Special Issue "Radiomics and Texture Analysis in Medical Imaging"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 1 March 2022.

Special Issue Editors

Dr. Renato Cuocolo
E-Mail Website
Guest Editor
Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy
Interests: musculoskeletal imaging; prostate cancer; radiomics; machine learning; CT; MRI
Dr. Lorenzo Ugga
E-Mail Website
Guest Editor
Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
Interests: neuroradiology; machine learning; MRI; radiomics
Dr. Valeria Romeo
E-Mail Website
Guest Editor
Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
Interests: breast imaging; machine learning; radiomics; PET/MRI

Special Issue Information

Dear Colleagues,

Recent years have seen a rising interest in quantitative image analysis using techniques such as texture analysis. This has led to the introduction of the term radiomics, which has come to define large radiological image-derived feature sets, primed for exploration and analysis with data mining or machine learning approaches. The field of radiomics represents a great opportunity to extract additional value and information from medical imaging, beyond what physicians are used to assessing qualitatively or with current quantitative analyses. On the other hand, there is great uncertainty about the actual clinical value of information derived from radiomic features as questions are raised on their reproducibility and interpretability in biological terms. To allow for real-world applications of radiomic data, high-quality investigations both on the extraction process and data interpretation are still required. Out of large radiomic datasets, robust features must be identified, and their clinical value must be demonstrated.

This Special Issue will present and highlight high-quality studies focused on texture analysis and radiomics across a variety of imaging modalities and pathologies, to provide a valuable contribution to this field and aid its further progress towards clinical applicability.

Dr. Renato Cuocolo
Dr. Lorenzo Ugga
Dr. Valeria Romeo
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. Journal of Imaging is an international peer-reviewed open access monthly 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

  • radiomics
  • texture analysis
  • computed tomography
  • magnetic resonance imaging
  • neuroradiology
  • breast cancer
  • prostate cancer

Published Papers (4 papers)

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Research

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Article
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients
J. Imaging 2021, 7(2), 17; https://doi.org/10.3390/jimaging7020017 - 28 Jan 2021
Viewed by 606
Abstract
Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of [...] Read more.
Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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Article
Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies
J. Imaging 2020, 6(11), 125; https://doi.org/10.3390/jimaging6110125 - 19 Nov 2020
Cited by 13 | Viewed by 1074
Abstract
Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim [...] Read more.
Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources’ requirements. Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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Article
An Automated Method for Quality Control in MRI Systems: Methods and Considerations
J. Imaging 2020, 6(10), 111; https://doi.org/10.3390/jimaging6100111 - 18 Oct 2020
Cited by 1 | Viewed by 923
Abstract
Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of [...] Read more.
Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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Review

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Review
Radiomics and Prostate MRI: Current Role and Future Applications
J. Imaging 2021, 7(2), 34; https://doi.org/10.3390/jimaging7020034 - 11 Feb 2021
Cited by 2 | Viewed by 750
Abstract
Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data [...] Read more.
Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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