Computer-assisted quantification of caix membrane immunoreaction destined for the clear cells in renal carcinoma. A pilot study.

Introduction/ Background Carbonic Anhydrase IX [CAIX] has been considered as a candidate prognostic factor in clear-cell renal carcinoma [CCRC], however the supporting evidence is conflicting. CAIX is strongly induced by hypoxia via HIV-1α, and in CCRC via mutations to the VHL gene. CAIX expression could be identify as an immunohistochemical predictor of CCRC patients outcome but the published studies related to the patients prognosis have based on the diverse quantification protocols of CAIX expression (TMAs vs. whole tissue section; semiquantitative vs. computerised image analysis; with/without intensity scoring; with various software). The available commercial image analysis tools are mainly for general purpose e.g. software for breast carcinoma HER2 membrane immunoreaction has been used in various tumour tissue studies. However the cytological images of CCRC and breast carcinoma show essential differences related to the nuclei (size, outlines, intracellular location) and nuclear/cytoplasmic proportion which could influence the measurement credibility in maladjusted algorithm. Aims The aim of our study was to evaluate an algorithm for quantification of the membranous CAIX expression specifically dedicated to CCRC (“snake variant”) in comparative analysis to applied HER2 breast cancer algorithm for CCRC. Methods In the quantitative analysis of the specimen, the image processing follows: recognition of the cell nuclei; segmentation of the immunoreactive cell membranes; the assignment of the membrane segments to an individual cell. The last step is challenging for analysis due to frequent discontinuities in membranous immunoreaction, great variability of cellular counters and intracellular nuclei location. Because the classical watershed method for the individual cell separation is insufficient, the snake active contour method was applied, starting from each nucleus outline. The built gradient image allowed to select the most adequate parameters in the snake adaptation process. The recognized snake represents the membrane associated with the particular cell. The material includes records of 39 patients with the histopathologically verified diagnosis of CCRC who had nephrectomy (between 2009-2011) and were treated with tyrosine kinases agents (the Clinic of Oncology registry). 74% (29 out 39) of patients presented stage I - T1 N0; 20,5% - stage III and 5,4% stage TII. The formalin-fixed tissue sections of the resected CCRCs (the Pathology Department registry) were immunostained for CAIX protein using CAIX antibody (clone NB100-417) (Antibodies-online GmbH) with EnVisionTM (DAKO) according to the manufacture recommendations. The representative digital images were selected from each Whole Slide Image (scanned with Aperio, under 20x) and were assessed automatically by 3 independent observers using two algorithms: “snake variant” and “breast HER2”. The extend of staining (percentage) was scored in the 10% intervals of CAIX positive carcinomatous cells and the intensity of immunoreaction was evaluated in 3 grade scale (1-3). ResultsThe obtained results have been under investigation for the intra- and inter-observer accuracy as well as for the comparative data analysis of both types of algorithm. The statistical analysis has been incorporated. This approach explores a new possibility of the computerised quantitative estimation of the membrane CAIX immunoreaction destine

DNA methylation-based classification of glioneuronal tumours synergises with histology and radiology to refine accurate molecular stratification

AIMS: Glioneuronal tumours (GNTs) are poorly distinguished by their histology and lack robust diagnostic indicators. Previously, we showed that common GNTs comprise two molecularly distinct groups, correlating poorly with histology. To refine diagnosis, we constructed a methylation-based model for GNT classification, subsequently evaluating standards for molecular stratification by methylation, histology and radiology. METHODS: We comprehensively analysed methylation, radiology and histology for 83 GNT samples: a training cohort of 49, previously classified into molecularly defined groups by genomic profiles, plus a validation cohort of 34. We identified histological and radiological correlates to molecular classification and constructed a methylation-based support vector machine (SVM) model for prediction. Subsequently, we contrasted methylation, radiological and histological classifications in validation GNTs. RESULTS: By methylation clustering, all training and 23/34 validation GNTs segregated into two groups, the remaining 11 clustering alongside control cortex. Histological review identified prominent astrocytic/oligodendrocyte-like components, dysplastic neurons, and a specific glioneuronal element as discriminators between groups. However, these were present in only a subset of tumours. Radiological review identified location, margin definition, enhancement, and T2 FLAIR-rim sign as discriminators. When validation GNTs were classified by SVM, 22/23 classified correctly, comparing favourably against histology and radiology which resolved 17/22 and 15/21 respectively where data were available for comparison. CONCLUSIONS: Diagnostic criteria inadequately reflect glioneuronal tumour biology, leaving a proportion unresolvable. In the largest cohort of molecularly defined glioneuronal tumours, we develop molecular, histological, and radiological approaches for biologically meaningful classification and demonstrate almost all cases are resolvable, emphasising the importance of an integrated diagnostic approach

Implementing a digital intervention for managing uncontrolled hypertension in Primary Care: a mixed methods process evaluation

Background: A high proportion of hypertensive patients remain above the target threshold for blood pressure, increasing the risk of adverse health outcomes. A digital intervention to facilitate healthcare practitioners (hereafter practitioners) to initiate planned medication escalations when patients’ home readings were raised was found to be effective in lowering blood pressure over 12 months. This mixed-methods process evaluation aimed to develop a detailed understanding of how the intervention was implemented in Primary Care, possible mechanisms of action and contextual factors influencing implementation. Methods: One hundred twenty-five practitioners took part in a randomised controlled trial, including GPs, practice nurses, nurse-prescribers, and healthcare assistants. Usage data were collected automatically by the digital intervention and antihypertensive medication changes were recorded from the patients’ medical notes. A sub-sample of 27 practitioners took part in semi-structured qualitative process interviews. The qualitative data were analysed using thematic analysis and the quantitative data using descriptive statistics and correlations to explore factors related to adherence. The two sets of findings were integrated using a triangulation protocol. Results: Mean practitioner adherence to escalating medication was moderate (53%), and the qualitative analysis suggested that low trust in home readings and the decision to wait for more evidence influenced implementation for some practitioners. The logic model was partially supported in that self-efficacy was related to adherence to medication escalation, but qualitative findings provided further insight into additional potential mechanisms, including perceived necessity and concerns. Contextual factors influencing implementation included proximity of average readings to the target threshold. Meanwhile, adherence to delivering remote support was mixed, and practitioners described some uncertainty when they received no response from patients

Planning and optimising a digital intervention to protect older adults' cognitive health.

BackgroundBy 2050, worldwide dementia prevalence is expected to triple. Affordable, scalable interventions are required to support protective behaviours such as physical activity, cognitive training and healthy eating. This paper outlines the theory-, evidence- and person-based development of 'Active Brains': a multi-domain digital behaviour change intervention to reduce cognitive decline amongst older adults.MethodsDuring the initial planning phase, scoping reviews, consultation with PPI contributors and expert co-investigators and behavioural analysis collated and recorded evidence that was triangulated to inform provisional 'guiding principles' and an intervention logic model. The following optimisation phase involved qualitative think aloud and semi-structured interviews with 52 older adults with higher and lower cognitive performance scores. Data were analysed thematically and informed changes and additions to guiding principles, the behavioural analysis and the logic model which, in turn, informed changes to intervention content.ResultsScoping reviews and qualitative interviews suggested that the same intervention content may be suitable for individuals with higher and lower cognitive performance. Qualitative findings revealed that maintaining independence and enjoyment motivated engagement in intervention-targeted behaviours, whereas managing ill health was a potential barrier. Social support for engaging in such activities could provide motivation, but was not desirable for all. These findings informed development of intervention content and functionality that appeared highly acceptable amongst a sample of target users.ConclusionsA digitally delivered intervention with minimal support appears acceptable and potentially engaging to older adults with higher and lower levels of cognitive performance. As well as informing our own intervention development, insights obtained through this process may be useful for others working with, and developing interventions for, older adults and/or those with cognitive impairment

Implementing a digital intervention for managing uncontrolled hypertension in Primary Care: a mixed methods process evaluation.

BACKGROUND: A high proportion of hypertensive patients remain above the target threshold for blood pressure, increasing the risk of adverse health outcomes. A digital intervention to facilitate healthcare practitioners (hereafter practitioners) to initiate planned medication escalations when patients' home readings were raised was found to be effective in lowering blood pressure over 12 months. This mixed-methods process evaluation aimed to develop a detailed understanding of how the intervention was implemented in Primary Care, possible mechanisms of action and contextual factors influencing implementation. METHODS: One hundred twenty-five practitioners took part in a randomised controlled trial, including GPs, practice nurses, nurse-prescribers, and healthcare assistants. Usage data were collected automatically by the digital intervention and antihypertensive medication changes were recorded from the patients' medical notes. A sub-sample of 27 practitioners took part in semi-structured qualitative process interviews. The qualitative data were analysed using thematic analysis and the quantitative data using descriptive statistics and correlations to explore factors related to adherence. The two sets of findings were integrated using a triangulation protocol. RESULTS: Mean practitioner adherence to escalating medication was moderate (53%), and the qualitative analysis suggested that low trust in home readings and the decision to wait for more evidence influenced implementation for some practitioners. The logic model was partially supported in that self-efficacy was related to adherence to medication escalation, but qualitative findings provided further insight into additional potential mechanisms, including perceived necessity and concerns. Contextual factors influencing implementation included proximity of average readings to the target threshold. Meanwhile, adherence to delivering remote support was mixed, and practitioners described some uncertainty when they received no response from patients. CONCLUSIONS: This mixed-methods process evaluation provided novel insights into practitioners' decision-making around escalating medication using a digital algorithm. Implementation strategies were proposed which could benefit digital interventions in addressing clinical inertia, including facilitating tracking of patients' readings over time to provide stronger evidence for medication escalation, and allowing more flexibility in decision-making whilst discouraging clinical inertia due to borderline readings. Implementation of one-way notification systems could be facilitated by enabling patients to send a brief acknowledgement response. TRIAL REGISTRATION: ( ISRCTN13790648 ). Registered 14 May 2015

Barriers and facilitators to screening and treating malnutrition in older adults living in the community: A mixed-methods synthesis

Background. Malnutrition (specifically undernutrition) in older, community-dwelling adults reduces well-being and predisposes to disease. Implementation of screen-and-treat policies could help to systematically detect and treat at-risk and malnourished patients. We aimed to identify barriers and facilitators to implementing malnutrition screen and treat policies in primary/community care, which barriers have been addressed and which facilitators have been successfully incorporated in existing interventions. Method. A data-base search was conducted using MEDLINE, Embase, PsycINFO, DARE, CINAHL, Cochrane Central and Cochrane Database of Systematic Reviews from 2012 to June 2016 to identify relevant qualitative and quantitative literature from primary/community care. Studies were included if participants were older, community dwelling adults (65+) or healthcare professionals who would screen and treat such patients. Barriers and facilitators were extracted and mapped onto intervention features to determine whether these had addressed barriers. Results. Of a total of 2182 studies identified, 21 were included (6 qualitative, 12 quantitative and 3 mixed; 14 studies targeting patients and 7 targeting healthcare professionals). Facilitators addressing a wide range of barriers were identified, yet few interventions addressed psychosocial barriers to screen-and-treat policies for patients, such as loneliness and reluctance to be screened, or healthcare professionals’ reservations about prescribing oral nutritional supplements. Conclusion. The studies reviewed identified several barriers and facilitators and addressed some of these in intervention design, although a prominent gap appeared to be psychosocial barriers. No single included study addressed all barriers or made use of all facilitators, although this appears to be possible. Interventions aiming to implement screen-and-treat approaches to malnutrition in primary care should consider barriers that both patients and healthcare professionals may face

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions