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Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model—the CanPredict (lung) model—for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005–March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004–Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25–84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R2 D ]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP] v2 , LLP v3 , Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO] M2012 , PLCO M2014 , Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55–74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R2 D in both sexes in the QResearch validation cohort and 59% of the R2 D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLP v2 and PLCO M2012), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk. The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme. Innovate UK (UK Research and Innovation). For the Chinese translation of the abstract see Supplementary Materials section. [ABSTRACT FROM AUTHOR]

Rationale and Objectives Noncontrast magnetic resonance angiography (NC-MRA) of pedal artery remains challenging because of the global and regional disease load, tissue integrity, and altered microcirculation. This study aims to investigate the feasibility of the NC-MRA of pedal arteries with flow-sensitive dephasing–prepared steady-state free precession (FSD-SSFP) and to explore the effect of disease load of type II diabetes on the vessel depiction. Materials and Methods FSD-SSFP was performed on a 1.5-T magnetic resonance system before the contrast-enhanced MRA (CE-MRA) as a reference standard in 39 consecutive diabetic subjects (29 men and 16 women, aged 57.9 ± 11.4 years). Two experienced radiologists evaluated the overall artery visibility (VA) and the contamination from soft tissue (SC) and veins (VC) with a four-point scale. Chronic complications and measures including random blood glucose (RBG), lipid panel, body mass index, risk of diabetic foot ulcers (RDF), and glycated hemoglobin (HbA1c) by the imaging were recorded as disease load indicators. Spearman rank correlation and ordinal regression were performed to investigate the effect of disease load on the depiction of pedal arteries. Results The measurement of RBG and RDF were significantly correlated with the VC in CE-MRA and with the overall visibility of pedal arteries in NC-MRA ( P < .025 and P < .001, respectively). Blood pressure was the only parameter that was significantly associated with SC in NC-MRA with FSD-SSFP ( P < .025). For CE-MRA the effect of RDF on the overall VA manifested a significant linear trend ( P < .001), and the level of RBG was substantially associated with the VC ( P < .025) without significantly impacting VA and SC. Hypertension only correlated with SC in NC-MRA. VA was found independent of the presence of diabetic nephropathy, coronary artery disease, abnormal lipid panel, HbA1c (75.0%), or optimized m 1 value that ranged from 70 to 160 mT⋅ms 2 /m (mean, 125 ± 18 mT⋅ms 2 /m) in this study. Conclusions FSD-SSFP proved to be a useful modality of NC-MRA for pedal artery imaging in diabetic patients. The vessel depiction is subject to the local and systemic disease load of type II diabetes. Technical optimization of the flow-sensitive dephasing gradient moment and properly choosing candidate would help augment the potential of this technique in patient care of peripheral artery disease. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses. Materials and Methods: T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection. Results: Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs. Conclusions: Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD. [Copyright &y& Elsevier]

Rationale and Objectives: Morphologic 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 Methods: T1-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. Results: Overall 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 (R 2 = 0.476, P < .001), nucleus accumbens (R 2 = 0.525, P < .001), pallidum (R 2 = 0.461, P < .001), and putamen (R 2 = 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 (R 2 = 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 (R 2 = 0.553, P < .001) and insula regions (R 2 = 0.405, P < .001). Conclusions: Morphologic 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. [Copyright &y& Elsevier]