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J. Imaging
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Radiomics and Texture Analysis in Medical Imaging
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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: closed (1 March 2022) | Viewed by 49925

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
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenzo Ugga

E-Mail Website
Guest Editor
Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
Interests: neuroradiology; neurological sciences; neurosurgery; brain tumor; head and neck pathology; radiomics
Special Issues, Collections and Topics in MDPI journals
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 submissions that pass pre-check are 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 1800 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 (15 papers)

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Research

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10 pages, 1057 KiB  
Article
Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen
by Jordan Sack, Jennifer Nitsch, Hans Meine, Ron Kikinis, Michael Halle and Anna Rutherford
J. Imaging 2022, 8(10), 277; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8100277 - 09 Oct 2022
Cited by 2 | Viewed by 1746
Abstract
Background: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients with cirrhosis [...] Read more.
Background: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients with cirrhosis to those without cirrhosis. Methods: This retrospective study compared MR-derived radiomic features between patients with cirrhosis undergoing hepatocellular carcinoma screening and patients without cirrhosis undergoing intraductal papillary mucinous neoplasm surveillance between 2015 and 2018 using the same imaging protocol. Secondary analyses stratified the cirrhosis cohort by liver disease severity using clinical compensation/decompensation and Model for End-Stage Liver Disease (MELD). Results: Of 167 patients, 90 had cirrhosis with 68.9% compensated and median MELD 8. Combined liver and spleen radiomic features generated an AUC 0.94 for detecting cirrhosis, with shape and texture components contributing more than size. Discrimination of cirrhosis remained high after stratification by liver disease severity. Conclusions: MR-based liver and spleen radiomic features had high accuracy in identifying cirrhosis, after stratification by clinical compensation/decompensation and MELD. Shape and texture features performed better than size features. These findings will inform radiomic-based applications for cirrhosis diagnosis and severity. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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13 pages, 3872 KiB  
Article
matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model
by Giovanni Pasini, Fabiano Bini, Giorgio Russo, Albert Comelli, Franco Marinozzi and Alessandro Stefano
J. Imaging 2022, 8(8), 221; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8080221 - 18 Aug 2022
Cited by 22 | Viewed by 3560
Abstract
Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users [...] Read more.
Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users must switch different software to complete the radiomics workflow. To address this issue, we developed a new free and user-friendly radiomics framework, namely matRadiomics, which allows the user: (i) to import and inspect biomedical images, (ii) to identify and segment the target, (iii) to extract the features, (iv) to reduce and select them, and (v) to build a predictive model using machine learning algorithms. As a result, biomedical images can be visualized and segmented and, through the integration of Pyradiomics into matRadiomics, radiomic features can be extracted. These features can be selected using a hybrid descriptive–inferential method, and, consequently, used to train three different classifiers: linear discriminant analysis, k-nearest neighbors, and support vector machines. Model validation is performed using k-fold cross-Validation and k-fold stratified cross-validation. Finally, the performance metrics of each model are shown in the graphical interface of matRadiomics. In this study, we discuss the workflow, architecture, application, future development of matRadiomics, and demonstrate its working principles in a real case study with the aim of establishing a reference standard for the whole radiomics analysis, starting from the image visualization up to the predictive model implementation. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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10 pages, 649 KiB  
Article
Effect of Gray Value Discretization and Image Filtration on Texture Features of the Pancreas Derived from Magnetic Resonance Imaging at 3T
by Bassam M. Abunahel, Beau Pontre and Maxim S. Petrov
J. Imaging 2022, 8(8), 220; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8080220 - 18 Aug 2022
Cited by 1 | Viewed by 1480
Abstract
Radiomics of pancreas magnetic resonance (MR) images is positioned well to play an important role in the management of diseases characterized by diffuse involvement of the pancreas. The effect of image pre-processing configurations on these images has been sparsely investigated. Fifteen individuals with [...] Read more.
Radiomics of pancreas magnetic resonance (MR) images is positioned well to play an important role in the management of diseases characterized by diffuse involvement of the pancreas. The effect of image pre-processing configurations on these images has been sparsely investigated. Fifteen individuals with definite chronic pancreatitis (an exemplar diffuse disease of the pancreas) and 15 healthy individuals were included in this age- and sex-matched case-control study. MR images of the pancreas were acquired using a single 3T scanner. A total of 93 first-order and second-order texture features of the pancreas were compared between the study groups, by subjecting MR images of the pancreas to 7 image pre-processing configurations related to gray level discretization and image filtration. The studied parameters of intensity discretization did not vary in terms of their effect on the number of significant first-order texture features. The number of statistically significant first-order texture features varied after filtering (7 with the use of logarithm filter and 3 with the use of Laplacian of Gaussian filter with 5 mm σ). Intensity discretization generally affected the number of significant second-order texture features more markedly than filtering. The use of fixed bin number of 16 yielded 42 significant second-order texture features, fixed bin number of 128–38 features, fixed bin width of 6–24 features, and fixed bin width of 42–26 features. The specific parameters of filtration and intensity discretization had differing effects on radiomics signature of the pancreas. Relative discretization with fixed bin number of 16 and use of logarithm filter hold promise as pre-processing configurations of choice in future radiomics studies in diffuse diseases of the pancreas. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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14 pages, 2619 KiB  
Article
Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction
by Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard and Su Ruan
J. Imaging 2022, 8(5), 130; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8050130 - 09 May 2022
Cited by 8 | Viewed by 2206
Abstract
It is proven that radiomic characteristics extracted from the tumor region are predictive. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and requires a highly trained physician. This process could be automated using [...] Read more.
It is proven that radiomic characteristics extracted from the tumor region are predictive. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and requires a highly trained physician. This process could be automated using computer-aided detection (CAD) tools. Current state-of-the-art methods are trained in a supervised learning setting, which requires a lot of data that are usually not available in the medical imaging field. The challenge is to train one model to segment different types of tumors with only a weak segmentation ground truth. In this work, we propose a prediction framework including a 3D tumor segmentation in positron emission tomography (PET) images, based on a weakly supervised deep learning method, and an outcome prediction based on a 3D-CNN classifier applied to the segmented tumor regions. The key step is to locate the tumor in 3D. We propose to (1) calculate two maximum intensity projection (MIP) images from 3D PET images in two directions, (2) classify the MIP images into different types of cancers, (3) generate the class activation maps through a multitask learning approach with a weak prior knowledge, and (4) segment the 3D tumor region from the two 2D activation maps with a proposed new loss function for the multitask. The proposed approach achieves state-of-the-art prediction results with a small data set and with a weak segmentation ground truth. Our model was tested and validated for treatment response and survival in lung and esophageal cancers on 195 patients, with an area under the receiver operating characteristic curve (AUC) of 67% and 59%, respectively, and a dice coefficient of 73% and 0.77% for tumor segmentation. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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15 pages, 29475 KiB  
Article
A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mouse Models
by Viviana Benfante, Alessandro Stefano, Albert Comelli, Paolo Giaccone, Francesco Paolo Cammarata, Selene Richiusa, Fabrizio Scopelliti, Marco Pometti, Milene Ficarra, Sebastiano Cosentino, Marcello Lunardon, Francesca Mastrotto, Alberto Andrighetto, Antonino Tuttolomondo, Rosalba Parenti, Massimo Ippolito and Giorgio Russo
J. Imaging 2022, 8(4), 92; https://doi.org/10.3390/jimaging8040092 - 30 Mar 2022
Cited by 17 | Viewed by 3136
Abstract
The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time [...] Read more.
The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after 64Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [64Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [64Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [64Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard template space (3D whole-body Digimouse atlas), and 108 radiomics features were extracted from seven organs (namely, heart, bladder, stomach, liver, spleen, kidney, and lung) to investigate possible changes over time in [64Cu]chelator biodistribution. The one-way analysis of variance and post hoc Tukey Honestly Significant Difference test revealed that, while heart, stomach, spleen, kidney, and lung districts showed a very low percentage of radiomics features with significant variations (p-value < 0.05) among the three groups of mice, a large number of features (greater than 60% and 50%, respectively) that varied significantly between groups were observed in bladder and liver, indicating a different in vivo uptake of the 64Cu-labeled chelator over time. The proposed methodology may improve the method of calculating the [64Cu]chelator biodistribution and open the way towards a decision support system in the field of new radiopharmaceuticals used in preclinical imaging trials. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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12 pages, 1781 KiB  
Article
A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and K-Nearest Neighbors Algorithm
by Mihaela Miron, Simona Moldovanu, Bogdan Ioan Ștefănescu, Mihai Culea, Sorin Marius Pavel and Anisia Luiza Culea-Florescu
J. Imaging 2022, 8(3), 81; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8030081 - 19 Mar 2022
Cited by 3 | Viewed by 2516
Abstract
(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta’s structure suffers dynamic modifications throughout the whole pregnancy and many of these changes, in which placental microcalcifications are by far [...] Read more.
(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta’s structure suffers dynamic modifications throughout the whole pregnancy and many of these changes, in which placental microcalcifications are by far the most prominent, are related to the process of aging and maturation and have no effect on fetal wellbeing. However, when placental microcalcifications are noticed earlier during pregnancy, they could suggest a major placental dysfunction with serious consequences for the fetus and mother. For better detectability of microcalcifications, we propose a new approach based on improving the clarity of details and the analysis of the placental structure using first and second order statistics, and fractal dimension. (2) Methods: The methodology is based on four stages: (i) cropping the region of interest and preprocessing steps; (ii) feature extraction, first order—standard deviation (SD), skewness (SK) and kurtosis (KR)—and second order—contrast (C), homogeneity (H), correlation (CR), energy (E) and entropy (EN)—are computed from a gray level co-occurrence matrix (GLCM) and fractal dimension (FD); (iii) statistical analysis (t-test); (iv) classification with the K-Nearest Neighbors algorithm (K-NN algorithm) and performance comparison with results from the support vector machine algorithm (SVM algorithm). (3) Results: Experimental results obtained from real clinical data show an improvement in the detectability and visibility of placental microcalcifications. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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11 pages, 697 KiB  
Article
Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy
by Valentina D. A. Corino, Marco Bologna, Giuseppina Calareso, Carlo Resteghini, Silvana Sdao, Ester Orlandi, Lisa Licitra, Luca Mainardi and Paolo Bossi
J. Imaging 2022, 8(2), 46; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8020046 - 15 Feb 2022
Cited by 5 | Viewed by 2112
Abstract
Background: Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Methods: Fifty patients were included [...] Read more.
Background: Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Methods: Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop a radiomic signature. One signature was developed for each sequence (T1-, T2-weighted and ADC). A multiagent decision-making algorithm was used to merge multiple signatures into one score. Results: The area under the curve (AUC) for mono-modality signatures was 0.79 (CI: 0.65–0.88), 0.76 (CI: 0.62–0.87) and 0.93 (CI: 0.75–1) using T1-, T2-weighted and ADC images, respectively. The fuse signature improved the AUC when an ADC-based signature was added. Radiological prediction using RECIST criteria reached an accuracy of 0.78. Conclusions: These results suggest the importance of early delta radiomics and of ADC maps to predict the response to IC in sinonasal cancers. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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13 pages, 5192 KiB  
Article
A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset
by Leandro Donisi, Giuseppe Cesarelli, Anna Castaldo, Davide Raffaele De Lucia, Francesca Nessuno, Gaia Spadarella and Carlo Ricciardi
J. Imaging 2021, 7(10), 215; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7100215 - 18 Oct 2021
Cited by 21 | Viewed by 2372
Abstract
Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach [...] Read more.
Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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13 pages, 2103 KiB  
Article
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients
by Michael Baine, Justin Burr, Qian Du, Chi Zhang, Xiaoying Liang, Luke Krajewski, Laura Zima, Gerard Rux, Chi Zhang and Dandan Zheng
J. Imaging 2021, 7(2), 17; https://doi.org/10.3390/jimaging7020017 - 28 Jan 2021
Cited by 11 | Viewed by 2685
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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14 pages, 2048 KiB  
Article
Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies
by Albert Comelli, Claudia Coronnello, Navdeep Dahiya, Viviana Benfante, Stefano Palmucci, Antonio Basile, Carlo Vancheri, Giorgio Russo, Anthony Yezzi and Alessandro Stefano
J. Imaging 2020, 6(11), 125; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6110125 - 19 Nov 2020
Cited by 32 | Viewed by 3547
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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25 pages, 7312 KiB  
Article
An Automated Method for Quality Control in MRI Systems: Methods and Considerations
by Angeliki C. Epistatou, Ioannis A. Tsalafoutas and Konstantinos K. Delibasis
J. Imaging 2020, 6(10), 111; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6100111 - 18 Oct 2020
Cited by 4 | Viewed by 3286
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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22 pages, 991 KiB  
Review
Harmonization Strategies in Multicenter MRI-Based Radiomics
by Elisavet Stamoulou, Constantinos Spanakis, Georgios C. Manikis, Georgia Karanasiou, Grigoris Grigoriadis, Theodoros Foukakis, Manolis Tsiknakis, Dimitrios I. Fotiadis and Kostas Marias
J. Imaging 2022, 8(11), 303; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8110303 - 07 Nov 2022
Cited by 15 | Viewed by 3054
Abstract
Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating [...] Read more.
Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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40 pages, 5188 KiB  
Review
Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey
by Andronicus A. Akinyelu, Fulvio Zaccagna, James T. Grist, Mauro Castelli and Leonardo Rundo
J. Imaging 2022, 8(8), 205; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8080205 - 22 Jul 2022
Cited by 37 | Viewed by 6828
Abstract
Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep [...] Read more.
Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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20 pages, 558 KiB  
Review
Radiomics of Musculoskeletal Sarcomas: A Narrative Review
by Cristiana Fanciullo, Salvatore Gitto, Eleonora Carlicchi, Domenico Albano, Carmelo Messina and Luca Maria Sconfienza
J. Imaging 2022, 8(2), 45; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8020045 - 13 Feb 2022
Cited by 14 | Viewed by 3348
Abstract
Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the [...] Read more.
Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the tumor microenvironment which, combined with other pertinent data such as genomics and proteomics and correlated with outcomes data, can produce accurate, robust, evidence-based, clinical-decision support systems. Our purpose in this narrative review is to offer an overview of radiomics studies dealing with Magnetic Resonance Imaging (MRI)-based radiomics models of bone and soft-tissue sarcomas that could help distinguish different histotypes, low-grade from high-grade sarcomas, predict response to multimodality therapy, and thus better tailor patients’ treatments and finally improve their survivals. Although showing promising results, interobserver segmentation variability, feature reproducibility, and model validation are three main challenges of radiomics that need to be addressed in order to translate radiomics studies to clinical applications. These efforts, together with a better knowledge and application of the “Radiomics Quality Score” and Image Biomarker Standardization Initiative reporting guidelines, could improve the quality of sarcoma radiomics studies and facilitate radiomics towards clinical translation. Full article
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
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14 pages, 978 KiB  
Review
Radiomics and Prostate MRI: Current Role and Future Applications
by Giuseppe Cutaia, Giuseppe La Tona, Albert Comelli, Federica Vernuccio, Francesco Agnello, Cesare Gagliardo, Leonardo Salvaggio, Natale Quartuccio, Letterio Sturiale, Alessandro Stefano, Mauro Calamia, Gaspare Arnone, Massimo Midiri and Giuseppe Salvaggio
J. Imaging 2021, 7(2), 34; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020034 - 11 Feb 2021
Cited by 34 | Viewed by 5869
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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