Test-retest reliability and time-of-day variations of perfusion imaging at rest and during a vigilance task.

Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.

Designing three-level cluster randomized trials to assess treatment effect heterogeneity.

Cluster randomized trials often exhibit a three-level structure with participants nested in subclusters such as health care providers, and subclusters nested in clusters such as clinics. While the average treatment effect has been the primary focus in planning three-level randomized trials, interest is growing in understanding whether the treatment effect varies among prespecified patient subpopulations, such as those defined by demographics or baseline clinical characteristics. In this article, we derive novel analytical design formulas based on the asymptotic covariance matrix for powering confirmatory analyses of treatment effect heterogeneity in three-level trials, that are broadly applicable to the evaluation of cluster-level, subcluster-level, and participant-level effect modifiers and to designs where randomization can be carried out at any level. We characterize a nested exchangeable correlation structure for both the effect modifier and the outcome conditional on the effect modifier, and generate new insights from a study design perspective for conducting analyses of treatment effect heterogeneity based on a linear mixed analysis of covariance model. A simulation study is conducted to validate our new methods and two real-world trial examples are used for illustrations.

Short-term and long-term phenotypic stability of actigraphic sleep metrics involving repeated sleep loss and recovery.

For the first time, we determined whether actigraphic-assessed sleep measures show inter-individual differences and intra-individual stability during baseline (BL) and recovery (REC) phases surrounding repeated total sleep deprivation (TSD). We conducted a 5-day experiment at Months 2 and 4 in two separate studies (N = 11). During each experiment, sleep measures were collected via wrist actigraphy during two BL 8 h time-in-bed (TIB) nights (B1, B2) and during two REC 8-10 h TIB nights (R1, R2). Intraclass correlation coefficients (ICCs) assessed actigraphic measure long-term stability between 2 and 4 months for (1) the pre-experimental phase before BL; and (2) the BL (B1 + B2), REC (R1 + R2), and BL and REC average (BL + REC) phases; and short-term stability at Month 2 and at Month 4; and (3) between B1 versus B2 and R1 versus R2 in each 5-day experiment. Nearly all ICCs during the pre-experimental, BL, REC, and BL + REC phases were moderate to almost perfect (0.446-0.970) between Months 2 and 4. B1 versus B2 ICCs were more stable (0.440-0.899) than almost all R1 versus R2 ICCs (-0.696 to 0.588) at Month 2 and 4. Actigraphic sleep measures show phenotypic long-term stability during BL and REC surrounding repeated TSD between 2 and 4 months. Furthermore, within each 5-day experiment at Month 2 and 4, the two BL nights before TSD were more stable than the two REC nights following TSD, likely due to increased R1 homeostatic pressure. Given the consistency of actigraphic measures across the short-term and long-term, they can serve as biomarkers to predict physiological and neurobehavioral responses to sleep loss.

Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity.

Stepped wedge design is a popular research design that enables a rigorous evaluation of candidate interventions by using a staggered cluster randomization strategy. While analytical methods were developed for designing stepped wedge trials, the prior focus has been solely on testing for the average treatment effect. With a growing interest on formal evaluation of the heterogeneity of treatment effects across patient subpopulations, trial planning efforts need appropriate methods to accurately identify sample sizes or design configurations that can generate evidence for both the average treatment effect and variations in subgroup treatment effects. To fill in that important gap, this article derives novel variance formulas for confirmatory analyses of treatment effect heterogeneity, that are applicable to both cross-sectional and closed-cohort stepped wedge designs. We additionally point out that the same framework can be used for more efficient average treatment effect analyses via covariate adjustment, and allows the use of familiar power formulas for average treatment effect analyses to proceed. Our results further sheds light on optimal design allocations of clusters to maximize the weighted precision for assessing both the average and heterogeneous treatment effects. We apply the new methods to the Lumbar Imaging with Reporting of Epidemiology Trial, and carry out a simulation study to validate our new methods.

Assessing the interrater and intrarater reliability of subglottic stenosis grading systems.

BACKGROUND: Subglottic stenosis (SGS) is a condition leading to narrowing of the upper airway which can lead to dyspnea and life-threatening airway obstruction. Although other proposed grading systems exist, the Cotton Myer (CM) and percent stenosis systems are the most widespread in clinical practice. Despite this, the CM system has not yet been validated for visual assessment of SGS. OBJECTIVE: To determine the interrater and intrarater reliability of the CM grading system among a cohort of physicians who manage patients with SGS. METHODS: An online survey created with videos of tracheoscopies from 20 adult patients with subglotticstenosis (SGS) was sent individually to 9 expert physicians from various medical specialties, all of whom managed patients with SGS. Physicians were asked to view the 20 tracheoscopy videos and assess both the percent stenosis and Cotton Myer (CM) grade of each patient. After a period of 4 weeks, the physicians were sent the same survey of the 20 tracheoscopy videos. The interrater and intrarater reliability was calculated using the intraclass correlation coefficient (ICC), a measurement used to evaluate the reliability (the extent to which a measurement can be replicated) of two or more raters measuring the same subject. RESULTS: Overall, CM and percent stenosis systems were found to have an ICC of 0.94 and 0.90 within the domain of interrater reliability, respectively, and ICC of 0.71 and 0.81 within the domain of intrarater reliability, respectively. CONCLUSION: Our findings suggest that the CM and percent stenosis grading systems remain a valid clinical tool to measure and communicate the severity of airway obstruction in SGS.

Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation.

PURPOSE: To propose radiomics features as a superior measure for evaluating the segmentation ability of physicians and auto-segmentation tools and to compare its performance with the most commonly used metrics: Dice similarity coefficient (DSC), surface Dice similarity coefficient (sDSC), and Hausdorff distance (HD). MATERIALS/METHODS: The data of 10 lung cancer patients' CT images with nine tumor segmentations per tumor were downloaded from the RIDER (Reference Database to Evaluate Response) database. Radiomics features of 90 segmented tumors were extracted using the PyRadiomics program. The intraclass correlation coefficient (ICC) of radiomics features were used to evaluate the segmentation similarity and compare their performance with DSC, sDSC, and HD. We calculated one ICC per radiomics feature and per tumor for nine segmentations and 36 ICCs per radiomics feature for 36 pairs of nine segmentations. Meanwhile, there were 360 DSC, sDSC, and HD values calculated for 36 pairs for 10 tumors. RESULTS: The ICC of radiomics features exhibited greater sensitivity to segmentation changes than DSC and sDSC. The ICCs of the wavelet-LLL first order Maximum, wavelet-LLL glcm MCC, wavelet-LLL glcm Cluster Shade features ranged from 0.130 to 0.997, 0.033 to 0.978, and 0.160 to 0.998, respectively. On the other hand, all DSC and sDSC were larger than 0.778 and 0.700, respectively, while HD varied from 0 to 1.9 mm. The results indicated that the radiomics features could capture subtle variations in tumor segmentation characteristics, which could not be easily detected by DSC and sDSC. CONCLUSIONS: This study demonstrates the superiority of radiomics features with ICC as a measure for evaluating a physician's tumor segmentation ability and the performance of auto-segmentation tools. Radiomics features offer a more sensitive and comprehensive evaluation, providing valuable insights into tumor characteristics. Therefore, the new metrics can be used to evaluate new auto-segmentation methods and enhance trainees' segmentation skills in medical training and education.

Designing multicenter individually randomized group treatment trials.

In an individually randomized group treatment (IRGT) trial, participant outcomes can be positively correlated due to, for example, shared therapists in treatment delivery. Oftentimes, because of limited treatment resources or participants at one location, an IRGT trial can be carried out across multiple centers. This design can be subject to potential correlations in the participant outcomes between arms within the same center. While the design of a single-center IRGT trial has been studied, little is known about the planning of a multicenter IRGT trial. To address this gap, this paper provides analytical sample size formulas for designing multicenter IRGT trials with a continuous endpoint under the linear mixed model framework. We found that accounting for the additional center-level correlation at the design stage can lead to sample size reduction, and the magnitude of reduction depends on the amount of between-therapist correlation. However, if the variance components of therapist-level random effects are considered as input parameters in the design stage, accounting for the additional center-level variance component has no impact on the sample size estimation. We presented our findings through numeric illustrations and performed simulation studies to validate our sample size procedures under different scenarios. Optimal design configurations under the multicenter IRGT trials have also been discussed, and two real-world trial examples are drawn to illustrate the use of our method.

Validation of a computerized model for a new biomechanical concept- the fossa-foveolar mismatch- the answer to lesions of the ligamentous fossa-foveolar complex in the hip?

BACKGROUND: Hip-preserving surgery in young patients frequently reveals lesions of the ligamentum teres (LT). Histological and clinical evidence supports that those lesions could be source of intraarticular hip pain. It has been hypothesized that LT degeneration could be linked to the abnormal positioning of the fovea outside the lunate surface during various daily motions. We introduce the "fossa-foveolar mismatch" (FFM) by determining the trajectory of the fovea in the fossa during hip motions, enabling a comparison across diverse hip-pathomorphologies. AIMS: to determine (1) intraobserver reliability and (2) interobserver reproducibility of our computer-assisted 3-dimensional (3D) model of the FFM. MATERIALS AND METHODS: All patients with joint preserving surgery for femoroacetabular impingement syndrome (FAIS) or developmental dysplasia of the hip (DDH) at our institution (11. 2015-08.2019)were initially eligible. We employed a simple random sampling technique to select 15 patients for analysis. Three-dimensional surface models based on preoperative computed tomography (CT) scans were built, the fossa virtually excised, the fovea capitis marked. Models were subjected to physiological range of motion with validated 3D collision detection software. Using a standardized medial view on the resected fossa and the transparent lunate surface, the FFM-index was calculated for 17 motions. It was obtained by dividing the surface occupied by the fovea outside of the fossa by the total foveolar tracking surface. Three observers independently performed all analyses twice. (1) Intraobserver reliability and (2) interobserver reproducibility were calculated using intraclass correlation coefficients (ICCs). RESULTS: (1) We obtained excellent intraobserver ICCs for the FFM-index averaging 0.92 with 95% CI 0.77-0.9 among the three raters for all motions. (2) Interobserver reproducibility between raters was good to excellent, ranging from 0.76 to 0.98. CONCLUSIONS: The FFM-index showed excellent intraobserver reliability and interobserver reproducibility for all motions. This innovative approach deepens our understanding of biomechanical implications, providing valuable insights for identifying patient populations at risk.

Evaluation of measurement reliability for selected indices of postural stability based on data from the GYKO Inertial Sensor System.

The main aim of this study is to use comprehensive statistical analyses to evaluate measurement reliability of selected variables that characterize postural stability. The study examined twenty-nine healthy non-athlete students. The examinations were performed twice, with a one-week interval. The Microgate GYKO inertial sensor system was used to evaluate the reliability of variables that characterize postural stability. The relative reliability of the repeated test was evaluated using the intraclass correlation coefficient (ICC) with 95% confidence interval (95% CI). Next, the standard error of measurement (SEM) and minimal detectable change (MDC) were computed. Relative reliability of the repeated test for all analysed variables of ICC ranged from 0.31 to 0.75. For four variables, ICC values were ca. 0.7, i.e., they can be considered as good. For four other variables, ICC ranged from 0.41 to 0.54, with these values considered fair. Satisfactory reproducibility of postural stability measurements using the GYKO inertial sensor system demonstrates that it can offer an inexpensive and efficient alternative to measurements that use force balance platforms.

Test-retest reliability of a finger-tapping fMRI task in a healthy population.

Measuring brain activity during functional MRI (fMRI) tasks is one of the main tools to identify brain biomarkers of disease or neural substrates associated with specific symptoms. However, identifying correct biomarkers relies on reliable measures. Recently, poor reliability was reported for task-based fMRI measures. The present study aimed to demonstrate the reliability of a finger-tapping fMRI task across two sessions in healthy participants. Thirty-one right-handed healthy participants aged 18-60 years took part in two MRI sessions 3 weeks apart during which we acquired finger-tapping task-fMRI. We examined the overlap of activations between sessions using Dice similarity coefficients, assessing their location and extent. Then, we compared amplitudes calculating intraclass correlation coefficients (ICCs) in three sets of regions of interest (ROIs) in the motor network: literature-based ROIs (10-mm-radius spheres centred on peaks of an activation likelihood estimation), anatomical ROIs (regions as defined in an atlas) and ROIs based on conjunction analyses (superthreshold voxels in both sessions). Finger tapping consistently activated expected regions, for example, left primary sensorimotor cortices, premotor area and right cerebellum. We found good-to-excellent overlap of activations for most contrasts (Dice coefficients: .54-.82). Across time, ICCs showed large variability in all ROI sets (.04-.91). However, ICCs in most ROIs indicated fair-to-good reliability (mean = .52). The least specific contrast consistently yielded the best reliability. Overall, the finger-tapping task showed good spatial overlap and fair reliability of amplitudes on group level. Although caution is warranted in interpreting correlations of activations with other variables, identification of activated regions in response to a task and their between-group comparisons are still valid and important modes of analysis in neuroimaging to find population tendencies and differences.

Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes.

Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for analyzing SW-CRTs with population-averaged interpretations, but the joint estimation of the mean and intraclass correlation coefficients (ICCs) can be computationally intensive due to large cluster-period sizes. Motivated by the need for marginal inference in SW-CRTs, we propose a simple and efficient estimating equations approach to analyze cluster-period means. We show that the quasi-score for the marginal mean defined from individual-level observations can be reformulated as the quasi-score for the same marginal mean defined from the cluster-period means. An additional mapping of the individual-level ICCs into correlations for the cluster-period means further provides a rigorous justification for the cluster-period approach. The proposed approach addresses a long-recognized computational burden associated with estimating equations defined based on individual-level observations, and enables fast point and interval estimation of the intervention effect and correlations. We further propose matrix-adjusted estimating equations to improve the finite-sample inference for ICCs. By providing a valid approach to estimate ICCs within the class of generalized linear models for correlated binary outcomes, this article operationalizes key recommendations from the CONSORT extension to SW-CRTs, including the reporting of ICCs.

Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study.

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.

White Matter Functional Connectivity in Resting-State fMRI: Robustness, Reliability, and Relationships to Gray Matter.

A comprehensive characterization of the spatiotemporal organization in the whole brain is critical to understand both the function and dysfunction of the human brain. Resting-state functional connectivity (FC) of gray matter (GM) has helped in uncovering the inherent baseline networks of brain. However, the white matter (WM), which composes almost half of brain, has been largely ignored in this characterization despite studies indicating that FC in WM does change during task and rest functional magnetic resonance imaging (fMRI). In this study, we identify 9 white matter functional networks (WM-FNs) and 9 gray matter functional networks (GM-FNs) of resting fMRI. Intraclass correlation coefficient (ICC) was calculated on multirun fMRI data to estimate the reliability of static functional connectivity (SFC) and dynamic functional connectivity (DFC). Associations between SFC, DFC, and their respective ICCs are estimated for GM-FNs, WM-FNs, and GM-WM-FNs. SFC of GM-FNs were stronger than that of WM-FNs, but the corresponding DFC of GM-FNs was lower, indicating that WM-FNs were more dynamic. Associations between SFC, DFC, and their ICCs were similar in both GM- and WM-FNs. These findings suggest that WM fMRI signal contains rich spatiotemporal information similar to that of GM and may hold important cues to better establish the functional organization of the whole brain.

COVID-19 Chest CT Quantification: Triage and Prognostic Value in Different Ages.

Objective: We evaluated the triage and prognostic performance of seven proposed computed tomography (CT)-severity score (CTSS) systems in two different age groups.Design: Retrospective study.Setting: COVID-19 pandemic.Participants: Admitted COVID-19, PCR-positive patients were included, excluding patients with heart failure and significant pre-existing pulmonary disease.Methods: Patients were divided into two age groups: ≥65 years and ≤64 years. Clinical data indicating disease severity at presentation and at peak disease severity were recorded. Initial CT images were scored by two radiologists according to seven CTSSs (CTSS1-CTSS7). Receiver operating characteristic (ROC) analysis for the performance of each CTSS in diagnosing severe/critical disease on admission (triage performance) and at peak disease severity (prognostic performance) was done for the whole cohort and each age group separately.Results: Included were 96 patients. Intraclass correlation coefficient (ICC) between the two radiologists scoring the CT scan images were good for all the CTSSs (ICC=0.764-0.837). In the whole cohort, all CTSSs showed an unsatisfactory area under the curve (AUC) in the ROC curve for triage, excluding CTSS2 (AUC=0.700), and all CTSSs showed acceptable AUCs for prognostic usage (0.759-0.781). In the older group (≥65 years; n=55), all CTSSs excluding CTSS6 showed excellent AUCs for triage (0.804-0.830), and CTSS6 was acceptable (AUC=0.796); all CTSSs showed excellent or outstanding AUCs for prognostication (0.859-0.919). In the younger group (≤64 years; n=41), all CTSSs showed unsatisfactory AUCs for triage (AUC=0.487-0.565) and prognostic usage (AUC=0.668-0.694), excluding CTSS6, showing marginally acceptable AUC for prognostic performance (0.700).Conclusion: Those CTSSs requiring more numerous segmentations, namely CTSS2, CTSS7, and CTSS5 showed the best ICCs; therefore, they are the best when comparison between two separate scores is needed. Irrespective of patients' age, CTSSs show minimal value in triage and acceptable prognostic value in COVID-19 patients. CTSS performance is highly variable in different age groups. It is excellent in those aged ≥65 years, but has little if any value in younger patients. Multicenter studies with larger sample size to evaluate results of this study should be conducted.

An evaluation of the test-retest reliability of the functional head impulse test in healthy young adults.

AIM: To determine the test and retest reliability of the functional head impulse test (fHIT) in healthy young adults. MATERIALS AND METHODS: Thirty-three healthy participants (17 women, 16 men) aged 18-30 years were included in the study. Each participant underwent the fHIT twice, 1 week apart, by the same experienced clinician. Intraclass correlation coefficients (ICCs) were used to determine test-retest reliability. RESULTS: There was no statistically significant difference between the results of total percentage of correct answer (CA%) of the fHIT obtained in session 1 and session 2 measurements in the lateral, anterior, and posterior semicircular canals (SCCs) (p > 0.05). ICC values for test-retest reliability were found to range from 0.619 to 0.665 for the three semicircular canals (SCCs). CONCLUSION: The test-retest reliability of the fHIT device was moderate. Attention, cognition, and fatigue may be the factors reducing reliability. In the diagnosis, follow-up, and rehabilitation processes of vestibular diseases in clinics, changes in the fHIT CA% can be used to assess vestibulo-ocular reflex (VOR) functionality.

Five or more gender- and size-diverse customizations of distal femur prostheses are needed to improve fit for Chinese knees.

PURPOSE: Mismatch between partial imported prostheses and Chinese anatomy affects the clinical outcome of the procedure. The purpose of this study was to measure the anatomical dimensions of Chinese distal femurs to provide guidance for the design of more compatible distal femoral prostheses. METHODS: A total of 406 healthy distal femurs were reconstructed and measured. Consistency of these measurements and differences in sides, gender, and populations were examined. Parameter correlations were analyzed, and pairs with strong correlations underwent linear regression analysis. The design of distal femoral prostheses was referenced from the results of K-means and hierarchical clustering analysis. RESULTS: Ten parameters were measured, including the trans-epicondylar axis, width of the distal femur (ML), anteroposterior diameter of the distal femur (AP), etc. The intra-class correlation coefficient ranged from 0.795 to 0.999 for intra-observer consistency, and from 0.796 to 0.998 for inter-observer consistency. Males exhibited significantly larger parameters than females, except for the posterior condylar angle (all P values < 0.05). Compared to other populations, substantial differences were observed for most parameters, such as ML, AP, width of lateral femoral condyle, etc. (all P values < 0.05). Clustering analysis suggested that distal femoral prostheses should include at least five sizes to adequately accommodate the sampled population. ML sizes for males were 68, 70, 83, 73, and 89 mm, and for females 64, 65, 71, 67, and 77 mm. AP sizes for males were 56, 60, 60, 64, and 64 mm, and for females 48, 52, 54, 57, and 58 mm. CONCLUSIONS: Chinese distal femur morphology, as analyzed using 3D techniques, varies significantly between genders and when compared with international data. For improved patient fit, the creation of five or more distal femur prostheses, diversified by gender and size and informed by the associated morphological parameters, is recommended. LEVEL OF EVIDENCE: IV.

Improving the reliability of measurements in orthopaedics and sports medicine.

A large space still exists for improving the measurements used in orthopaedics and sports medicine, especially as we face rapid technological progress in devices used for diagnostic or patient monitoring purposes. For a specific measure to be valuable and applicable in clinical practice, its reliability must be established. Reliability refers to the extent to which measurements can be replicated, and three types of reliability can be distinguished: inter-rater, intra-rater, and test-retest. The present article aims to provide insights into reliability as one of the most important and relevant properties of measurement tools. It covers essential knowledge about the methods used in orthopaedics and sports medicine for reliability studies. From design to interpretation, this article guides readers through the reliability study process. It addresses crucial issues such as the number of raters needed, sample size calculation, and breaks between particular trials. Different statistical methods and tests are presented for determining reliability depending on the type of gathered data, with particular attention to the commonly used intraclass correlation coefficient.

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses.

BACKGROUND: Female breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging. OBJECTIVE: The purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC. METHODS: 302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray-scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad-score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model. RESULTS: When we selected 10 radiomics features to calculate the rad-score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value. CONCLUSION: The radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).

Evaluation of a simple activity measurement method in rats.

[Purpose] Behavioral analysis is widely used in animal research. However, such analysis requires specialized equipment and can be difficult to perform. Therefore, this study aimed to explore and validate a simple behavioral analysis method. [Participants and Methods] For behavioral assessments, Wistar rats were placed in a rearing cage and videotaped from two directions: overhead and side view. The filmed videos were analyzed using ImageJ software to calculate the distance traveled and activity and inactivity times of the rats. Intraclass correlation coefficients 1 and 2 were calculated to examine the reliability of the behavioral analysis method. [Results] Intraclass correlation coefficients 1 and 2 for distance traveled and activity and inactivity times determined using the behavioral analysis method showed high reliability. [Conclusion] The behavioral analysis method validated in this study used inexpensive and easily accessible equipment and devices. The results show high correlation coefficients for the measurement of distance traveled and activity time performed by experimental animals, demonstrating the reliability of this simple method.

Reliability of task-evoked neural activation during face-emotion paradigms: Effects of scanner and psychological processes.

Assessing and improving test-retest reliability is critical to efforts to address concerns about replicability of task-based functional magnetic resonance imaging. The current study uses two statistical approaches to examine how scanner and task-related factors influence reliability of neural response to face-emotion viewing. Forty healthy adult participants completed two face-emotion paradigms at up to three scanning sessions across two scanners of the same build over approximately 2 months. We examined reliability across the main task contrasts using Bayesian linear mixed-effects models performed voxel-wise across the brain. We also used a novel Bayesian hierarchical model across a predefined whole-brain parcellation scheme and subcortical anatomical regions. Scanner differences accounted for minimal variance in temporal signal-to-noise ratio and task contrast maps. Regions activated during task at the group level showed higher reliability relative to regions not activated significantly at the group level. Greater reliability was found for contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. nonface discrimination) compared to conditions with more similar demands (e.g., angry vs. happy face discrimination). Voxel-wise reliability estimates tended to be higher than those based on predefined anatomical regions. This work informs attempts to improve reliability in the context of task activation patterns and specific task contrasts. Our study provides a new method to estimate reliability across a large number of regions of interest and can inform researchers' selection of task conditions and analytic contrasts.