M. Lyasheva

Synergistic application of pulmonary 18F-FDG PET/HRCT and computer-based CT analysis with conventional severity measures to refine current risk stratification in idiopathic pulmonary fibrosis (IPF)

F. Fraioli, B. Ganeshan, R. Shortman, 2019, European Journal of Nuclear Medicine and Molecular Imaging.

A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

E. V. van Beek, S. Achenbach, S. Neubauer, 2019, European heart journal.

A novel CT-based radiotranscriptomic signature of perivascular fat improves cardiac risk prediction

S. Achenbach, S. Neubauer, M. Desai, 2019 .

Filtration-histogram based texture analysis and CALIPER based pattern analysis as quantitative CT techniques in idiopathic pulmonary fibrosis: head-to-head comparison.

F. Fraioli, B. Ganeshan, A. Groves, 2022, The British journal of radiology.

Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19

Calliope A. Dendrou, I. Pavord, G. Rodger, 2022, The Lancet Digital Health.

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

L. Rundo, F. Zaccagna, R. Lodi, 2022, Diagnostics.

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.

S. Neubauer, M. Desai, K. Channon, 2023, JACC. Cardiovascular imaging.

The use of multiparametric 18F-fluoro-l-3,4-dihydroxy-phenylalanine PET/MRI in post-therapy assessment of patients with gliomas

F. Zaccagna, F. Fraioli, H. Hyare, 2020, Nuclear medicine communications.