Professor Emiliano Spezi

Biography

Education

2003: PhD (Medical Physics), University of Wales College of Medicine, Cardiff, UK
1998: MSc (Clinical Scientist), University of Bologna, Bologna, Italy
1996: Laurea (Physics), University of Bologna, Bologna, Italy

Honours and awards

Winner of the European Radiology ESGAR Silver Award 2018 (awarded in 2019)

• Foley et al Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. Eur Radiol. 2018 Jan;28(1):428-436

Winner of the ESTRO-Varian Research Award 2019

• Deist et al Distributed learning on 20 000+ lung cancer patients, Radiother Oncol (2019) Vol 133 Supp. 1, S287-8 https://www.estro.org/Congresses/ESTRO-38/Awards

Winner of the journal’s Best Paper Award 2018: European Journal of Nuclear Medicine and Molecular Imaging Physics

• Sjögreen Gleisner et al Variations in the practice of molecular radiotherapy and implementation of dosimetry: results from a European survey, EJNMMI Physics (2017) 4:28 https://doi.org/10.1186/s40658-017-0193-4

Winner of the Burgen Scholarship Award 2016: Academia Europaea

• Computational Models in Funtional Imaging and Radiation Therapy

Winner of the Best Physics Poster Award 2016: ESTRO

• Berthon et al Towards standardisation of PET-autosegmentaion with the ATLAAS machine learning algorithm, Radiother. Oncol. (2016) 119 (Supp 1): S452

Winner of the Manufactures' Award for Innovation 2015: IPEM

• ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography, Phys. Med. Biol. (2016) Jul 7;61(13):4855-69

Winner of IPEM/AAPM Travel award: IPEM/AAPM

• A Monte Carlo investigation of the accuracy of intensity modulated radiotherapy, Med. Phys. (2004) https://doi.org/10.1118/1.164451

Professional memberships

Fellow Member of the Institute of Physics and Engineering in Medicine (IPEM)

Chartered Member of the Instutte of Physics (IoP)

State Registered Clinical Scientist: Health and Care Professions Council (HCPC)

Academic positions

External examiner (research degrees): University of The Free State (South Africa), University of Cape Town (South Africa), Swansea University, Swansea (United Kingdom), Universitat Politècnica de Catalunya, Barcelona (Spain), National University of Ireland, Galway (Ireland), University of London, London (United Kingdom), Niels Bohr Institute, Copenhagen (Denmark).

Committees and reviewing

Current duties

Chair of the Research Ethics Committe of Cardiff University School of Engineering

Associate Editor of the European Journal of Medical Physics (Physica Medica), Elsevier

Member of the Welsh Government - Welsh Scientific Advisory Committee

Past duties

Vice-Chair of the American Association of Physicists in Medicine (AAPM) Task Group 211 Classification, Advantages and Limitations of the Auto-Segmentation Approaches for PET

Chair of theNational Cancer Research Institute (NCRI) Radiotherapy Trials QA Database solutions and IT subgroup

Member of the NCRI Clinical and Translational Radiotherapy Research Working Group (CTRad) Workstream 4: New Technology, Physics and Quality Assurance

Publications

We are currently unable to retrieve the list of publications. Visit our institutional repository.

Teaching

In addition to providing undergraduate project, dissertation and essay supervision, I am Module Organiser for the following modules at the School of Engineering:

• EN4505: Medical Image Processing (MEng)
• EN4506: Clinical Engineering 2 (MEng)

Between 2016 and 2021 I was also module organiser for the following module at the School of Physics and Astronomy:

• PX3247: Radiation for Medical Therapy (BSc)

Life Imaging and Data Analytics

Life Imaging and Data Analytics is a team with multi-disciplinary skills established at the Cardiff University School of Engineering. LIDA is led by Dr Emiliano Spezi (Professor of Healthcare Engineering) and consists of post-doctoral research associates and PhD students. The research interest of the group spans from advanced medical image processing and radiomics to advanced computer modelling in radiation oncology. In the field of medical imaging the team has developed ATLAAS, an award-winning machine learning based tool which can be used to select the optimal Positron Emission Tomography automated segmentation method for radiotherapy treatment planning.

Furthermore, we have established a programme of image analysis techniques, including segmentation, texture, shape and intensity analysis and wavelet analysis, combining this with clinical and genomic data to produce diagnostic, prognostic and predictive models. We have a very intensive programme of development of radiomics algorithms, which are advanced imaging techniques that allow non-invasive, high-throughput, three-dimensional extraction of large numbers of descriptive features from any volume of interest. LIDA is a founding member of the Image Biomarker Standardisation Initiative (IBSI) to develop standardised radiomics algorithms and reporting guidelines that can make radiomics analyses reproducible and comparable. We developed SPAARC radiomics, a tool for multimodal quantitative image analysis incorporating 164 features all compliant and validated in accordance with the IBSI recommendations. Features include morphology, intensity-based statistics, intensity and intensity volume histograms and grey level matrixes. This is a selected list of publications featuring SPAARC radiomics:

• Palumbo, D.et al. 2021. Prediction of early distant recurrence in upfront resectable pancreatic adenocarcinoma: A multidisciplinary, machine learning-based approach. Cancers13(19), article number: 4938. (10.3390/cancers13194938)
• Mori, M.et al. 2020. Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer. Radiotherapy and Oncology153, pp. 258-264. (10.1016/j.radonc.2020.07.003)
• Zwanenburg, A.et al. 2020. The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high throughput image-based phenotyping. Radiology295(2), pp. 328-338. (10.1148/radiol.2020191145)
• Piazzese, C.et al. 2019. Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer. PLoS ONE14(11), article number: e0225550. (10.1371/journal.pone.0225550)
• Whybra, P.et al. 2019. Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging. Scientific Reports9(1), article number: 9649. (10.1038/s41598-019-46030-0)

Watch a video on radiomics research: radiomics reseach

(credit: MAASTRO Clinic).

In addition, we are building an IT infrastructure and standardised algorithms that can be included in any machine learning training and validation process. The idea is that any developed and validated prognostic/predictive model can be included in libraries as part of a Decision Support System that can be used in real time by clinicians in the clinic. LIDA and Velindre University NHS Trust were the recipients of the NHS Innovation Award 2018 (Welsh Government, Efficiency Through Technology programme). As part of the “AI Solutions for Personalised Radiotherapy” (ASPIRE) project LIDA, Velindre and Intel Corporation are working on a project aimed at training and validating AI software for the automated delineation of tumour volumes on anatomical and functional imaging modalities. The training is designed to be performed on a large retrospective dataset of labelled clinical scans and will be validated on a prospective dataset acquired throughout the duration of the project. In addition to reducing dramatically the workload in radiotherapy planning, we expect AI auto-segmented volumes to be of equal quality and more consistent than those outlined manually, which our research group have investigated extensively. The integration of AI in the clinical radiotherapy workflow will prepare the ground for future developments related to high throughput medical image analysis (radiomics), development of a fully automated radiotherapy workflow (from AI-based automated segmentation to AI-based automated planning), and development of decision support system for clinicians to use in clinical practice. LIDA and Velindre are also partners on a project (FAST-RTP2) aimed at including AI in the process of automating the preparation of external radiotherapy plans.

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. Large datasets from a broad range of different populations representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. In LIDA we use a distributed learning approach which was designed to address ethical and legal boundaries and to limit the impact of data privacy collaboration between research institutes. Cardiff University and The Christie NHS Foundation Trust are the only two UK centers participating to the European Computer Assisted Theragnostics project (EuroCAT) project and to the Community in Oncology for Rapid Learning (CORAL). CORAL includes almost 30 cancer centres worldwide (Netherlands, USA, UK, India, China, South-Africa, Australia, Italy, Germany, Belgium, Canada, Denmark). CORAL is based on a distributed approach in which data do not cross the firewall, where data is made semantically interoperable locally, and where centres allow applications to enter their firewall and use their data to answer a particular research questions, without any patient identifiable information being exposed/shared. Applications are focused on machine learning and modelling to predict for instance overall survival in a particular tumour site.

Watch a video on distributed learning in radiation oncology and on personal health trains:

distributed learning in radiation oncology

personal health train

(credit: MAASTRO Clinic).

Members of the LIDA team

Dr Philip Whybra (Research associate)

Miss Iona Foster (PhD candidate)

Mr Emad Alsyed (PhD candidate)

Miss Elisabetta Cagni (PhD candidate)

Mr Kerim Duman (PhD candidate)

Associated members of the LIDA team

Prof John Staffurth (Professor Cinical Oncology, Cardiff University School of Medicine and Velindre Cancer Centre)

Dr Kieran Foley (Consultant Radiologist, Velindre Cancer Centre)

Contracts

Title	Role	Sponsor	Value	Duration
ASPIRE: AI Solutions for Personalised Radiotherapy (Efficiency Through Technology programme)	Co-PI	Welsh Government and Intel Corp	198,030	2018-2020
DOTATER+: Advanced Personalised 3D Dosimetry for a clinical trial in peptide radionuclide therapy	PI	Cancer Research Wales	80,341	2015-2018
ARENA: Extension of RTTQA outlining activity into the educational arena	Co-PI	Velindre NHS Trust	340,200	2018-2021
PEARL: PET-based Adaptive Radiotherapy Clinical Trial	Co-I	Cancer Research Wales	720,000	2017-2021
FAST-RTP2: Implementing automated techniques in radiotherapy treatment planning	Co-I	Velindre NHS Trust	76,430	2018-2021
STORM_GLIO: Developing Radiomics as an Imaging Biomarker in High Grade Glioma	Co-PI	Velindre NHS Trust	37,835	2018-2021
TEXRAD: Establishing image derived prognostic and predictive biomarkers of radiotherapy treatments and assessing treatment response using texture analysis	Co-PI	Velindre NHS Trust - Moondance Foundation	81,540	2017-2020

Past grants and contracts

Title: Informatics Platform for Advanced Cancer Imaging Research
Value: £18,638
Role: Principal Investigator
Period: 2017
Funding body: Data Innovation and Research Institute

Title: Raydose GUI development
Value: £26,646
Role: Principal Investigator
Period: 2015-2016
Funding body: EURAMET - Velindre NHS Trust

Title: Advanced FDG PET-CT target volume delineation in Intensity Modulated Radiotherapy planning for Head and Neck cancers
Value: £45,000
Role: Principal Investigator
Period: 2014-2015
Funding body: Cancer Research Wales

Title: 3-D Printed sources for High-Resolution Molecular Imaging
Value: £10,000
Role: Principal Investigator
Period: 2014-2015
Funding body: Velindre NHS Trust

Title: RAYDOSEPLAN a multimodality platform for treatment planning research in molecular radiotherapy
Value: €242,000
Role: Principal Investigator
Period: 2012-2015
Funding body: European Association of National Metrology Institutes (EURAMET)

Title: Adaptive Image-Guided Radiotherapy Strategies for Bladder and Cervical Cancer to Enable Dose Escalation and Reduce Late Toxicity
Value: £60,000
Role: Co-Investigator
Period: 2014-2015
Funding body: Cancer Research Wales

Title: XVI5IEC: Evaluation of patient dose to skin and eye lens for default CBCT settings
Value: £8,000
Role: Principal Investigator
Period: 2013
Funding body: Elekta Ltd Crawley UK

Title: XVIoptimal5: Evaluation of patient dose reduction and image quality for new Cone Beam CT settings
Value: £8,100
Role: Principal Investigator
Period: 2013
Funding body: Elekta Ltd Crawley UK

Title: MOZART: Parameters affecting tumour control and toxicity in oesophageal cancer: a multi-dimensional outcome analysis
Value: £67,000
Role: Principal Investigator
Period: 2012-2015
Funding body: Cancer Research Wales

Title: POSITIVE: Optimisation of positron emission tomography based target volume delineation in Head and Neck radiotherapy
Value: £81,000
Role: Principal Investigator
Period: 2011-2014
Funding body: Cancer Research Wales

Title: RAYDOSE: Assessment of patient dose using novel radioisotopes in Molecular Targeted Radiotherapy
Value: £170,000
Role: Principal Investigator
Period: 2010-2012
Funding body: Wales Office of Research and Development for Health and Social Care (WORD)

Title: A Comparison of Convolution/Superposition and Monte Carlo methods for conformal radiotherapy
Value: £80,000
Role: Co-Investigator
Period: 2009-2014
Funding body: Cancer Research Wales

Title: XVIctdi: Cone Beam CT dosimetry using an optimised phantom
Value: £20,000
Role: Principal Investigator
Period: 2009-2014
Funding body: Elekta Ltd Crawley UK

Title: Extending the RTGrid portal to the wider user community
Value: £52,000
Role: Co-Investigator
Period: 2009
Funding body: JISC ENGAGE e-Infrastructure programme

Title: MRI in radiotherapy planning
Value: £10,000
Role: Principal Investigator
Period: 2009
Funding body: Velindre NHS Trust

Title: The application of GafChromic film in routine and non routine quality control methods in radiotherapy physics
Value: £4,000
Role: Principal Investigator
Period: 2007
Funding body: Velindre NHS Trust

Supervision

Areas of interest

I am available to supervise Post Graduate Research students in the following areas:

• MEDICAL IMAGE ANALYSIS
• RADIOMICS
• MACHINE LEARNING IN RADIATION ONCOLOGY
• MONTE CARLO MODELLING OF RADIATION TRANSPORT
• MOLECULAR RADIOTHERAPY DOSIMETRY
• ADVANCED RADIOTHERAPY TECHNIQUES

PubMed search of publications by Professor Spezi (link).

Interdisciplinary Precision Oncology Cardiff Hub (IPOCH)

We offer 7 funded studentships available at the Interdisciplinary Doctoral Training Hub in Precision Oncology at Cardiff University. The studentships available are in biomedical imaging, pathology and genomics. These 3.5-year studentships provide funding for fees and stipend at the rate set by the UK Research Councils, as well as other research training and support costs, and are available to UK, EU and International students.

Find out more at IPOCH research website and FindAPhD.

Other opportunities for Post Graduate Research

We have the following exciting opportunity for self-funded PhD candidates

• Towards achieving sub-millimetre accuracy of human-tissue-equivalent synthetic tumours via digital manufacturing

If you are interested in enrolling on Postgraduate Research at the School og Engineering, contact the PGR Enquiries Team to know more about all current opportunities.

Title	Student	Status	Degree	Type	Role
IMPLEMENTING AUTOMATED TECHNIQUES IN RADIOTHERAPY TREATMENT PLANNING	FOSTER Iona	Current	PhD	Full time	Main supervisor
PET IMAGE TEXTURE ANALYSIS AND RADIOTHERAPY	ALSYED Emad	Current	PhD	Full time	Main supervisor
RADIOMICS ENHANCED DEEP LEARNING-BASED CLASSIFIER TO IMPROVE SURVIVAL IN GLIOBLASTOMA MULTIFORME	DUMAN Kerim	Current	PhD	Full time	Main supervisor
AUTOMATED PLANNING FOR IMAGE-GUIDED RADIOTHERAPY	CAGNI Elisabetta	Current	PhD	Full time	Main supervisor
OPTIMISATION OF IMAGE-BASED DOSIMETRY FOR Y90 SELECTIVE INTERNAL RADIATION THERAPY	YILDIRIM Mehmet	Current	PhD	Full time	Main supervisor

Current supervision

Past projects

Title	Student	Status	Degree	Type	Role
STANDARDISATION AND OPTIMISATION OF RADIOMIC TECHNIQUES FOR THE IDENTIFICATION OF ROBUST IMAGING BIOMARKERS IN ONCOLOGY	WHYBRA Philip	Completed	PhD	Full time	Main supervisor
PERSONALISED ADVANCED 3D DOSIMETRY IN PEPTIDE RECEPTOR RADIONUCLIDE THERAPY	BERENATO Salvatore	Completed	PhD	Full time	Main supervisor
ADVANCED AUTOMATED PET IMAGE SEGMENTATION IN RADIATION THERAPY	PARKINSON Craig	Completed	PhD	Full time	Main supervisor
DEVELOPMENT OF TECHNIQUES FOR VERIFICATION OF ADVANCED RADIOTHERAPY BY PORTAL DOSIMETRY	MD RADZI Yasmin	Completed	PhD	Full time	Main Supervisor
RADIOTHERAPY DOSE CALCULATION IN OESOPHAGAEL CANCER: COMPARISON OF ANALYTICAL AND MONTE CARLO METHODS	JOHNS Dewi	Completed	PhD	Part time	Main supervisor
PARAMETERS AFFECTING TUMOUR CONTROL AND TOXICITY IN OESOPHAGEAL CANCER: A MULTI-DIMENSIONAL OUTCOME ANALYSIS	CARRINGTON Rhys	Completed	PhD	Full time	Co supervisor
OPTIMISATION OF POSITRON EMISSION TOMOGRAPHY BASED TARGET VOLUME DELINEATION IN HEAD AND NECK RADIOTHERAPY	BERTHON Beatrice	Completed	PhD	Full time	Co supervisor