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Research background, gaps, and aim: Lung cancer (LC) is a research priority in the UK, due to its high incidence and mortality, and poor survival. Current population-based studies are focused on investigating ‘route to diagnosis’, factors associated with late diagnosis and poor survival, and the implications of different intervals (e.g. primary care interval, diagnostic interval, treatment interval) in the cancer care pathway. However, the longitudinal sequence of interdependent patient-GP events over time (patient’s help-seeking behaviours and general practitioner (GP) management) preceding cancer diagnosis is a research area less investigated. Therefore, this PhD study proposes a new perspective of studying primary care sequences for early diagnosis research, using a novel statistical method – sequence analysis (SA), to identify meaningful typologies in a less investigated population – patients at high risk but not yet diagnosed with LC. Methodology: A systematic scoping review was conducted to understand how SA has been applied to study disease trajectories and care pathways in health services research, to learn the lessons from published studies and inform the application of SA in the main study. - Study design, setting, and participants: 899 community patients at high risk of developing LC (based on patient's smoking history) but not yet diagnosed with LC from eight general practices in the South coast of England consented to participate in this study. Their primary care records from June 2010 to October 2012 (29 months) were reviewed. Information was extracted from GP notes in free text and transcribed manually. - Research process: Two study phases, methodological exploration and empirical analysis, were involved to address three research objectives: how to construct primary care sequences from discrete health events, how to use different features of SA to obtain meaningful cluster patterns (the outcome of SA), and how patients’ sociodemographic and clinical characteristics can help explain the variation in the cluster patterns and help-seeking behaviours. - The primary outcome, covariates, and statistical methods: SA and cluster analysis were used to obtain the primary outcome – typology of clusters. Descriptive statistics were used to characterise patient profiles for each cluster, followed by traditional statistical tests (ANOVA, chi-square test, Kruskal-Wallis test) to compare patients’ sociodemographic and clinical features among clusters. Generalised linear models were used to explore the association between patient characteristics and clusters, and to quantify the relative risk ratios. Key findings: The study sample was classified into seven clusters. The subgroups of patients who presented with potential LC symptoms were categorised into four clusters with different GP management: GP ordered tests or prescribed medications for transient symptoms (Cluster 1, n=133/899, 14.8%); GP ordered chest X-ray or referred patients to specialists (Cluster 2, n=65, 7.2%); GP offered health advice to patients (Cluster 3, n=60, 6.7%); and patients presented symptoms multiple times and received repeated prescriptions from GP (Cluster 4, n=37, 4.1%). Cluster 5 was patients without potential LC symptoms but presented in general practice with cardiorespiratory comorbidities and/or other alarm symptoms (n=326, 36.3%). Patients in Cluster 6 only had minor care needs (n=237, 26.4%). Patients in Cluster 7 did not visit GP at all during the whole study period (n=41, 4.6%). For patients who had ≥2 visits with potential LC symptoms, the median interval was 61.5 days, interquartile range [16, 217] days. Age and the number of comorbidities were the two significant variables in different models. Variation of patient management among practices (practice effect) was observed. Conclusions, clinical relevance, and implications: This PhD thesis has made an original contribution by establishing the feasibility and analytical framework of using SA to study complex primary care sequences in the field of early diagnosis research. This study demonstrates the potential of applying SA in a larger scale study with a more representative population, to investigate the complex and heterogeneous primary care sequences leading to LC diagnosis, which has clinical implications for patient care and management, promote early cancer diagnosis from primary care, and eventually improve LC survival in the UK.

BACKGROUND AND RESEARCH AIM: The incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings. METHODS: This population-based study uses the QResearch® database (version 46) and includes adult patients aged 25-84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008-30 June 2021). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox/competing risk regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal-external cross-validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated. DISCUSSION: The DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits.

Additional file 1. Co-investigators and members in the DeLIVER consortium.

Background: Pancreatic cancer has the worst survival rate among all cancers. Almost 70% of patients were diagnosed at Stage IV. Aim: This study aimed to investigate the symptoms associated with the diagnoses of pancreatic ductal adenocarcinoma (PDAC) and neuroendocrine neoplasms (PNEN), comparatively characterise the symptomatology between the two tumour types to inform earlier diagnosis. Design and Setting: A nested case-control study was conducted using data from the QResearch database. Patients aged ≥25 years and diagnosed with PDAC or PNEN during 2000-2019 were the cases. Up to 10 controls from the same general practice were matched with each case by age, sex, and calendar year using incidence density sampling. Methods: Conditional logistic regression was used to investigate the association between the forty-two shortlisted symptoms and the diagnoses of PDAC/PNEN in different timeframes relative to the index date, adjusting for patients’ sociodemographic characteristics, lifestyle, and relevant comorbidities. Results: There were 23,640 patients diagnosed with PDAC and 596 with PNEN. Twenty-three symptoms were significantly associated with PDAC, and nine symptoms with PNEN. Jaundice and gastrointestinal bleeding were the two alarm symptoms for both tumours. Thirst and dark urine were the two new identified symptoms for PDAC. The risk of unintentional weight loss may be longer than two years before the diagnosis of PNEN. Conclusion: PDAC and PNEN have overlapping symptom profiles. The QCancer (Pancreas) risk prediction model could be updated by including the newly identified symptoms and comorbidities, which could help GP identify high-risk patients for timely investigation in primary care.

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.\ud \ud Roche: Personalised lung cancer treatment modelling using electronic health records and genomics\ud \ud Cancer immunotherapy (CIT) is a promising new type of cancer treatment that uses the patient’s own immune system to fight cancer cells. CIT drugs work to stop the cancer cells from turning off the immune system’s T-cells by inhibiting the PD-L1 produced by the tumour cells (PD-L1 is a protein that binds to PD-1 receptors on T-cells and prevents the immune system from attacking the cancer cells).\ud \ud CIT is currently being used to treat patients with non-small cell lung cancer (NSCLC) for whom chemotherapy or other drugs have failed. CIT is also be-ing used as part of the first-line treatment in patients with advanced NSCLC (aNSCLC - stage III and higher). Theoretically, patients with high PD-L1 ex-pression levels are more likely to respond well to CIT; however, in practice, patient outcomes vary considerably.\ud \ud In this data study group, we investigated different approaches for predicting survival time for patients treated with CIT as first line of treatment, using both electronic health records and tumour genomic data. We also investigated the causal effects of CIT vs other oncology treatments, and studied treatment heterogeneity. The results contribute to identifying patients who are most likely to benefit from CIT.

Laterality of human brain varies under healthy aging and diseased conditions. The alterations in hemispheric asymmetry may embed distinct biomarkers linked to the disease dynamics. Statistical parametric mapping based on high‐resolution magnetic resonance imaging (MRI) and image processing techniques have allowed automated characterization of morphological features across the entire brain. In this study, 149 subjects grouped in healthy young, healthy elderly, mild cognitive impairment (MCI), and Alzheimer's disease (AD) were investigated using multivariate analysis for regional cerebral laterality indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume measured on high‐resolution MR images. Asymmetry alteration of MCI and AD were characterized by marked region‐specific reduction, while healthy elderly featured a distinct laterality shift in the limbic system in addition to regional asymmetry loss. Lack of the laterality shift in limbic system and early loss of asymmetry in entorhinal cortex may be biomarkers to identify preclinical AD among other dementia. Multivariate analysis of hemispheric asymmetry may provide information helpful for monitoring the disease progress and improving the management of MCI and AD. Hum Brain Mapp 34:3400–3410, 2013. © 2012 Wiley Periodicals, Inc.

Rationale and ObjectivesMorphologic changes of the human brain during healthy aging provide useful reference knowledge for age-related brain disorders. The aim of this study was to explore age-related global and regional morphological changes of healthy adult brains.Materials and MethodsT1-weighted magnetic resonance images covering the entire brain were acquired for 314 subjects. Image processing of registration, segmentation, and surface construction were performed to calculate the volumes of the cerebrum, cerebellum, brain stem, lateral ventricle, and subcortical nuclei, as well as the surface area, mean curvature index, cortical thickness of the cerebral cortex, and subjacent white matter volume using FreeSurfer software. Mean values of each morphologic index were calculated and plotted against age group for sectional analysis. Regression analysis was conducted using SPSS to investigate the age effects on global and regional volumes of human brain.ResultsOverall global and regional volume loss was observed for the entire brain during healthy aging. Moderate atrophy was observed in subcortical gray matter structures, including the thalamus (R2 = 0.476, P < .001), nucleus accumbens (R2 = 0.525, P < .001), pallidum (R2 = 0.461, P < .001), and putamen (R2 = 0.533, P < .001). The volume of hippocampus showed a slight increase by 40 years of age, followed by a relatively faster decline after the age of 50 years (R2 = 0.486, P < .001). Surface area and mean curvature were less affected by aging relative to cortical thickness and subjacent white matter volume. Significant cortical thinning was mainly found in the parietal (R2 = 0.553, P < .001) and insula regions (R2 = 0.405, P < .001).ConclusionsMorphologic alterations of human brain manifested regional heterogeneity in the scenario of general volume loss during healthy aging. The age effect on the hippocampus demonstrated a unique evolution. These findings provide informative reference knowledge that may help in identifying and differentiating pathologic aging and other neurologic disorders.