Mayo Clinic Validation of the AUA Risk Groups for Localized Renal Cell Carcinoma.

PURPOSE: The AUA guidelines introduced a new risk group stratification system based primarily on tumor stage and grade to guide surveillance for patients treated surgically for localized renal cell carcinoma (RCC). We sought to evaluate the predictive ability of these risk groups using progression-free survival (PFS) and cancer-specific survival (CSS), and to compare their performance to that of our published institutional risk models. MATERIALS AND METHODS: We queried our Nephrectomy Registry to identify adults treated with radical or partial nephrectomy for unilateral, M0, clear cell RCC, or papillary RCC from 1980 to 2012. The AUA stratification does not apply to other RCC subtypes as tumor grading for other RCC, such as chromophobe, is not routinely performed. PFS and CSS were estimated using the Kaplan-Meier method. Predictive abilities were evaluated using C indexes from Cox proportional hazards regression models. RESULTS: A total of 3191 patients with clear cell RCC and 633 patients with papillary RCC were included. For patients with clear cell RCC, C indexes for the AUA risk groups and our model were 0.780 and 0.815, respectively (P < .001) for PFS, and 0.811 and 0.857, respectively (P < .001), for CSS. For patients with papillary RCC, C indexes for the AUA risk groups and our model were 0.775 and 0.751, respectively (P = .002) for PFS, and 0.830 and 0.803, respectively (P = .2) for CSS. CONCLUSIONS: The AUA stratification is a parsimonious system for categorizing RCC that provides C indexes of about 0.80 for PFS and CSS following surgery for localized clear cell and papillary RCC.

Automatic Detection of Cognitive Impairment with Virtual Reality.

Cognitive impairment features in neuropsychiatric conditions and when undiagnosed can have a severe impact on the affected individual's safety and ability to perform daily tasks. Virtual Reality (VR) systems are increasingly being explored for the recognition, diagnosis and treatment of cognitive impairment. In this paper, we describe novel VR-derived measures of cognitive performance and show their correspondence with clinically-validated cognitive performance measures. We use an immersive VR environment called VStore where participants complete a simulated supermarket shopping task. People with psychosis (k=26) and non-patient controls (k=128) participated in the study, spanning ages 20-79 years. The individuals were split into two cohorts, a homogeneous non-patient cohort (k=99 non-patient participants) and a heterogeneous cohort (k=26 patients, k=29 non-patient participants). Participants' spatio-temporal behaviour in VStore is used to extract four features, namely, route optimality score, proportional distance score, execution error score, and hesitation score using the Traveling Salesman Problem and explore-exploit decision mathematics. These extracted features are mapped to seven validated cognitive performance scores, via linear regression models. The most statistically important feature is found to be the hesitation score. When combined with the remaining extracted features, the multiple linear regression model resulted in statistically significant results with R2 = 0.369, F-Stat = 7.158, p(F-Stat) = 0.000128.

The temporal dynamics of sleep disturbance and psychopathology in psychosis: a digital sampling study.

BACKGROUND: Sleep disruption is a common precursor to deterioration and relapse in people living with psychotic disorders. Understanding the temporal relationship between sleep and psychopathology is important for identifying and developing interventions which target key variables that contribute to relapse. METHODS: We used a purpose-built digital platform to sample self-reported sleep and psychopathology variables over 1 year, in 36 individuals with schizophrenia. Once-daily measures of sleep duration and sleep quality, and fluctuations in psychopathology (positive and negative affect, cognition and psychotic symptoms) were captured. We examined the temporal relationship between these variables using the Differential Time-Varying Effect (DTVEM) hybrid exploratory-confirmatory model. RESULTS: Poorer sleep quality and shorter sleep duration maximally predicted deterioration in psychosis symptoms over the subsequent 1-8 and 1-12 days, respectively. These relationships were also mediated by negative affect and cognitive symptoms. Psychopathology variables also predicted sleep quality, but not sleep duration, and the effect sizes were smaller and of shorter lag duration. CONCLUSIONS: Reduced sleep duration and poorer sleep quality anticipate the exacerbation of psychotic symptoms by approximately 1-2 weeks, and negative affect and cognitive symptoms mediate this relationship. We also observed a reciprocal relationship that was of shorter duration and smaller magnitude. Sleep disturbance may play a causal role in symptom exacerbation and relapse, and represents an important and tractable target for intervention. It warrants greater attention as an early warning sign of deterioration, and low-burden, user-friendly digital tools may play a role in its early detection.

Physician review of image registration and normal structure delineation.

INTRODUCTION: Image registration and delineation of organs at risk (OARs) are key components of three-dimensional conformal (3DCRT) and intensity-modulated radiotherapy (IMRT) treatment planning. This study hypothesized that image registration and OAR delineation are often performed by medical physicists and/or dosimetrists and are not routinely reviewed by treating physicians. METHODS: An anonymous, internet-based survey of medical physicists and dosimetrists was distributed via the MEDPHYS and MEDDOS listserv groups. Participants were asked to characterize standard practices for completion and review of OAR contouring, target volume contouring, and image registration at their institution along with their personal training in these areas and level of comfort performing these tasks. Likert-type scales are reported as Median [Interquartile range] with scores ranging from 1 = "Extremely/All of the time" to 5 = "Not at all/Never." RESULTS: Two hundred and ninety-seven individuals responded to the survey. Overall, respondents indicated significantly less frequent physician review (3 [2-4] vs 2 [1-3]), and less confidence in the thoroughness of physician review (3 [2-4] vs 2 [1-3], P < 0.01) of OAR contours compared to image registration. Only 19% (95% CI 14-24%) of respondents reported a formal process by which OAR volumes are reviewed by physicians in their clinic. The presence of a formal review process was also associated with significantly higher perceived thoroughness of review of OAR volumes compared to clinics with no formal review process (2 [2-3] vs 3 [2-4], P < 0.01). CONCLUSION: Despite the critical role of OAR delineation and image registration in the 3DCRT and IMRT treatment planning process, physician review of these tasks is not always optimal. Radiotherapy clinics should consider implementation of formal processes to promote adequate physician review of OARs and image registrations to ensure the quality and safety of radiotherapy treatment plans.

Correction to: Skating on thin ice: pragmatic prescribing for medication refractory schizophrenia.

After publication of the original article [1], the authors have notified us that there was an oversight on acknowledging funding received for the study. They would like to mention that Professor Sukhi Shergill was funded by an ERC Consolidator Award.

Parsing the Roles of the Frontal Lobes and Basal Ganglia in Task Control Using Multivoxel Pattern Analysis.

Cognitive control has traditionally been associated with pFC based on observations of deficits in patients with frontal lesions. However, evidence from patients with Parkinson disease indicates that subcortical regions also contribute to control under certain conditions. We scanned 17 healthy volunteers while they performed a task-switching paradigm that previously dissociated performance deficits arising from frontal lesions in comparison with Parkinson disease, as a function of the abstraction of the rules that are switched. From a multivoxel pattern analysis by Gaussian Process Classification, we then estimated the forward (generative) model to infer regional patterns of activity that predict Switch/Repeat behavior between rule conditions. At 1000 permutations, Switch/Repeat classification accuracy for concrete rules was significant in the BG, but at chance in the frontal lobe. The inverse pattern was obtained for abstract rules, whereby the conditions were successfully discriminated in the frontal lobe but not in the BG. This double dissociation highlights the difference between cortical and subcortical contributions to cognitive control and demonstrates the utility of multivariate approaches in investigations of functions that rely on distributed and overlapping neural substrates.

Realising stratified psychiatry using multidimensional signatures and trajectories.

BACKGROUND: Stratified or personalised medicine targets treatments for groups of individuals with a disorder based on individual heterogeneity and shared factors that influence the likelihood of response. Psychiatry has traditionally defined diagnoses by constellations of co-occurring signs and symptoms that are assigned a categorical label (e.g. schizophrenia). Trial methodology in psychiatry has evaluated interventions targeted at these categorical entities, with diagnoses being equated to disorders. Recent insights into both the nosology and neurobiology of psychiatric disorder reveal that traditional categorical diagnoses cannot be equated with disorders. We argue that current quantitative methodology (1) inherits these categorical assumptions, (2) allows only for the discovery of average treatment response, (3) relies on composite outcome measures and (4) sacrifices valuable predictive information for stratified and personalised treatment in psychiatry. METHODS AND FINDINGS: To achieve a truly 'stratified psychiatry' we propose and then operationalise two necessary steps: first, a formal multi-dimensional representation of disorder definition and clinical state, and second, the similar redefinition of outcomes as multidimensional constructs that can expose within- and between-patient differences in response. We use the categorical diagnosis of schizophrenia-conceptualised as a label for heterogeneous disorders-as a means of introducing operational definitions of stratified psychiatry using principles from multivariate analysis. We demonstrate this framework by application to the Clinical Antipsychotic Trials of Intervention Effectiveness dataset, showing heterogeneity in both patient clinical states and their trajectories after treatment that are lost in the traditional categorical approach with composite outcomes. We then systematically review a decade of registered clinical trials for cognitive deficits in schizophrenia highlighting existing assumptions of categorical diagnoses and aggregate outcomes while identifying a small number of trials that could be reanalysed using our proposal. CONCLUSION: We describe quantitative methods for the development of a multi-dimensional model of clinical state, disorders and trajectories which practically realises stratified psychiatry. We highlight the potential for recovering existing trial data, the implications for stratified psychiatry in trial design and clinical treatment and finally, describe different kinds of probabilistic reasoning tools necessary to implement stratification.