'It was nothing that you would think was anything': Qualitative analysis of appraisal and help seeking preceding brain cancer diagnosis.

BACKGROUND: The patient's interpretation of the events and decisions leading up to consultation with a healthcare professional for symptoms of brain cancer is under researched. The aim of this study was to document responses to noticing the changes preceding a diagnosis of brain cancer and living with them, focusing on appraisal of changes and the decision to seek (and re-seek) help, with attention to the psychological processes underpinning the appraisal and help-seeking intervals. METHOD: In this qualitative study set in Eastern and NW England, in-depth interviews with adult patients recently diagnosed with primary brain cancer and their family members were analysed thematically, using the Model of Pathways to Treatment as a conceptual framework. RESULTS: 39 adult patients were interviewed. Regarding the appraisal interval, cognitive heuristics were found to underpin explanations of changes/symptoms. The subtlety and normality of changes often suggested nothing serious was wrong. Common explanations included stress or being busy at work, or age and these did not seem to warrant a visit to a doctor. Explanations and the decision to seek help were made within the social context, with friends, family and work colleagues contributing to appraisal and help-seeking decisions. Regarding the help-seeking interval, barriers to seeking help reflected components of Social Cognitive Theory, and included having other priorities, outcome expectations (e.g. 'feeling silly', not sure much can be done about it, not wanting to waste doctors' time) and accessibility of a preferred healthcare professional. CONCLUSION: Application of psychological theory facilitated understanding of the influences on cognition and behaviour. The study highlights implications for theory, awareness campaigns and potential opportunities promoting more timely help-seeking.the brain tumour charit

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.

BACKGROUND: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care. OBJECTIVE: This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated. METHODS: We searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. RESULTS: We identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures. CONCLUSIONS: AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.CRU

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer : Systematic Review

Acknowledgments This research was funded by the National Institute for Health Research (NIHR) Policy Research Programme, conducted through the Policy Research Unit in Cancer Awareness, Screening, and Early Diagnosis, PR-PRU-1217-21601. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by the CanTest Collaborative (funded by Cancer Research UK C8640/A23385), of which FW and WH are directors and JE, HS, and NdW are associate directors. HS is additionally supported by the Houston Veterans Administration Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety (CIN13-413) and the Agency for Healthcare Research and Quality (R01HS27363). The funding sources had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or the decision to submit for publication. The authors would like to thank Isla Kuhn, Reader Services Librarian, University of Cambridge Medical Library, for her help in developing the search strategy.Peer reviewedPublisher PD

Missed opportunities for diagnosing brain tumours in primary care: a qualitative study of patient experiences.

BACKGROUND: Brain tumours are uncommon, and have extremely poor outcomes. Patients and GPs may find it difficult to recognise early symptoms because they are often non-specific and more likely due to other conditions. AIM: To explore patients' experiences of symptom appraisal, help seeking, and routes to diagnosis. DESIGN AND SETTING: Qualitative study set in the East and North West of England. METHOD: In-depth interviews with adult patients recently diagnosed with a primary brain tumour and their family members were analysed thematically, using the Model of Pathways to Treatment as a conceptual framework. RESULTS: Interviews were carried out with 39 patients. Few participants (n = 7; 18%) presented as an emergency without having had a previous GP consultation; most had had one (n = 15; 38%), two (n = 9; 23%), or more (n = 8; 21%) GP consultations. Participants experienced multiple subtle 'changes' rather than 'symptoms', often noticed by others rather than the patient, which frequently led to loss of interest or less ability to engage with daily living activities. The most common changes were in cognition (speaking, writing, comprehension, memory, concentration, and multitasking), sleep, and other 'head feelings' such as dizziness. Not all patients experienced a seizure, and few seizures were experienced 'out of the blue'. Quality of communication in GP consultations played a key role in patients' subsequent symptom appraisal and the timing of their decision to re-consult. CONCLUSION: Multiple subtle changes and frequent GP visits often precede brain tumour diagnosis, giving possible diagnostic opportunities for GPs. Refined community symptom awareness and GP guidance could enable more direct pathways to diagnosis, and potentially improve patient experiences and outcomes

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers : A Systematic Review

Funding This study and the journal’s rapid service fee were supported by the CanTest Collaborative (funded by Cancer Research UK C8640/A23385) of which Fiona M. Walter is Director, Jon Emery is an Associate Director, Mike Messenger is co-investigator, and Natalia Calanzani and Garth Funston are researchers. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Paige Druce, Kristi Milley and Jon Emery are supported by the Cancer Australia Primary Care Collaborative Cancer Clinical Trials Group (PC4). Mike Messenger is funded by the NIHR Leeds In Vitro Diagnostic Co-operative (UK). No Open Access Fee was received by the journal for the publication of this article.Peer reviewedPublisher PD

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Lower Gastrointestinal Cancers: A Systematic Review and Meta-Analysis

Abstract: Introduction: Lower gastrointestinal (GI) cancers are a major cause of cancer deaths worldwide. Prognosis improves with earlier diagnosis, and non-invasive biomarkers have the potential to aid with early detection. Substantial investment has been made into the development of biomarkers; however, studies are often carried out in specialist settings and few have been evaluated for low-prevalence populations. Methods: We aimed to identify novel biomarkers for the detection of lower GI cancers that have the potential to be evaluated for use in primary care. MEDLINE, Embase, Emcare and Web of Science were systematically searched for studies published in English from January 2000 to October 2019. Reference lists of included studies were also assessed. Studies had to report on measures of diagnostic performance for biomarkers (single or in panels) used to detect colorectal or anal cancers. We included all designs and excluded studies with fewer than 50 cases/controls. Data were extracted from published studies on types of biomarkers, populations and outcomes. Narrative synthesis was used, and measures of specificity and sensitivity were meta-analysed where possible. Results: We identified 142 studies reporting on biomarkers for lower GI cancers, for 24,844 cases and 45,374 controls. A total of 378 unique biomarkers were identified. Heterogeneity of study design, population type and sample source precluded meta-analysis for all markers except methylated septin 9 (mSEPT9) and pyruvate kinase type tumour M2 (TuM2-PK). The estimated sensitivity and specificity of mSEPT9 was 80.6% (95% CI 76.6–84.0%) and 88.0% (95% CI 79.1–93.4%) respectively; TuM2-PK had an estimated sensitivity of 81.6% (95% CI 75.2–86.6%) and specificity of 80.1% (95% CI 76.7–83.0%). Conclusion: Two novel biomarkers (mSEPT9 and TuM2-PK) were identified from the literature with potential for use in lower-prevalence populations. Further research is needed to validate these biomarkers in primary care for screening and assessment of symptomatic patients