Special Issue "Quantitative Imaging Network"

A special issue of Tomography (ISSN 2379-139X).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Chad Quarles
E-Mail Website
Guest Editor
Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
Interests: brain cancer imaging; brain tumor; clinical utility of bioimaging techniques; neuroimaging research; biophysics
Prof. Dr. Lubomir Hadjiiski
E-Mail Website
Guest Editor
Department of Radiology, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA
Interests: computer-aided diagnosis; neural networks; predictive models; image processing; medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Robert J. Nordstrom
E-Mail Website
Guest Editor
The Quantitative Imaging Network, The Cancer Imaging Program NCI, Rockville, MD 20892-9739, USA
Interests: quantitative imaging; benchmarking; image-based biomarkers; cancer therapy response assessment

Special Issue Information

Dear Colleagues,

The National Cancer Institute’s Quantitative Imaging Network (QIN) promotes research, development and clinical validation of quantitative imaging tools and methods for the measurement and prediction of tumor response to therapies in clinical trial settings; ultimately, its overall goal is to facilitate clinical decision-making. Projects include the development and adaptation/implementation of quantitative imaging methods, imaging protocols, and software solutions/tools, and application of these methods in the context of the standards of care patient management and in clinical therapy trials.

Dr. Chad Quarles
Dr. Lubomir Hadjiiski
Dr. Robert J. Nordstrom
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Quantitative imaging
  • Cancer imaging
  • Cancer therapy response assessment
  • Benchmarking
  • Image-based biomarkers

Published Papers (1 paper)

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Research

Article
Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies
Tomography 2021, 7(4), 877-892; https://0-doi-org.brum.beds.ac.uk/10.3390/tomography7040074 (registering DOI) - 03 Dec 2021
Viewed by 108
Abstract
Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we retrain an open-source convolutional [...] Read more.
Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we retrain an open-source convolutional neural network (CNN) to harmonize computerized tomography (CT) images with various reconstruction kernels to improve feature reproducibility and radiomic model performance using epidermal growth factor receptor (EGFR) mutation prediction in lung cancer as a paradigm. In the training phase, the CNN was retrained and tested on 32 lung cancer patients’ CT images between two different groups of reconstruction kernels (smooth and sharp). In the validation phase, the retrained CNN was validated on an external cohort of 223 lung cancer patients’ CT images acquired using different CT scanners and kernels. The results showed that the retrained CNN could be successfully applied to external datasets with different CT scanner parameters, and harmonization of reconstruction kernels from sharp to smooth could significantly improve the performance of radiomics model in predicting EGFR mutation status in lung cancer. In conclusion, the CNN based method showed great potential in improving feature reproducibility and generalizability by harmonizing medical images with heterogeneous reconstruction kernels. Full article
(This article belongs to the Special Issue Quantitative Imaging Network)
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