing performance through imagery-skin conductance level analysis in guided vs. self-produced imagery.

Athletes need to achieve their optimal level of arousal for peak performance. Visualization or mental rehearsal (i.e., Imagery) often helps to obtain an appropriate level of activation, which can be detected by monitoring Skin Conductance Level (SCL). However, different types of imagery could elicit different amount of physiological arousal. Therefore, this study aims: (1) to investigate differences in SCL associated with two instructional modalities of imagery (guided vs. self-produced) and six different scripts; (2) to check if SCL could differentiate respondents according to their sport expertise. Thirty participants, aged between 14 and 42 years (M = 22.93; SD = 5.24), with different sport levels took part in the study. Participants listened to each previously recorded script and then were asked to imagine the scene for a minute. During the task, SCL was monitored. We analysed the mean value, variance, slope and number of fluctuations per minute of the electrodermal signal. Unsupervised machine learning models were used for measuring the resemblance of the signal. The Wilcoxon signed-rank test was used for distinguishing guided and self-produced imagery, and The Mann-Whitney U test was used for distinguishing results of different level athletes. We discovered that among others, self-produced imagery generates lower SCL, higher variance, and a higher number of fluctuations compared to guided imagery. Moreover, we found similarities of the SCL signal among the groups of athletes (i.e. expertise level). From a practical point of view, our findings suggest that different imagery instructional modalities can be implemented for specific purposes of mental preparation.

Exploring the influence of personal factors on physiological responses to mental imagery in sport.

Imagery is a well-known technique in mental training which improves performance efficiency and influences physiological arousal. One of the biomarkers indicating the amount of physiological arousal is skin conductance level (SCL). The aim of our study is to understand how individual differences in personality (e.g. neuroticism), general imagery and situational sport anxiety are linked to arousal measuring with SCL in situational imagery. Thirty participants aged between 14 and 42 years (M = 22.93; SD = 5.24), with sport experience ranging between 2 and 20 years (M = 10.15; SD = 4.75), took part in our study. Participants listened to each previously recorded script and then were asked to imagine the scene for a minute. During the task SCL was monitored using the Biofeedback Expert 2000. Machine learning predictive models based on artificial neural networks have been trained for prediction of physiological response, as a function of selected psychological tests. We found an association among neuroticism, prestart anxiety, and general tendency to use imagery with SCL. From a practical point of view our results may help athletes, coaches, and psychologists to be more aware of the role of individual differences in sport.

Neural Oscillation During Mental Imagery in Sport: An Olympic Sailor Case Study.

The purpose of the current study was to examine the cortical correlates of imagery depending on instructional modality (guided vs. self-produced) using various sports-related scripts. According to the expert-performance approach, we took an idiosyncratic perspective analyzing the mental imagery of an experienced two-time Olympic athlete to verify whether different instructional modalities of imagery (i.e., guided vs. self-produced) and different scripts (e.g., training or competition environment) could differently involve brain activity. The subject listened to each previously recorded script taken from two existing questionnaires concerning imagery ability in sport and then was asked to imagine the scene for a minute. During the task, brain waves were monitored using EEG (32-channel g. Nautilus). Our findings indicate that guided imagery might induce higher high alpha and SMR (usually associated with selective attention), whereas self-produced imagery might facilitate higher low alpha (associated with global resting state and relaxation). Results are discussed in light of the neural efficiency hypothesis as a marker of optimal performance and transient hypofrontality as a marker of flow state. Practical mental training recommendations are presented.

Evolution of Ag nanostructures created from thin films: UV-vis absorption and its theoretical predictions.

Ag-based plasmonic nanostructures were manufactured by thermal annealing of thin metallic films. Structure and morphology were studied using scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HR-TEM) and X-ray photoelectron spectroscopy (XPS). SEM images show that the formation of nanostructures is influenced by the initial layer thickness as well as the temperature and the time of annealing. The Ag 3d and Ag 4d XPS spectra are characteristic of nanostructures. The quality of the nanostructures, in terms of their use as plasmonic platforms, is reflected in the UV-vis absorption spectra. The absorption spectrum is dominated by a maximum in the range of 450-500 nm associated with the plasmon resonance. As the initial layer thickness increases, an additional peak appears around 350 nm, which probably corresponds to the quadrupole resonance. For calculations leading to a better illustration of absorption, scattering and overall absorption of light in Ag nanoparticles, the Mie theory is employed. Absorbance and the distribution of the electromagnetic field around the nanostructures are calculated by finite-difference time-domain (FDTD) simulations. For calculations a novel approach based on modelling the whole sample with a realistic shape of the nanoparticles, instead of full spheres, was used. This led to a very good agreement with the experiment.

Au-Si plasmonic platforms: synthesis, structure and FDTD simulations.

Plasmonic platforms based on Au nanostructures have been successfully synthesized by directional solidification of a eutectic from Au and the substrate. In order to determine homogeneous shape and space distribution, the influence of annealing conditions and the initial thickness of the Au film on the nanostructures was analyzed. For the surface morphology studies, SEM and AFM measurements were performed. The structure of platforms was investigated using XRD and XPS methods. Structural investigations confirmed, that nanostructures consist of metallic Au, growing along the [111] direction. The most homogeneous seems to be the platform obtained by solidification of a 2.8 nm Au film, annealed at 550 °C for 15 min. This sample was subsequently chosen for theoretical calculations. Simulations of electromagnetic field propagation through the produced samples were performed using the finite-difference time domain (FDTD) method. The calculated absorbance, as a result of the FDTD simulation shows a quite good agreement with experimental data obtained in the UV-vis range.