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• Journal article
  Nan Y, Felder FN, Humphries S, Mackintosh JA, Grainge C, Jo HE, Goh N, Reynolds PN, Hopkins PMA, Navaratnam V, Moodley Y, Walters H, Ellis S, Keir G, Zappala C, Corte T, Glaspole I, Wells AU, Yang G, Walsh SLFet al., 2025,

  Prognostication in patients with idiopathic pulmonary fibrosis using quantitative airway analysis from HRCT: a retrospective study.

  , Eur Respir J, Vol: 66

  BACKGROUND: Predicting shorter life expectancy is crucial for prioritising antifibrotic therapy in fibrotic lung diseases (FLDs), where progression varies widely, from stability to rapid deterioration. This heterogeneity complicates treatment decisions, emphasising the need for reliable baseline measures. This study focuses on leveraging an artificial intelligence (AI) model to address heterogeneity in disease outcomes, focusing on mortality as the ultimate measure of disease trajectory. METHODS: This retrospective study included 1744 anonymised patients who underwent high-resolution computed tomography (HRCT) scanning. The AI model, SABRE (Smart Airway Biomarker Recognition Engine), was developed using data from patients with various lung diseases (n=460, including lung cancer, pneumonia, emphysema and fibrosis). Then, 1284 HRCT scans with evidence of diffuse FLD from the Australian Idiopathic Pulmonary Fibrosis Registry and Open Source Imaging Consortium were used for clinical analyses. Airway branches were categorised and quantified by anatomical structures and volumes, followed by multivariable analysis to explore the associations between these categories and patients' progression and mortality, adjusting for disease severity or traditional measurements. RESULTS: Cox regression identified SABRE-based variables as independent predictors of mortality and progression, even adjusting for disease severity (fibrosis extent, traction bronchiectasis extent and interstitial lung disease extent), traditional measures (forced vital capacity percentage predicted, diffusing capacity of the lung for carbon monoxide (D LCO) percentage predicted and composite physiological index), and previously reported deep learning algorithms for fibrosis quantification and morphological analysis. Combining SABRE with D LCO significantly improved prognosis utility, yielding an area under the curve of 0.852 at the first year and a C-index of 0.752. CONCLUSIONS: SABRE-based variables capture p

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• Journal article
  Zeyu T, Xing X, Wang G, Yang Get al., 2025,

  Enhancing super-resolution network efficacy in CT imaging: cost-effective simulation of training data

  , IEEE Open Journal of Engineering in Medicine and Biology, ISSN: 2644-1276

  Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring appropriate training data for these Super-Resolution (SR) models is challenging. Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs. However, these methods either rely on simplistic interpolation techniques that lack realism or on sinogram reconstruction, which requires the release of raw data and complex reconstruction algorithms. Thus, we introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms. The training pairs produced by our method closely resemble real data distributions (PSNR=49.74 vs. 40.66, p<0.05). A multivariate Cox regression analysis involving thick slice CT images with lung fibrosis revealed that only the radiomics features extracted using our method demonstrated a significant correlation with mortality (HR=1.19 and HR=1.14, p<0.005). This paper represents the first to identify and address the challenge of generating appropriate paired training data for Deep Learning-based CT SR models, which enhances the efficacy and applicability of SR models in real-world scenarios.

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• Journal article
  Vano LJ, McCutcheon RA, Sedlacik J, Rutigliano G, Kaar SJ, Finelli V, Lobo MC, Berry A, Statton B, Fazlollahi A, Everall IP, Howes ODet al., 2025,

  The role of low subcortical iron, white matter myelin, and oligodendrocytes in schizophrenia: a quantitative susceptibility mapping and diffusion tensor imaging study

  , MOLECULAR PSYCHIATRY, ISSN: 1359-4184
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• Journal article
  Ai R, Xiao X, Deng S, Yang N, Xing X, Watne LO, Wedatilake Y, Xie C, Selback G, Rubinsztein DC, Neerland BE, Palmer JE, Chen H, Niu Z, Yang G, Fang EFet al., 2025,

  Artificial intelligence in drug development for delirium and Alzheimer's disease

  , ACTA PHARMACEUTICA SINICA B, Vol: 15, Pages: 4386-4410, ISSN: 2211-3835
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• Conference paper
  Patel KHK, Bajaj N, Statton BK, Bishop MJ, Stoks J, Li X, Sau A, Brezitski M, Ware JS, Purkayastha S, O'Regan D, Coronel R, Cluitmans M, Lambiase PD, Ng FSet al., 2025,

  WEIGHT LOSS REVERSES ADVERSE STRUCTURAL, ELECTROPHYSIOLOGICAL AND AUTONOMIC REMODELLING IN OBESITY

  , Publisher: OXFORD UNIV PRESS, Pages: i3-i3, ISSN: 0032-5473
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• Journal article
  Li S, Zhuang B, Cui C, He J, Ren Y, Wang H, Francone M, Yang G, Mohiaddin R, Lu M, Xu Let al., 2025,

  Prognostic significance of myocardial fibrosis in men with alcoholic cardiomyopathy: insights from cardiac MRI

  , EUROPEAN RADIOLOGY, Vol: 35, Pages: 5594-5603, ISSN: 0938-7994
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  • Citations: 1
• Journal article
  Vano LJ, McCutcheon RA, Sedlacik J, Kaar SJ, Rutigliano G, Nordio G, Finelli V, Townsend L, Berry A, Statton B, Fazlollahi A, Veronese M, Hopkins SC, Koblan KS, Everall IP, Howes ODet al., 2025,

  Reduced Brain Iron and Striatal Hyperdopaminergia in Schizophrenia: A Quantitative Susceptibility Mapping MRI and PET Study

  , AMERICAN JOURNAL OF PSYCHIATRY, Vol: 182, Pages: 830-839, ISSN: 0002-953X
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  • Citations: 3
• Journal article
  Toma T, Tekle K, Smith J, Quest R, Kelly R, Lord C, Alavi Aet al., 2025,

  Enhancing the detection of paediatric ankle fractures with zero echo time imaging: A case of an occult salter-harris III ankle fracture

  , EMERGENCY RADIOLOGY, ISSN: 1070-3004
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• Journal article
  Zhu J, Liao Y, Chen Y, Gao F, Li R, Yang G, Lai X, Chen Jet al., 2025,

  Multimodal MRI-Based Glioma Segmentation and MGMT Promoter Methylation Status Prediction Using Multitask Learning Architecture

  , INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Vol: 35, ISSN: 0899-9457
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• Journal article
  Zahid U, Osugo M, Selvaggi P, Lythgoe DJ, Fortunato C, Diederen K, Kiemes A, Wall MB, Whitehurst T, Onwordi EC, Statton B, Berry A, Dimitrov M, Lau R, McCutcheon RA, Murray R, Marques TR, Mehta MA, Howes ODet al., 2025,

  The effects of dopamine receptor antagonist and partial agonist antipsychotics on the glutamatergic system: double-blind, randomised, placebo-controlled <SUP>1</SUP>H-MRS cross-over study in healthy volunteers

  , BRITISH JOURNAL OF PSYCHIATRY, ISSN: 0007-1250
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