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Background: Quantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard. Method: The validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression. Result: Mean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®'s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94, and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2). Conclusion: QyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response. [ABSTRACT FROM AUTHOR]

Background: Quantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard. Method: The validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression. Result: Mean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®'s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94, and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2). Conclusion: QyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response. [ABSTRACT FROM AUTHOR]

Objectives: To compare the prevalence of physical morbidities among men and women with older adult bipolar disorder (OABD), and men with and without OABD.Methods: Cross-sectional analysis of the collaborative Global Aging & Geriatric Experiments in Bipolar Disorder (GAGE-BD) database and non-OABD data from the Health in Men Study. OABD defined as bipolar disorder among adults aged greater than or equal to 50 years. Outcomes of interest were diseases affecting the cardiovascular, respiratory, gastrointestinal, renal, musculoskeletal and endocrinological systems.Results: We examined 1407 participants with OABD aged 50-95 years, of whom 787 were women. More women than men showed evidence of morbidities affecting the respiratory, gastrointestinal, musculoskeletal and endocrinological systems. More men with than without OABD showed evidence of cardiovascular, renal and endocrinological diseases.Conclusion: GAGE-BD data showed that physical morbidities affect more women than men with OABD, and more men with than without OABD. The underlying reasons for these differences require clarification. [ABSTRACT FROM AUTHOR]

Background: The suboptimal selection of patients is a key challenge for disease‐modifying trials in mild cognitive impairment (MCI) and Alzheimer's disease. Improved selection strategies are urgently needed to better power trials. Recent advances in AI predictive modeling, such as the QyPredict® algorithm, are promising tools to improve the selection of patients likely to clinically progress during the clinical trial timeframe. Method: QyPredict® was applied to 519 MCI patients from ADNI: age: (71.8(±7.1), 303 Male, MMSE range 24‐30, CDR = 0.5, with available amyloid and APOE‐ε4 + status. QyPredict®, a tunable machine learning model, incorporated baseline QyScore® volumetric MRI results, demographic (age and sex) and clinical (MMSE and CDR) inputs. A QyPredict® score (0‐1) was produced for each individual, representing the probability they will demonstrate a modelled outcome (here an increase in CDR‐SOB of 0.5+ over 24 months). A score close to zero indicating very low probability of change. Predictive performance was evaluated using positive predictive value (PPV), sensitivity and specificity. Mean and standard deviation for change in CDR‐SOB over 24 months were calculated and compared using Mann‐Whitney test at increasing QyPredict® cut‐offs (0.1, 0.2, 0.3, 0.4 and 0.5). Actual change versus predicted change in CDR‐SB scores was further investigates for 'Stable' (QyPredict®<0.5) versus 'Decliners' (QyPredict®>0.5) (Figure 1). Sample sizes to detect a 30% treatment effect (reduction in change in CDR‐SOB) were calculated for the full sample and a cohort enriched with only 'Decliners'. Result: For the full sample without QyPredict® enrichment, change in CDR‐SOB at 24 months was 2.3 (±2.11), which significantly increase (p<0.001) from a QyPredict®>0.2, reaching a change of 3.4 (±2.30) points for MCI patients with QyPredict®>0.5. PPV, sensitivity and specificity were: 0.76, 0.70 and 0.75 (the full cohort); 0.83, 0.77 and 0.70 (Amyloid positive patients) and 0.82, 0.75 and 0.74 (APOE‐ε4 + patients). Sample sizes for a putative trial where 304 (full sample) compared with 110 (enriched cohort) per treatment arm. Conclusion: Using baseline QyScore® metrics, basic demographic, and typical clinical data available at screening visits, QyPredict® accurately modelled the likelihood of future clinical decline, resulting in substantially reduced patient cohorts to detect treatment effects when enriched with 'decliners'. [ABSTRACT FROM AUTHOR]

Background: The suboptimal selection of patients is a key challenge for disease‐modifying trials in mild cognitive impairment (MCI) and Alzheimer's disease. Improved selection strategies are urgently needed to better power trials. Recent advances in AI predictive modeling, such as the QyPredict® algorithm, are promising tools to improve the selection of patients likely to clinically progress during the clinical trial timeframe. Method: QyPredict® was applied to 519 MCI patients from ADNI: age: (71.8(±7.1), 303 Male, MMSE range 24‐30, CDR = 0.5, with available amyloid and APOE‐e4 + status. QyPredict®, a tunable machine learning model, incorporated baseline QyScore® volumetric MRI results, demographic (age and sex) and clinical (MMSE and CDR) inputs. A QyPredict® score (0‐1) was produced for each individual, representing the probability they will demonstrate a modelled outcome (here an increase in CDR‐SOB of 0.5+ over 24 months). A score close to zero indicating very low probability of change. Predictive performance was evaluated using positive predictive value (PPV), sensitivity and specificity. Mean and standard deviation for change in CDR‐SOB over 24 months were calculated and compared using Mann‐Whitney test at increasing QyPredict® cut‐offs (0.1, 0.2, 0.3, 0.4 and 0.5). Actual change versus predicted change in CDR‐SB scores was further investigates for 'Stable' (QyPredict®<0.5) versus 'Decliners' (QyPredict®>0.5) (Figure 1). Sample sizes to detect a 30% treatment effect (reduction in change in CDR‐SOB) were calculated for the full sample and a cohort enriched with only 'Decliners'. Result: For the full sample without QyPredict® enrichment, change in CDR‐SOB at 24 months was 2.3 (±2.11), which significantly increase (p<0.001) from a QyPredict®>0.2, reaching a change of 3.4 (±2.30) points for MCI patients with QyPredict®>0.5. PPV, sensitivity and specificity were: 0.76, 0.70 and 0.75 (the full cohort); 0.83, 0.77 and 0.70 (Amyloid positive patients) and 0.82, 0.75 and 0.74 (APOE‐e4 + patients). Sample sizes for a putative trial where 304 (full sample) compared with 110 (enriched cohort) per treatment arm. Conclusion: Using baseline QyScore® metrics, basic demographic, and typical clinical data available at screening visits, QyPredict® accurately modelled the likelihood of future clinical decline, resulting in substantially reduced patient cohorts to detect treatment effects when enriched with 'decliners'. [ABSTRACT FROM AUTHOR]

Purpose We tested the effects of tumor size, distribution and grade on progression-free survival in patients with upper tract urothelial carcinoma treated with flexible ureteroscopy with Ho:YAG laser photoablation. Materials and Methods Included in analysis were data on 92 consecutive patients with upper tract urothelial carcinoma treated with Ho:YAG laser photoablation from 2003 to 2015 at a single tertiary care referral center. Stringent followup was offered according to EAU (European Association of Urology) guidelines. Progression during followup was defined by tumor upgrading, distant metastases and/or a relapsing tumor that could not be completely removed with a conservative approach. Kaplan-Meier curves were used to assess the rate of disease progression according to tumor size (1 or less cm vs greater than 1 cm), tumor distribution (unifocal vs multifocal) and tumor grade (low vs high). Cox regression analysis was done to test the impact of clinical and pathological characteristics on the rate of progression-free survival. Results At a median followup of 52 months (IQR 27.8–76.4) the progression-free survival rate was 68% vs 72% in patients with a tumor size of 1 or less vs greater than 1 cm (p = 0.9), 72% vs 69% in patients with unifocal vs multifocal lesions (p = 0.6) and 75% vs 52% in patients with a low vs a high grade tumor (p = 0.03). On multivariable Cox regression analysis tumor grade at first treatment was the only independent predictor of disease progression (HR 5.16, 95% CI 1.19–22.26, p = 0.03). Conclusions High tumor grade independently decreased progression-free survival in patients with upper tract urothelial carcinoma treated with Ho:YAG laser photoablation. Tumor size greater than 1 cm and multifocality did not increase the risk of disease progression in patients treated conservatively with Ho:YAG laser photoablation. [ABSTRACT FROM AUTHOR]

Purpose: Patients with a single microfocus of prostate cancer at initial biopsy represent the ideal candidates for active surveillance. We investigate whether the number of cores taken affects the concordance rate between microfocus of prostate cancer and the confirmation of a pathologically insignificant prostate cancer at radical prostatectomy. Materials and Methods: Data were analyzed from 233 patients with a single microfocus of prostate cancer at initial transrectal prostate biopsy (a single focus of Gleason 6 involving 5% or less of the core) subsequently treated with radical prostatectomy. The chi-square test, cubic spline analyses and logistic regression analyses were used to depict the relationship between the number of cores taken and the probability of confirming the presence of an indolent disease (pathologically confirmed insignificant prostate cancer defined as radical prostatectomy Gleason score 6 or less, tumor volume 0.5 ml or less and organ confined disease). Results: Overall 65 patients (27.9%) showed pathologically confirmed insignificant prostate cancer at radical prostatectomy. The rate of pathologically confirmed insignificant prostate cancer was 3.8%, 29.6% and 39.4% in patients who underwent biopsy of 12 or fewer cores, 13 to 18 cores and 19 or more cores, respectively (p <0.001). After adjusting for the available confounders, age (p = 0.04), number of cores taken (p <0.001) and prostate specific antigen density (p <0.02) were independent predictors of pathologically confirmed insignificant prostate cancer. Conclusions: Of patients diagnosed with a single microfocus of prostate cancer the number of biopsy cores taken was a major independent predictor of having pathologically confirmed insignificant prostate cancer at radical prostatectomy. Therefore, when active surveillance is considered as a possible alternative in patients with microfocus of prostate cancer, the number of cores taken should be taken into account in decision making. [Copyright &y& Elsevier]

While brain structure during adolescence and early adulthood has been relatively well characterized in bipolar disorder (BD), it is still unclear how the brain's structure is affected by the illness in later life, and whether age of onset may influence age-related changes. High resolution structural magnetic resonance images were acquired at 3 Tesla in 103 individuals with BD and 98 healthy controls (HC), ages 40 to 79 years. Categorical group comparisons (BD vs. HC), and analyses for group differences in age-related patterns, were performed for cortical thickness and grey matter volume. The BD group was also subdivided by age of first mood symptom onset, being either age 25 years and over (adult-onset, n =21) or below age 25 years (adolescent-onset, n =82), and the BD subgroups were compared to controls in a three-way analysis. Compared to the HC group, the overall BD group was characterized by lower cortical thickness within the insula and parahippocampus regions (pcorrected = 0.0054), and lower grey matter volume within the hippocampus, thalamus and putamen (pcorrected = 0.001). Significant age-related differences were not detected between the overall BD group and HC group. However, when individuals with BD were divided by those with adolescent or adult onset of the illness, the adult-onset BD subgroup showed significant age-related declines in insula and parahippocampal thickness, and hippocampal, thalamic and striatal grey matter volume, compared to the early-onset BD subgroup and HC group (p = 0.021). The findings support structural abnormalities within the insula, parahippocampus, hippocampus, thalamus and striatum in adults with BD ages 40-79 years. Age-related decreases within the insula, parahippocampus and hippocampus may occur particularly in individuals with BD with onset after the age of 25 years. The results suggest that individuals with later onset of BD may be at a greater risk for age-related abnormalities in brain structure in brain systems that subserve emotional and cognitive processes. Funding for the project was awarded by the National Institute of Mental Health. [ABSTRACT FROM AUTHOR]