ORCID Profile
0000-0001-8139-3480
Current Organisation
University of Oxford
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Publisher: Elsevier BV
Date: 04-2020
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 29-12-2020
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 26-05-2022
DOI: 10.1186/S13244-022-01161-3
Abstract: Positron emission tomography (PET) imaging is a costly tracer-based imaging modality used to visualise abnormal metabolic activity for the management of malignancies. The objective of this study is to demonstrate that non-contrast CTs alone can be used to differentiate regions with different Fluorodeoxyglucose (FDG) uptake and simulate PET images to guide clinical management. Paired FDG-PET and CT images ( n = 298 patients) with diagnosed head and neck squamous cell carcinoma (HNSCC) were obtained from The cancer imaging archive. Random forest (RF) classification of CT-derived radiomic features was used to differentiate metabolically active (tumour) and inactive tissues (ex. thyroid tissue). Subsequently, a deep learning generative adversarial network (GAN) was trained for this CT to PET transformation task without tracer injection. The simulated PET images were evaluated for technical accuracy (PERCIST v.1 criteria) and their ability to predict clinical outcome [(1) locoregional recurrence, (2) distant metastasis and (3) patient survival]. From 298 patients, 683 hot spots of elevated FDG uptake (elevated SUV, 6.03 ± 1.71) were identified. RF models of intensity-based CT-derived radiomic features were able to differentiate regions of negligible, low and elevated FDG uptake within and surrounding the tumour. Using the GAN-simulated PET image alone, we were able to predict clinical outcome to the same accuracy as that achieved using FDG-PET images. This pipeline demonstrates a deep learning methodology to simulate PET images from CT images in HNSCC without the use of radioactive tracer. The same pipeline can be applied to other pathologies that require PET imaging.
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 02-06-2010
DOI: 10.1038/CLPT.2010.95
Abstract: Side effects account for most of the instances of failure of candidate drugs at late stages of development. These development failures contribute to the exorbitant cost of bringing new compounds to market: a single withdrawal can represent a loss of more than $1 billion. Many unwanted actions of drugs affect the heart, resulting in potentially proarrhythmic alteration of ion channel function. Because these can be fatal, potential electrophysiological cardiotoxicity is among the most stringent exclusion criteria in the licensing process.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 23-11-2020
Publisher: Public Library of Science (PLoS)
Date: 02-04-2014
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Vicente Grau.