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Evidence Synthesis in Cancer Imaging

Guest Editors:
Qi Yong H. AI: The Hong Kong Polytechnic University, Hong Kong
Valerio Di Paola: Fondazione Policlinico Universitario Agostino Gemelli, Italy
Natale Quartuccio: A.R.N.A.S. Ospedali Civico Di Cristina e Benfratelli, Italy

Submission Status: Open   |   Submission Deadline: 31 July 2023


BMC Medical Imaging  is calling for submissions to our Collection which will give researchers the opportunity to publish articles presenting evidence synthesis in the field of cancer imaging.

Meet the Guest Editors

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Qi Yong H. AI: The Hong Kong Polytechnic University, Hong Kong

Dr Qi Yong H. AI is an assistant professor for the Department of Health Technology and Informatics at The Hong Kong Polytechnic University. His research mainly focuses on clinical applications of head and neck radiology, particularly imaging markers on staging head and neck magnetic resonance imaging (MRI) of nasopharyngeal carcinoma (NPC) for outcome prediction. He also has applied new functional MRI techniques to clinical applications and is one of the first researchers to apply T1rho imaging to clinical head and neck cancer research. He has accumulated valuable experiences in the adoption of artificial intelligence (AI) to the head and neck imaging and oral and maxillofacial radiology for clinical applications and collaborating with bioengineers to develop AI algorithms that are practicable for clinical practice.
 


Valerio Di Paola: Fondazione Policlinico Universitario Agostino Gemelli, Italy
Dr Valerio Di Paola is a medical doctor working as radiologist at Fondazione Universitario Agostino Gemelli – IRCCS, Rome, Italy since 2017. His main clinical activity consists of diagnostic CT and MRI imaging, with particular regard to genitourinary pathology. His research activity is focused on MRI abdominal imaging of urologic and gynecological diseases, with particular interest in recent technical developments and applications, such as Diffusion Tensor Imaging (DTI) and Radiomics.

 

Natale Quartuccio: A.R.N.A.S. Ospedali Civico Di Cristina e Benfratelli, Italy
Dr Natale Quartuccio has been working as a visiting researcher and clinical research fellow in various research institutes in Italy (University of Turin), USA (Bradley-Alavi Student Fellowship at the Memorial Sloan Kettering Cancer Center, New York, USA) and UK (MD, Wolfson Molecular Imaging Centre - University of Manchester). He is currently working as a Nuclear Medicine Consultant in Palermo, Italy and the main topics of his research include nuclear medicine, PET imaging, and oncology.




About the collection

BMC Medical Imaging is calling for submissions to our Collection on evidence-based medicine in cancer imaging.

Evidence-based medicine is crucial, as clinical practice should not be based on findings from a single primary study without insight on their reproducibility.

Cancer imaging is an essential tool to guide clinicians in patient management, from diagnosis to treatment planning and follow-up. It involves specialists, including radiologists, nuclear medicine physicians, physicists, biomedical engineers, oncologists, radiation therapists and surgeons, representing the base for a multidisciplinary approach. Consequently, multidisciplinary approach based   The choice of the most appropriate imaging technique to investigate cancer among the multiple available options is not always straightforward for the clinicians.

This collection gives researchers the opportunity to publish articles presenting evidence synthesis in the field of cancer imaging. This includes systematic reviews and meta-analysis studies focusing on novel imaging and tracking methods, as well as on the evaluation of less cutting-edge or less recent methods applied to cancer research and clinical practice.

Please find below a non-exhaustive list of topics that will be considered:

  • Conventional and functional imaging in cancer management, which includes cancer detection, differentiation, treatment monitoring, radiation treatment planning and outcome prediction (short-term and long-term).
  • Clinical application of medical imaging to identify biological biomarkers (such as histological, immunological markers) in cancer management.
  • Cost-effectiveness analysis and change of management by means of imaging techniques.
  • Machine-learning based techniques, such as radiomics analysis and artificial intelligence, applied to medical imaging for cancer management.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system, SNAPP. During the submission process you will be asked whether you are submitting to a Collection, please select "Evidence Synthesis in Cancer Imaging" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.