Neoadjuvant chemotherapy for high-grade serous ovarian cancer: radiologic-pathologic correlation of response assessment and predictors of progression.

PURPOSE: Neoadjuvant chemotherapy is often administered for high-grade serous ovarian carcinoma (HGSC) prior to cytoreductive surgery. We evaluated treatment response by CT (simplified peritoneal carcinomatosis index [S-PCI]), pathology (chemotherapy response score [CRS]), laboratory markers (serum CA-125), and surgical outcomes, to identify predictors of disease-free survival. METHODS: For this retrospective, HIPAA-compliant, IRB-approved study, we identified 396 women with HGSC receiving neoadjuvant chemotherapy between 2010 and 2019. Two hundred and ninety-nine patients were excluded (surgery not performed; imaging/pathology unavailable). Pre- and post-treatment abdominopelvic CTs were assigned CT S-PCI scores 0-24 (higher score indicating more tumor). Specimens were assigned CRS of 1-3 (minimal to complete response). Clinical data were obtained via chart review. Univariate, multivariate, and survival analyses were performed. RESULTS: Ninety-seven women were studied, with mean age of 65 years ± 10. Interreader agreement was good to excellent for CT S-PCI scores (ICC 0.64-0.77). Despite a significant decrease in CT S-PCI scores after treatment (p < 0.001), mean decrease in CT S-PCI did not differ significantly among CRS categories (p = 0.20) or between patients who were optimally versus suboptimally debulked (p = 0.29). In a survival analysis, lower CRS (more viable tumor) was associated with shorter time to progression (p < 0.001). A joint Cox proportional-hazard models showed that only residual pathologic disease (CRS 1/2) (HR 4.19; p < 0.001) and change in CA-125 (HR 1.79; p = 0.01) predicted progression. CONCLUSION: HGSC response to neoadjuvant therapy by CT S-PCI did not predict pathologic CRS score, optimal debulking, or progression, revealing discordance between imaging, pathologic, biochemical, and surgical assessments of tumor response.

A framework of NGO inside and outside strategies in the commercial determinants of health: findings from a narrative review.

BACKGROUND: Public health scholarship has uncovered a wide range of strategies used by industry actors to promote their products and influence government regulation. Less is known about the strategies used by non-government organisations to attempt to influence commercial practices. This narrative review applies a political science typology to identify a suite of 'inside' and 'outside' strategies used by NGOs to attempt to influence the commercial determinants of health. METHODS: We conducted a systematic search in Web of Science, ProQuest and Scopus. Articles were eligible for inclusion if they comprised an empirical study, explicitly sought to examine 'NGOs', were in English, and identified at least one NGO strategy aimed at commercial and/or government policy and practice. RESULTS: One hundred forty-four studies met the inclusion criteria. Eight industry sectors were identified: extractive, tobacco, food, alcohol, pharmaceuticals, weapons, textiles and asbestos, and a small number of general studies. We identified 18 types of NGO strategies, categorised according to the target (i.e. commercial actor or government actor) and type of interaction with the target (i.e. inside or outside). Of these, five NGO 'inside' strategies targeted commercial actors directly: 1) participation in partnerships and multistakeholder initiatives; 2) private meetings and roundtables; 3) engaging with company AGMs and shareholders; 4) collaborations other than partnerships; and 5) litigation. 'Outside' strategies targeting commercial actors through the mobilisation of public opinion included 1) monitoring and reporting; 2) protests at industry sites; 3) boycotts; 4) directly engaging the public; and 5) creative use of alternative spaces. Four NGO 'inside' strategies directly targeting government actors included: 1) lobbying; 2) drafting legislation, policies and standards; 3) providing technical support and training; and 4) litigation. NGO 'outside' strategies targeting government included 1) protests and public campaigns; 2) monitoring and reporting; 3) forum shifting; and 4) proposing and initiating alternative solutions. We identified three types of NGO impact: substantive, procedural, and normative. CONCLUSION: The analysis presents a matrix of NGO strategies used to target commercial and government actors across a range of industry sectors. This framework can be used to guide examination of which NGO strategies are effective and appropriate, and which conditions enable NGO influence.

Radiologist age and diagnostic errors.

PURPOSE: Previous investigations into the causes of error by radiologists have addressed work schedule, volume, shift length, and sub-specialization. Studies regarding possible associations between radiologist errors and radiologist age and timing of residency training are lacking in the literature, to our knowledge. The aim of our study was to determine if radiologist age and residency graduation date is associated with diagnostic errors. METHODS: Our retrospective analysis included 1.9 million preliminary interpretations (out of a total of 5.2 million preliminary and final interpretations) of imaging examinations by 361 radiologists in a US-based national teleradiology practice between 1/1/2019 and 1/1/2020. Quality assurance data regarding the number of radiologist errors was generated through client facility feedback to the teleradiology practice. With input from both the client radiologist and the teleradiologist, the final determination of the presence, absence, and severity of a teleradiologist error was determined by the quality assurance committee of radiologists within the teleradiology company using standardized criteria. Excluded were 3.2 million final examination interpretations and 93,963 (1.8%) of total examinations from facilities reporting less than one discrepancy in examination interpretation in 2019. Logistic regression with covariates radiologist age and residency graduation date was performed for calculation of relative risk of overall error rates and by major imaging modality. Major errors were separated from minor errors as those with a greater likelihood of affecting patient care. Logistic regression with covariates radiologist age, residency graduation date, and log total examinations interpreted was used to calculate odds of making a major error to that of making a minor error. RESULTS: Mean age of the 361 radiologists was 51.1 years, with a mean residency graduation date of 2001. Mean error rate for all examinations was 0.5%. Radiologist age at any residency graduation date was positively associated with major errors (p < 0.05), with a relative risk 1.021 for each 1-year increase in age and relative risk 1.235 for each decade as well as for minor errors (p < 0.05, relative risk 1.007 for each year, relative risk 1.082 for each decade). By major imaging modality, radiologist age at any residency graduation date was positively associated with computed tomography (CT) and X-ray (XR) major and minor error, magnetic resonance imaging (MRI) major error, and ultrasound (US) minor error (p < 0.05). Radiologist age was positively associated with odds of making a major vs. minor error (p < 0.05). CONCLUSIONS: The mean error rate for all radiologists was low. We observed that increasing age at any residency graduation date was associated with increasing relative risk of major and minor errors as well as increasing odds of a major vs. minor error among providers. Further study is needed to corroborate these results, determine clinical relevance, and highlight strategies to address these findings.

Daytime, evening, and overnight: the 24-h radiology cycle and impact on interpretative accuracy.

PURPOSE: To assess the influence of time of day when a study is interpreted on discrepancy rates for common and advanced studies performed in the acute community setting. METHODS: This retrospective study used the databank of a U.S. teleradiology company to retrieve studies between 2012 and 2016 with a preliminary report followed by a final report by the on-site client hospital. Neuroradiology, abdominal radiology, and musculoskeletal radiology studies were included. Teleradiologists were fellowship trained in one of these subspecialty areas. Daytime, evening, and overnight times were defined. Associations between major and minor discrepancies, time of day, and whether the study was common or advanced were tested with significance set at p = .05. RESULTS: A total of 5,883,980 studies were analyzed. There were 8444 major discrepancies (0.14%) and 17,208 minor discrepancies (0.29%). For common studies, daytime (0.13%) and evening (0.13%) had lower major discrepancy rates compared to overnight (0.14%) (daytime to overnight, RR = 0.57, 95%CI: 0.45, 0.72, p < 0.01 and evening to overnight, RR = 0.57, 95%CI: 0.49,0.67, p < 0.01). Minor discrepancy rates for common studies were decreased for evening (0.29%) compared to overnight (0.30%) (RR = 0.89, 95%CI: 0.80,0.99, p = 0.029). For advanced studies, daytime (.15%) had lower major discrepancy rates compared to evening (0.20%) and overnight (.23%) (daytime to evening, RR = 0.77, 95%CI: 0.61, 0.97, p = 0.028 and daytime to overnight, RR = 0.66, 95%CI: 0.50, 0.87, p ≤ 0.01). CONCLUSION: Significantly higher major discrepancy rates for studies interpreted overnight suggest the need for radiologists to exercise greater caution when interpreting studies overnight and may require practice management strategies to help optimize overnight work conditions. The lower major discrepancy rates on advanced studies interpreted during the daytime suggest the need for reserving advanced studies for interpretation during the day when possible.

Neurobehavioral indices of gaze perception are associated with social cognition across schizophrenia patients and healthy controls.

BACKGROUND: Gaze perception is a basic building block of social cognition, which is impaired in schizophrenia (SZ) and contributes to functional outcomes. Few studies, however, have investigated neural underpinnings of gaze perception and their relation to social cognition. We address this gap. METHOD: We recruited 77 SZ patients and 71 healthy controls, who completed various social-cognition tasks. During functional magnetic resonance imaging, participants (62 SZ, 54 controls) completed a gaze-perception task, where they judged whether faces with varying gaze angles were self-directed or averted; as a control condition, participants identified stimulus gender. Activation estimates were extracted based on (a) task versus baseline, (b) gaze-perception versus gender-identification, (c) parametric modulation by perception of stimuli as self-directed versus averted, and (d) parametric modulation by stimulus gaze angle. We used latent variable analysis to test associations among diagnostic group, brain activation, gaze perception, and social cognition. RESULTS: Preferential activation to gaze perception was observed throughout dorsomedial prefrontal cortex, superior temporal sulcus, and insula. Activation was modulated by stimulus gaze angle and perception of stimuli as self-directed versus averted. More precise gaze perception and higher task-related activation were associated with better social cognition. Patients with SZ showed hyperactivation within left pre-/postcentral gyrus, which was associated with more precise gaze perception and fewer symptoms and thus may be a compensatory mechanism. CONCLUSIONS: Neural and behavioral indices of gaze perception were related to social cognition, across patients and controls. This suggests gaze perception is an important perceptual building block for more complex social cognition. Results are discussed in the context of dimensional psychopathology and clinical heterogeneity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Neurological and Neuroradiological Manifestations in Neonates Born to Mothers With Coronavirus Disease 2019.

BACKGROUND: To investigate the complications that occurred in neonates born to mothers with coronavirus disease 2019 (COVID-19), focusing on neurological and neuroradiological findings, and to compare differences associated with the presence of maternal symptoms. METHODS: Ninety neonates from 88 mothers diagnosed with coronavirus disease 2019 (COVID-19) during pregnancy were retrospectively reviewed. Neonates were divided into two groups: symptomatic (Sym-M-N, n = 34) and asymptomatic mothers (Asym-M-N, n = 56). The results of neurological physical examinations were compared between the groups. Data on electroencephalography, brain ultrasound, and magnetic resonance imaging abnormalities were collected for neonates with neurological abnormalities. RESULTS: Neurological abnormalities at birth were found in nine neonates (Sym-M-N, seven of 34, 20.6%). Decreased tone was the most common physical abnormality (n = 7). Preterm and very preterm birth (P < 0.01), very low birth weight (P < 0.01), or at least one neurological abnormality on physical examination (P = 0.049) was more frequent in Sym-M-N neonates. All infants with abnormalities on physical examination showed neuroradiological abnormalities. The most common neuroradiological abnormalities were intracranial hemorrhage (n = 5; germinal matrix, n = 2; parenchymal, n = 2; intraventricular, n = 1) and hypoxic brain injury (n = 3). CONCLUSIONS: Neonates born to mothers with symptomatic COVID-19 showed an increased incidence of neurological abnormalities. Most of the mothers (96.4%) were unvaccinated before the COVID-19 diagnosis. Our results highlight the importance of neurological and neuroradiological management in infants born to mothers with COVID-19 and the prevention of maternal COVID-19 infection.

Bayesian Inference for Brain Activity from Functional Magnetic Resonance Imaging Collected at Two Spatial Resolutions.

Neuroradiologists and neurosurgeons increasingly opt to use functional magnetic resonance imaging (fMRI) to map functionally relevant brain regions for noninvasive presurgical planning and intraoperative neuronavigation. This application requires a high degree of spatial accuracy, but the fMRI signal-to-noise ratio (SNR) decreases as spatial resolution increases. In practice, fMRI scans can be collected at multiple spatial resolutions, and it is of interest to make more accurate inference on brain activity by combining data with different resolutions. To this end, we develop a new Bayesian model to leverage both better anatomical precision in high resolution fMRI and higher SNR in standard resolution fMRI. We assign a Gaussian process prior to the mean intensity function and develop an efficient, scalable posterior computation algorithm to integrate both sources of data. We draw posterior samples using an algorithm analogous to Riemann manifold Hamiltonian Monte Carlo in an expanded parameter space. We illustrate our method in analysis of presurgical fMRI data, and show in simulation that it infers the mean intensity more accurately than alternatives that use either the high or standard resolution fMRI data alone.

A Data-Driven Approach to Predicting 5-Aminolevulinic Acid-Induced Fluorescence and World Health Organization Grade in Newly Diagnosed Diffuse Gliomas.

BACKGROUND: A growing body of evidence has revealed the potential utility of 5-aminolevulinic acid (5-ALA) as a surgical adjunct in selected lower-grade gliomas. However, a reliable means of identifying which lower-grade gliomas will fluoresce has not been established. OBJECTIVE: To identify clinical and radiological factors predictive of intraoperative fluorescence in intermediate-grade gliomas. In addition, given that higher-grade gliomas are more likely to fluoresce than lower-grade gliomas, we also sought to develop a means of predicting glioma grade. METHODS: We investigated a cohort of patients with grade II and grade III gliomas who received 5-ALA before resection at a single institution. Using a logistic regression-based model, we evaluated 14 clinical and molecular variables considered plausible determinants of fluorescence. We then distilled the most predictive features to develop a model for predicting both fluorescence and tumor grade. We also explored the relationship between intraoperative fluorescence and diagnostic molecular markers. RESULTS: One hundered seventy-nine subjects were eligible for inclusion. Our logistic regression classifier accurately predicted intraoperative fluorescence in our cohort with 91.9% accuracy and revealed enhancement as the singular variable in determining intraoperative fluorescence. There was a direct relationship between enhancement on MRI and the likelihood of observed fluorescence. Observed fluorescence correlated with MIB-1 index but not with isocitrate dehydrogenase (IDH) status, 1p19q codeletion, or methylguanine DNA methyltransferase promoter methylation. CONCLUSION: We demonstrate a strong correlation between enhancement on preoperative MRI and the likelihood of visible fluorescence during surgery in patients with intermediate-grade glioma. Our analysis provides a robust method for predicting 5-ALA-induced fluorescence in patients with grade II and grade III gliomas.

A spatial Bayesian latent factor model for image-on-image regression.

Image-on-image regression analysis, using images to predict images, is a challenging task, due to (1) the high dimensionality and (2) the complex spatial dependence structures in image predictors and image outcomes. In this work, we propose a novel image-on-image regression model, by extending a spatial Bayesian latent factor model to image data, where low-dimensional latent factors are adopted to make connections between high-dimensional image outcomes and image predictors. We assign Gaussian process priors to the spatially varying regression coefficients in the model, which can well capture the complex spatial dependence among image outcomes as well as that among the image predictors. We perform simulation studies to evaluate the out-of-sample prediction performance of our method compared with linear regression and voxel-wise regression methods for different scenarios. The proposed method achieves better prediction accuracy by effectively accounting for the spatial dependence and efficiently reduces image dimensions with latent factors. We apply the proposed method to analysis of multimodal image data in the Human Connectome Project where we predict task-related contrast maps using subcortical volumetric seed maps.

Exposure-Focused CBT Outperforms Relaxation-Based Control in an RCT of Treatment for Child and Adolescent Anxiety.

OBJECTIVE: The relative contribution of individual cognitive behavioral therapy (CBT) components to treatment outcomes for child anxiety disorders (CADs) is unclear. Recent meta-analyses suggest that exposure may be the primary active ingredient in CBT for CADs, and that relaxation may be relatively less effective. This brief report tests the hypothesis that exposure-focused CBT (EF-CBT) would outperform a relaxation-based active therapy control (Relaxation Mentorship Training; RMT) for the treatment of CADs. METHOD: Participants were 102 youth with CADs (mean age = 11.91, 26 males; 76.4% White, 14.7% Multiracial, 3.9% Black, 3.9% Asian, 0.9% other/do not wish to identify) as part of an ongoing neuroimaging randomized controlled trial. Participants were randomly assigned (ratio 2:1) to receive 12 sessions of EF-CBT (n = 70) or RMT (n = 32). Clinical improvement was measured at Week 12 (Clinical Global Impression - Improvement scale; CGI-I); treatment response was defined as receiving a rating of "very much" or "much improved" on the CGI-I. Anxiety severity was measured at Weeks 1, 6, 9, 12 (Pediatric Anxiety Rating Scale; PARS). Outcome measures were completed by an independent evaluator unaware of condition. RESULTS: EF-CBT exhibited 2.98 times higher odds of treatment completion than RMT; 13 treatment non-completers were included in analyses. Estimated treatment response rates were higher for EF-CBT (57.3%) than for RMT (19.2%). Longitudinal analyses indicated that EF-CBT was associated with faster and more pronounced anxiety reductions than RMT on the PARS (Hedges' g = .77). CONCLUSIONS: Results suggest that EF-CBT without relaxation is effective for CADs, and more effective than a relaxation-based intervention.

A Comprehensive Assessment of 68Ga-PSMA-11 PET in Biochemically Recurrent Prostate Cancer: Results from a Prospective Multicenter Study on 2,005 Patients.

We prospectively investigated the performance of the prostate-specific membrane antigen (PSMA) ligand 68Ga-PSMA-11 for detecting prostate adenocarcinoma in patients with elevated levels of prostate-specific antigen (PSA) after initial therapy. Methods:68Ga-PSMA-11 hybrid PET was performed on 2,005 patients at the time of biochemically recurrent prostate cancer after radical prostatectomy (RP) (50.8%), definitive radiation therapy (RT) (19.7%), or RP with postoperative RT (PORT) (29.6%). The presence of prostate cancer was assessed qualitatively (detection rate = positivity rate) and quantitatively on a per-patient and per-region basis, creating a disease burden estimate from the presence or absence of local (prostate/prostate bed), nodal (N1: pelvis), and distant metastatic (M1: distant soft tissue and bone) disease. The primary study endpoint was the positive predictive value (PPV) of 68Ga-PSMA-11 PET/CT confirmed by histopathology. Results: After RP, the scan detection rate increased significantly with rising PSA level (44.8% at PSA < 0.25%-96.2% at PSA > 10 ng/mL; P < 0.001). The detection rate significantly increased with rising PSA level in each individual region, overall disease burden, prior androgen deprivation, clinical T-stage, and Gleason grading from the RP specimen (P < 0.001). After RT, the detection rate for in-gland prostate recurrence was 64.0%, compared with 20.6% prostate bed recurrence after RP and 13.3% after PORT. PSMA-positive pelvic nodal disease was detected in 42.7% after RP, 40.8% after PORT, and 38.8% after RT. In patients with histopathologic validation, the PPV per patient was 0.82 (146/179). The SUVmax of histologically proven true-positive lesions was significantly higher than that of false-positive lesions (median, 11.0 [interquartile range, 6.3-22.2] vs. 5.1 [interquartile range, 2.2-7.4]; P < 0.001). Conclusion: We confirmed a high PPV for 68Ga-PSMA-11 PET in biochemical recurrence and the PSA level as the main predictor of scan positivity.

Interpretations of Examinations Outside of Radiologists' Fellowship Training: Assessment of Discrepancy Rates Among 5.9 Million Examinations From a National Teleradiology Databank.

BACKGROUND. In community settings, radiologists commonly function as multispecialty radiologists, interpreting examinations outside of their area of fellowship training. OBJECTIVE. The purpose of this article was to compare discrepancy rates for preliminary interpretations of acute community-setting examinations that are concordant versus discordant with interpreting radiologists' area of fellowship training. METHODS. This retrospective study used the databank of a U.S. teleradiology company that provides preliminary interpretations for client community hospitals. The analysis included 5,883,980 acute examinations performed from 2012 to 2016 that were preliminarily interpreted by 269 teleradiologists with a fellowship of neuroradiology, abdominal radiology, or musculoskeletal radiology. When providing final interpretations, client on-site radiologists voluntarily submitted quality assurance (QA) requests if preliminary and final interpretations were discrepant; the teleradiology company's QA committee categorized discrepancies as major (n = 8444) or minor (n = 17,208). Associations among examination type (common vs advanced), relationship between examination subspecialty and the teleradiologist's fellowship (concordant vs discordant), and major and minor discrepancies were assessed using three-way conditional analyses with generalized estimating equations. RESULTS. For examinations with a concordant subspecialty, the major discrepancy rate was lower for common than for advanced examinations (0.13% vs 0.26%; relative risk [RR], 0.50, 95% CI, 0.42-0.60; p < .001). For examinations with a discordant subspecialty, the major discrepancy rate was lower for common than advanced examinations (0.14% vs 0.18%; RR, 0.81; 95% CI, 0.72-0.90; p < .001). For common examinations, the major discrepancy rate was not different between examinations with concordant versus discordant subspecialty (0.13% vs 0.14%; RR, 0.90; 95% CI, 0.81-1.01; p = .07). For advanced examinations, the major discrepancy rate was higher for examinations with concordant versus discordant subspecialty (0.26% vs 0.18%; RR, 1.45; 95% CI, 1.18-1.79; p < .001). The minor discrepancy rate was higher among advanced examinations for those with concordant versus discordant subspecialty (0.34% vs 0.29%; RR, 1.17; 95% CI, 1.00-1.36; p = .04), but not different for other comparisons (p > .05). CONCLUSION. Major and minor discrepancy rates were not higher for acute community-setting examinations outside of interpreting radiologists' fellowship training. Discrepancy rates increased for advanced examinations. CLINICAL IMPACT. The findings support multispecialty radiologist practice in acute community settings. Efforts to match examination and interpreting radiologist sub-specialty may not reduce diagnostic discrepancies.

Applying hierarchical bayesian modeling to experimental psychopathology data: An introduction and tutorial.

Over the past 2 decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical science. Although Bayesian methods can be an attractive alternative to classical methods for answering certain research questions, they involve a heavy "overhead" (e.g., advanced mathematical methods, complex computations), which pose significant barriers to researchers interested in adding Bayesian methods to their statistical toolbox. To increase the accessibility of Bayesian methods for psychopathology researchers, this article presents a gentle introduction of the Bayesian inference framework and a tutorial on implementation. We first provide a primer on the key concepts of Bayesian inference and major implementation considerations related to Bayesian estimation. We then demonstrate how to apply hierarchical Bayesian modeling (HBM) to experimental psychopathology data. Using a real dataset collected from two clinical groups (schizophrenia and bipolar disorder) and a healthy comparison sample on a psychophysical gaze perception task, we illustrate how to model individual responses and group differences with probability functions respectful of the presumed underlying data-generating process and the hierarchical nature of the data. We provide the code with explanations and the data used to generate and visualize the results to facilitate learning. Finally, we discuss interpretation of the results in terms of posterior probabilities and compare the results with those obtained using a traditional method. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Aberrant activation of the mentalizing brain system during eye gaze discrimination in bipolar disorder.

Bipolar disorder (BD) is associated with a range of social cognitive deficits. This study investigated the functioning of the mentalizing brain system in BD probed by an eye gaze perception task during fMRI. Compared with healthy controls (n = 21), BD participants (n = 14) showed reduced preferential activation for self-directed gaze discrimination in the medial prefrontal cortex (mPFC) and temporo-parietal junction (TPJ), which was associated with poorer cognition/social cognition. Aberrant functions of the mentalizing system should be further investigated as marker of social dysfunction and treatment targets.

Improved detection of air trapping on expiratory computed tomography using deep learning.

BACKGROUND: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. OBJECTIVE: To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. MATERIALS AND METHODS: Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. RESULTS: QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. CONCLUSION: The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma.

RATIONALE AND OBJECTIVES: Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression. MATERIALS AND METHODS: We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRMrFLAIR+), decreased (PRMrFLAIR-), or unchanged (PRMrFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRMrFLAIR between weeks 10 and 20 and continuously until the PRMrFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection. RESULTS: Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRMrFLAIR+ (PFS: p <0.01; OS: p <0.001). PRMrFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRMrFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001). CONCLUSION: PRMrFLAIR may provide an early biomarker of disease progression in glioblastoma.

Disrupted Eye Gaze Perception as a Biobehavioral Marker of Social Dysfunction: An RDoC Investigation.

Social dysfunction is an intractable problem in a wide spectrum of psychiatric illnesses, undermining patients' capacities for employment, independent living, and maintaining meaningful relationships. Identifying common markers of social impairment across disorders and understanding their mechanisms are prerequisites to developing targeted neurobiological treatments that can be applied productively across diagnoses and illness stages to improve functional outcome. This project focuses on eye gaze perception, the ability to accurately and efficiently discriminate others' gaze direction, as a potential biomarker of social functioning that cuts across psychiatric diagnoses. This premise builds on both the monkey and human literatures showing gaze perception as a basic building block supporting higher-level social communication and social development, and reports of abnormal gaze perception in multiple psychiatric conditions accompanied by prominent social dysfunction (e.g., psychosis-spectrum disorders, autism-spectrum disorders, social phobia). A large sample (n = 225) of adolescent and young adult (age 14-30) psychiatric patients (regardless of diagnosis) with various degrees of impaired social functioning, and demographically-matched healthy controls (n = 75) will be recruited for this study. Participant's psychiatric phenotypes, cognition, social cognition, and community functioning will be dimensionally characterized. Eye gaze perception will be assessed using a psychophysical task, and two metrics (precision, self-referential bias) that respectively tap into gaze perception disturbances at the visual perceptual and interpretation levels, independent of general deficits, will be derived using hierarchical Bayesian modeling. A subset of the participants (150 psychiatric patients, 75 controls) will additionally undergo multimodal fMRI to determine the functional and structural brain network features of altered gaze perception. The specific aims of this project are three-fold: (1) Determine the generality of gaze perception disturbances in psychiatric patients with prominent social dysfunction; (2) Map behavioral indices of gaze perception disturbances to dimensions of psychiatric phenotypes and core functional domains; and (3) Identify the neural correlates of altered gaze perception in psychiatric patients with social dysfunction. Successfully completing these specific aims will identify the specific basic deficits, clinical profile, and underlying neural circuits associated with social dysfunction that can be used to guide targeted, personalized treatments, thus advancing NIMH's Strategic Objective 1 (describe neural circuits associated with mental illnesses and map the connectomes for mental illnesses) and Objective 3 (develop new treatments based on discoveries in neuroscience and behavioral science).

Estimating the prevalence of missing experiments in a neuroimaging meta-analysis.

Coordinate-based meta-analyses (CBMA) allow researchers to combine the results from multiple functional magnetic resonance imaging experiments with the goal of obtaining results that are more likely to generalize. However, the interpretation of CBMA findings can be impaired by the file drawer problem, a type of publication bias that refers to experiments that are carried out but are not published. Using foci per contrast count data from the BrainMap database, we propose a zero-truncated modeling approach that allows us to estimate the prevalence of nonsignificant experiments. We validate our method with simulations and real coordinate data generated from the Human Connectome Project. Application of our method to the data from BrainMap provides evidence for the existence of a file drawer effect, with the rate of missing experiments estimated as at least 6 per 100 reported. The R code that we used is available at https://osf.io/ayhfv/.

Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children (Erratum).

The erratum concerns amendment of Section 2.5 of the published article.

Lung T1 mapping magnetic resonance imaging in the assessment of pulmonary disease in children with cystic fibrosis: a pilot study.

BACKGROUND: Assessment tools for early cystic fibrosis (CF) lung disease are limited. Detecting early pulmonary disease is crucial to increasing life expectancy by starting interventions to slow the progression of the pulmonary disease with the many treatment options available. OBJECTIVE: To compare the utility of lung T1-mapping MRI with ultrashort echo time (UTE) MRI in children with cystic fibrosis in detecting early stage lung disease and monitoring pulmonary exacerbations. MATERIALS AND METHODS: We performed a prospective study in 16 children between September 2017 and January 2018. In Phase 1, we compared five CF patients with normal spirometry (mean 11.2 years) to five age- and gender-matched healthy volunteers. In Phase 2, we longitudinally evaluated six CF patients (median 11 years) in acute pulmonary exacerbation. All children had non-contrast lung T1-mapping and UTE MRI and spirometry testing. We compared the mean normalized T1 value and percentage lung volume without T1 value in CF patients and healthy subjects in Phase 1 and during treatment in Phase 2. We also performed cystic fibrosis MRI scoring. We evaluated differences in continuous variables using standard statistical tests. RESULTS: In Phase 1, mean normalized T1 values of the lung were significantly lower in CF patients in comparison to healthy controls (P=0.02) except in the right lower lobe (P=0.29). The percentage lung volume without T1 value was also significantly higher in CF patients (P=0.006). UTE MRI showed no significant differences between CF patients and healthy volunteers (P=0.11). In Phase 2, excluding one outlier case who developed systemic disease in the course of treatment, the whole-lung T1 value increased (P=0.001) and perfusion scoring improved (P=0.02) following therapy. We observed no other significant changes in the MRI scoring. CONCLUSION: Lung T1-mapping MRI can detect early regional pulmonary CF disease in children and might be helpful in the assessment of acute pulmonary exacerbations.