Comparative Effectiveness of Psychosocial Protective Factors for Prostate Cancer Survivorship - A UK Biobank Study.

Background: This study investigates psychosocial and lifestyle factors to improve survival outcomes in prostate cancer patients.

Methods: From the UK Biobank cohort, 13,110 male prostate cancer subjects were analysed to examine the relationship between psychosocial and lifestyle factors and survival with a mean follow-up of 14.2 years from recruitment.

Ultra-low-field Balanced Steady-state Free Precession MRI at 0.05 Tesla.

Objective: The high cost and limited accessibility of MRI scanners remain significant barriers to their broader use in clinical settings. This study aims to demonstrate the feasibility of balanced steady-state free precession (bSSFP) imaging at ultra-low-field (ULF) on a highly simplified and low-cost 0.05 Tesla whole-body MRI scanner.

A deep learning sex-specific body composition ageing biomarker using dual-energy X-ray absorptiometry scan.

Background: Chronic diseases are closely linked to alterations in body composition, yet there is a need for reliable biomarkers to assess disease risk and progression. This study aimed to develop and validate a biological age indicator based on body composition derived from dual-energy X-ray absorptiometry (DXA) scans, offering a novel approach to evaluating health status and predicting disease outcomes.

Methods: A deep learning model was trained on a reference population from the UK Biobank to estimate body composition biological age (BCBA).

Multi-modal machine learning approach for early detection of neurodegenerative diseases leveraging brain MRI and wearable sensor data.

Neurodegenerative diseases, such as Alzheimer's and Parkinson's Disease, pose a significant healthcare burden to the aging population. Structural MRI brain parameters and accelerometry data from wearable devices have been proven to be useful predictors for these diseases but have been separately examined in the prior literature. This study aims to determine whether a combination of accelerometry data and MRI brain parameters may improve the detection and prognostication of Alzheimer's and Parkinson's disease, compared with MRI brain parameters alone.

MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis.

The Vision Transformer (ViT) has demonstrated remarkable performance in Self-Supervised Learning (SSL) for 3D medical image analysis. Masked AutoEncoder (MAE) for feature pre-training can further unleash the potential of ViT on various medical vision tasks. However, due to large spatial sizes with much higher dimensions of 3D medical images, the lack of hierarchical design for MAE may hinder the performance of downstream tasks.

A large model for non-invasive and personalized management of breast cancer from multiparametric MRI.

Breast Magnetic Resonance Imaging (MRI) demonstrates the highest sensitivity for breast cancer detection among imaging modalities and is standard practice for high-risk women. Interpreting the multi-sequence MRI is time-consuming and prone to subjective variation. We develop a large mixture-of-modality-experts model (MOME) that integrates multiparametric MRI information within a unified structure, leveraging breast MRI scans from 5205 female patients in China for model development and validation.

Association between moderate-to-vigorous physical activity and chronic disease risk in adults and elderly: insights from the UK Biobank study.

Background: This study aimed to determine the associations between different intensities of moderate to vigorous physical activity (MVPA) and the incidence of chronic diseases, and to assess the risk levels associated with these activities over time.

Methods: A prospective cohort study (UK Biobank Activity Project) with data collected between June 2013 and December 2015 included 59,896 adults (mean age = 59.68; male = 38.

Personalized progression modelling and prediction in Parkinson's disease with a novel multi-modal graph approach.

Parkinson's disease (PD) is a complex neurological disorder characterized by dopaminergic neuron degeneration, leading to diverse motor and non-motor impairments. This variability complicates accurate progression modelling and early-stage prediction. Traditional classification methods based on clinical symptoms are often limited by disease heterogeneity.

Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset.

Background: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accurate prediction model difficult due to the imbalanced nature of the dataset. This study introduces an innovative application of graph convolutional networks (GCNs) to predict COVID-19 patient survival using a highly imbalanced dataset.

Unsupervised Domain Adaptation for Low-Dose CT Reconstruction via Bayesian Uncertainty Alignment.

Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning (DL) is widely used in this problem, but the performance of testing data (also known as target domain) is often degraded in clinical scenarios due to the variations that were not encountered in training data (also known as source domain). Unsupervised domain adaptation (UDA) of LDCT reconstruction has been proposed to solve this problem through distribution alignment.

Stereotactic Body Radiotherapy and Liver Transplant for Liver Cancer: A Nonrandomized Controlled Trial.

Importance: Whether stereotactic body radiotherapy (SBRT) as a bridge to liver transplant for hepatocellular carcinoma (HCC) is effective and safe is still unknown.

Objective: To investigate the feasibility of SBRT before deceased donor liver transplant (DDLT) for previously untreated unresectable HCC.

Design, Setting, And Participants: In this phase 2 nonrandomized controlled trial conducted between June 1, 2015, and October 18, 2019, 32 eligible patients within UCSF (University of California, San Francisco) criteria underwent dual-tracer (18F-fluorodeoxyglucose and 11C-acetate [ACC]) positron emission tomography with computed tomography (PET-CT) and magnetic resonance imaging (MRI) with gadoxetate followed by SBRT of 35 to 50 Gy in 5 fractions, and the same imaging afterward while awaiting DDLT.

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions.

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities.

Machine learning prediction of hepatic steatosis using body composition parameters: A UK Biobank Study.

Non-alcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver disease worldwide, yet detection has remained largely based on surrogate serum biomarkers, elastography or biopsy. In this study, we used a total of 2959 participants from the UK biobank cohort and established the association of dual-energy X-ray absorptiometry (DXA)-derived body composition parameters and leveraged machine learning models to predict NAFLD. Hepatic steatosis reference was based on MRI-PDFF which has been extensively validated previously.

Radiologic extranodal extension for nodal staging in nasopharyngeal carcinoma.

Purpose: Extranodal extension (ENE) has the potential to add value to the current nodal staging system (N) for predicting outcome in nasopharyngeal carcinoma (NPC). This study aimed to incorporate ENE, as well as cervical nodal necrosis (CNN) to the current stage N3 and evaluated their impact on outcome prediction. The findings were validated on an external cohort.

Design and synthesis of chiral DOTA-based MRI contrast agents with remarkable relaxivities.

Due to the adverse effects of de-metallation in past concerning FDA-approved gadolinium-based contrast agents (GBCAs), researchers have been focusing on developing safer and more efficient alternatives that could avoid toxicity caused by free gadolinium ions. Herein, two chiral GBCAs, Gd-LS with sulfonate groups and Gd-T with hydroxyl groups, are reported as potential candidates for magnetic reasonance imaging (MRI). The r relaxivities of TSAP, SAP isomers of Gd-LS and SAP isomer of Gd-T at 1.

Photon-Counting CT Material Decomposition in Bone Imaging.

The accurate screening of osteoporosis is important for identifying persons at risk. The diagnosis of bone conditions using dual X-ray absorptiometry is limited to extracting areal bone mineral density (BMD) and fails to provide any structural information. Computed tomography (CT) is excellent for morphological imaging but not ideal for material quantification.

Metabolic biomarkers using nuclear magnetic resonance metabolomics assay for the prediction of aging-related disease risk and mortality: a prospective, longitudinal, observational, cohort study based on the UK Biobank.

The identification of metabolic biomarkers for aging-related diseases and mortality is of significant interest in the field of longevity. In this study, we investigated the associations between nuclear magnetic resonance (NMR) metabolomics biomarkers and aging-related diseases as well as mortality using the UK Biobank dataset. We analyzed NMR samples from approximately 110,000 participants and used multi-head machine learning classification models to predict the incidence of aging-related diseases.

Towards visceral fat estimation at population scale: correlation of visceral adipose tissue assessment using three-dimensional cross-sectional imaging with BIA, DXA, and single-slice CT.

Background: In terms of assessing obesity-associated risk, quantification of visceral adipose tissue (VAT) has become increasingly important in risk assessment for cardiovascular and metabolic diseases. However, differences exist in the accuracy of various modalities, with a lack of up-to-date comparison with three-dimensional whole volume assessment.

Aims: Using CT or MRI three-dimensional whole volume VAT as a reference, we evaluated the correlation of various commonly used modalities and techniques namely body impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA) as well as single slice CT to establish how these methods compare.

Optimal time for early therapeutic response prediction in nasopharyngeal carcinoma with functional magnetic resonance imaging.

Background And Purpose: Physiological changes in tumour occur much earlier than morphological changes. They can potentially be used as biomarkers for therapeutic response prediction. This study aimed to investigate the optimal time for early therapeutic response prediction with multi-parametric magnetic resonance imaging (MRI) in patients with nasopharyngeal carcinoma (NPC) receiving concurrent chemo-radiotherapy (CCRT).

Intraventricular 4D flow cardiovascular magnetic resonance for assessing patients with heart failure with preserved ejection fraction: a pilot study.

Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging. Intraventricular four-dimensional flow (4D flow) phase-contrast cardiovascular magnetic resonance (CMR) can assess different components of left ventricular (LV) flow including direct flow, delayed ejection, retained inflow and residual volume. This could be utilised to identify HFpEF.

Diagnostic accuracy of cardiovascular magnetic resonance strain analysis and atrial size to identify heart failure with preserved ejection fraction.

Aims: Heart failure with preserved ejection fraction (HFpEF) continues to be a diagnostic challenge. Cardiac magnetic resonance atrial measurement, feature tracking (CMR-FT), tagging has long been suggested to diagnose HFpEF and potentially complement echocardiography especially when echocardiography is indeterminate. Data supporting the use of CMR atrial measurements, CMR-FT or tagging, are absent.

Joint Cancer Segmentation and PI-RADS Classification on Multiparametric MRI Using MiniSegCaps Network.

MRI is the primary imaging approach for diagnosing prostate cancer. Prostate Imaging Reporting and Data System (PI-RADS) on multiparametric MRI (mpMRI) provides fundamental MRI interpretation guidelines but suffers from inter-reader variability. Deep learning networks show great promise in automatic lesion segmentation and classification, which help to ease the burden on radiologists and reduce inter-reader variability.

Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction.

Background: Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g.