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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.