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Gao M, Cheng J, Qiu A, Zhao D, Wang J, Liu J. Magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics for prognosis prediction in glioma patients. Clin Radiol 2024:S0009-9260(24)00417-3. [PMID: 39218720 DOI: 10.1016/j.crad.2024.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 06/30/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024]
Abstract
AIM The purpose of this study was to identify robust radiological features from intratumoral and peritumoral regions, evaluate MRI protocols, and machine learning methods for overall survival stratification of glioma patients, and explore the relationship between radiological features and the tumour microenvironment. MATERIAL AND METHODS A retrospective analysis was conducted on 163 glioma patients, divided into a training set (n=113) and a testing set (n=50). For each patient, 2135 features were extracted from clinical MRI. Feature selection was performed using the Minimum Redundancy Maximum Relevance method and the Random Forest (RF) algorithm. Prognostic factors were assessed using the Cox proportional hazards model. Four machine learning models (RF, Logistic Regression, Support Vector Machine, and XGBoost) were trained on clinical and radiological features from tumour and peritumoral regions. Model evaluations on the testing set used receiver operating characteristic curves. RESULTS Among the 163 patients, 96 had an overall survival (OS) of less than three years postsurgery, while 67 had an OS of more than three years. Univariate Cox regression in the validation set indicated that age (p=0.003) and tumour grade (p<0.001) were positively associated with the risk of death within three years postsurgery. The final predictive model incorporated 13 radiological and 7 clinical features. The RF model, combining intratumor and peritumor radiomics, achieved the best predictive performance (AUC = 0.91; ACC = 0.86), outperforming single-region models. CONCLUSION Combined intratumoral and peritumoral radiomics can improve survival prediction and have potential as a practical imaging biomarker to guide clinical decision-making.
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Affiliation(s)
- M Gao
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - J Cheng
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China; Institute of Guizhou Aerospace Measuring and Testing Technology, Guiyang, China
| | - A Qiu
- Department of Biomedical Engineering, The Johns Hopkins University, MD, USA; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - D Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
| | - J Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
| | - J Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China; Department of Radiology Quality Control Center, Changsha, China.
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Churchill NW, Roudaia E, Chen JJ, Sekuler A, Gao F, Masellis M, Lam B, Cheng I, Heyn C, Black SE, MacIntosh BJ, Graham SJ, Schweizer TA. Persistent fatigue in post-acute COVID syndrome is associated with altered T1 MRI texture in subcortical structures: a preliminary investigation. Behav Brain Res 2024; 469:115045. [PMID: 38734034 DOI: 10.1016/j.bbr.2024.115045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/13/2024]
Abstract
Post-acute COVID syndrome (PACS) is a global health concern and is often associated with debilitating symptoms. Post-COVID fatigue is a particularly frequent and troubling issue, and its underlying mechanisms remain incompletely understood. One potential contributor is micropathological injury of subcortical and brainstem structures, as has been identified in other patient populations. Texture-based analysis (TA) may be used to measure such changes in anatomical MRI data. The present study develops a methodology of voxel-wise TA mapping in subcortical and brainstem regions, which is then applied to T1-weighted MRI data from a cohort of 48 individuals who had PACS (32 with and 16 without ongoing fatigue symptoms) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19. Both groups were assessed an average of 4-5 months post-infection. There were no significant differences between PACS and control groups, but significant differences were observed within the PACS groups, between those with and without fatigue symptoms. This included reduced texture energy and increased entropy, along with reduced texture correlation, cluster shade and profile in the putamen, pallidum, thalamus and brainstem. These findings provide new insights into the neurophysiological mechanisms that underlie PACS, with altered tissue texture as a potential biomarker of this debilitating condition.
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Affiliation(s)
- Nathan W Churchill
- Brain Health and Wellness Research Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science of St. Michael's Hospital, Unity Health Toronto, Canada; Physics Department, Toronto Metropolitan University, Canada.
| | - Eugenie Roudaia
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada
| | - J Jean Chen
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Allison Sekuler
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada; Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Fuqiang Gao
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Mario Masellis
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Lam
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Ivy Cheng
- Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada; Integrated Community Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Chris Heyn
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Sandra E Black
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Bradley J MacIntosh
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada; Computational Radiology & Artificial Intelligence Unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Simon J Graham
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Tom A Schweizer
- Brain Health and Wellness Research Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science of St. Michael's Hospital, Unity Health Toronto, Canada; Faculty of Medicine (Neurosurgery), University of Toronto, Canada
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Reza SMS, Islam A, Iftekharuddin KM. Texture Estimation for Abnormal Tissue Segmentation in Brain MRI. ADVANCES IN NEUROBIOLOGY 2024; 36:469-486. [PMID: 38468048 DOI: 10.1007/978-3-031-47606-8_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
This chapter discusses multifractal texture estimation and characterization of brain lesions (necrosis, edema, enhanced tumor, nonenhanced tumor, etc.) in magnetic resonance (MR) images. This work formulates the complex texture of tumor in MR images using a stochastic model known as multifractional Brownian motion (mBm). Mathematical derivations of the mBm model and corresponding algorithm to extract the spatially varying multifractal texture feature are discussed. Extracted multifractal texture feature is fused with other effective features to enhance the tissue characteristics. Segmentation of the tissues is performed using a feature-based classification method. The efficacy of the mBm texture feature in segmenting different abnormal tissues is demonstrated using a large-scale publicly available clinical dataset. Experimental results and performance of the methods confirm the efficacy of the proposed technique in an automatic segmentation of abnormal tissues in multimodal (T1, T2, Flair, and T1contrast) brain MRIs.
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Affiliation(s)
- Syed M S Reza
- Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA
| | - Atiq Islam
- Applied Research, Ebay Inc, San Jose, CA, USA
| | - Khan M Iftekharuddin
- Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA.
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Yan B, Jia Y, Li Z, Ding C, Lu J, Liu J, Zhang Y. Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region. Acta Radiol 2023; 64:2636-2645. [PMID: 37312525 DOI: 10.1177/02841851231181681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. PURPOSE To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). MATERIAL AND METHODS A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. RESULTS Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUCtrain = 0.919, and AUCvalidation = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. CONCLUSION The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.
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Affiliation(s)
- Bin Yan
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
- Department of Radiology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Yuxia Jia
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, PR China
| | - Zhihao Li
- GE Healthcare China, Xi'an, Shaanxi, PR China
| | - Caixia Ding
- Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Jianrong Lu
- Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, Xi'an, PR China
| | - Jixin Liu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, PR China
| | - Yuchen Zhang
- Department of Nuclear Medicine, First Affiliated Hospital of Xi'an Jiaotong University, PR China
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Liu X, Zhang J, Zhou J, He Y, Xu Y, Zhang Z, Cao G, Miao H, Chen Z, Zhao Y, Jin X, Wang M. Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification. Front Oncol 2022; 12:992509. [PMID: 36531052 PMCID: PMC9755840 DOI: 10.3389/fonc.2022.992509] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/11/2022] [Indexed: 10/25/2023] Open
Abstract
OBJECTIVE To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions. MATERIAL AND METHODS In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist. RESULTS The All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets. CONCLUSION The proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.
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Affiliation(s)
- Xinmiao Liu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Ji Zhang
- Radiotherapy Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiejie Zhou
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yun He
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yunyu Xu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhenhua Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guoquan Cao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haiwei Miao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Youfan Zhao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiance Jin
- Radiotherapy Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
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Gonçalves M, Gsaxner C, Ferreira A, Li J, Puladi B, Kleesiek J, Egger J, Alves V. Radiomics in Head and Neck Cancer Outcome Predictions. Diagnostics (Basel) 2022; 12:2733. [PMID: 36359576 PMCID: PMC9689406 DOI: 10.3390/diagnostics12112733] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/02/2022] [Accepted: 11/04/2022] [Indexed: 09/16/2023] Open
Abstract
Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients' clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans.
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Affiliation(s)
- Maria Gonçalves
- Center Algoritmi, LASI, University of Minho, 4710-057 Braga, Portugal
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
| | - Christina Gsaxner
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
| | - André Ferreira
- Center Algoritmi, LASI, University of Minho, 4710-057 Braga, Portugal
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Girardetstraße 2, 45131 Essen, Germany
| | - Jianning Li
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Girardetstraße 2, 45131 Essen, Germany
| | - Behrus Puladi
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Girardetstraße 2, 45131 Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Hufelandstraße 55, 45147 Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, 45147 Essen, Germany
| | - Jan Egger
- Computer Algorithms for Medicine Laboratory, 8010 Graz, Austria
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Girardetstraße 2, 45131 Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Hufelandstraße 55, 45147 Essen, Germany
| | - Victor Alves
- Center Algoritmi, LASI, University of Minho, 4710-057 Braga, Portugal
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Balaban MO, Gümüş B, Gümüş E. Comparison of three image-analysis-based visual texture calculation methods: energy, entropy, and texture change index. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:5984-5994. [PMID: 35445408 DOI: 10.1002/jsfa.11951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 07/01/2021] [Accepted: 04/20/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Three image analysis methods to measure visual texture were applied to an image with much texture (scaled carp), and one with little texture (mirror carp). For each method, the effect of image rotation at 0°, 10°, 20°, 30°, 45°, 60°, 75° and 90° was evaluated. RESULTS Rotation did not change energy (E) and entropy (H) calculations using image histograms. Using co-occurrence matrices with different step size d (1-19 in increases of 2) and step angle θ (0°, 45°, 90° and 135°) showed that, as d increased, E decreased and H increased, and the number of legitimate pixel pairs decreased linearly. Averaging E and H at different θ values rendered the results rotation invariant. Theoretically, the 'texture primitives' method is not rotation independent. However, the variation in texture change index (TCI) with image rotation was negligible. Also, the increase in TCI between the less textured image and the more textured image was 5.3-11. In comparison, the E values from histograms for the images above were 0.0069-0.0081. For co-occurrence matrix-based calculations, at d = 1 and for all θ, E range was from 220 to 389 for scaled carp and from 232 to 412 for mirror carp. CONCLUSION The more than doubling of TCI for these images implies that it is a more precise method than energy and entropy to discern between visual texture levels. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Murat O Balaban
- Chemical and Materials Engineering Department, University of Auckland, Auckland, New Zealand
| | - Bahar Gümüş
- Department of Gastronomy and Culinary Arts, Faculty of Tourism, Akdeniz University, Antalya, Turkey
| | - Erkan Gümüş
- Department of Aquaculture, Faculty of Fisheries, Akdeniz University, Antalya, Turkey
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Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17:6. [PMID: 35246207 PMCID: PMC8897888 DOI: 10.1186/s13027-022-00422-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. METHODS This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. RESULTS In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. CONCLUSIONS Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy.
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Igino Simonetti
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesca Grassi
- Division of Radiology, "Università Degli Studi Della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
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Juras V, Szomolanyi P, Janáčová V, Kirner A, Angele P, Trattnig S. Differentiation of Cartilage Repair Techniques Using Texture Analysis from T 2 Maps. Cartilage 2021; 13:718S-728S. [PMID: 34269072 PMCID: PMC8808785 DOI: 10.1177/19476035211029698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/07/2021] [Accepted: 06/07/2021] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE The aim of this study was to investigate texture features from T2 maps as a marker for distinguishing the maturation of repair tissue after 2 different cartilage repair procedures. DESIGN Seventy-nine patients, after either microfracture (MFX) or matrix-associated chondrocyte transplantation (MACT), were examined on a 3-T magnetic resonance (MR) scanner with morphological and quantitative (T2 mapping) MR sequences 2 years after surgery. Twenty-one texture features from a gray-level co-occurrence matrix (GLCM) were extracted. The texture feature difference between 2 repair types was assessed individually for the femoral condyle and trochlea/anterior condyle using linear regression models. The stability and reproducibility of texture features for focal cartilage were calculated using intra-observer variability and area under curve from receiver operating characteristics. RESULTS There was no statistical significance found between MFX and MACT for T2 values (P = 0.96). There was, however, found a statistical significance between MFX and MACT in femoral condyle in GLCM features autocorrelation (P < 0.001), sum of squares (P = 0.023), sum average (P = 0.005), sum variance (P = 0.0048), and sum entropy (P = 0.05); and in anterior condyle/trochlea homogeneity (P = 0.02) and dissimilarity (P < 0.001). CONCLUSION Texture analysis using GLCM provides a useful extension to T2 mapping for the characterization of cartilage repair tissue by increasing its sensitivity to tissue structure. Some texture features were able to distinguish between repair tissue after different cartilage repair procedures, as repair tissue texture (and hence, probably collagen organization) 24 months after MACT more closely resembled healthy cartilage than did MFX repair tissue.
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Affiliation(s)
- Vladimir Juras
- High-Field MR Centre, Department of
Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna,
Austria
| | - Pavol Szomolanyi
- High-Field MR Centre, Department of
Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna,
Austria
- Institute of Measurement Science,
Slovak Academy of Sciences, Bratislava, Slovakia
| | - Veronika Janáčová
- High-Field MR Centre, Department of
Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna,
Austria
| | | | | | - Siegfried Trattnig
- High-Field MR Centre, Department of
Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna,
Austria
- CD laboratory for Clinical Molecular MR
imaging, Vienna, Austria
- Austrian Cluster for Tissue
Regeneration, Vienna, Austria
- Institute for Clinical Molecular MRI in
the Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria
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10
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Wanamaker MW, Vernau KM, Taylor SL, Cissell DD, Abdelhafez YG, Zwingenberger AL. Classification of neoplastic and inflammatory brain disease using MRI texture analysis in 119 dogs. Vet Radiol Ultrasound 2021; 62:445-454. [PMID: 33634942 PMCID: PMC9970026 DOI: 10.1111/vru.12962] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 01/06/2021] [Accepted: 01/10/2021] [Indexed: 01/06/2023] Open
Abstract
Magnetic resonance imaging is the primary method used to diagnose canine glial cell neoplasia and noninfectious inflammatory meningoencephalitis. Subjective differentiation of these diseases can be difficult due to overlapping imaging characteristics. This study utilizes texture analysis (TA) of intra-axial lesions both as a means to quantitatively differentiate these broad categories of disease and to help identify glial tumor grade/cell type and specific meningoencephalitis subtype in a group of 119 dogs with histologically confirmed diagnoses. Fifty-nine dogs with gliomas and 60 dogs with noninfectious inflammatory meningoencephalitis were retrospectively recruited and randomly split into training (n = 80) and test (n = 39) cohorts. Forty-five of 120 texture metrics differed significantly between cohorts after correcting for multiple testing (false discovery rate < 0.05). After training the random forest algorithm, the classification accuracy for the test set was 85% (sensitivity 89%, specificity 81%). TA was only partially able to differentiate the inflammatory subtypes (granulomatous meningoencephalitis [GME], necrotizing meningoencephalitis [NME], and necrotizing leukoencephalitis [NLE]) (out-of-bag error rate of 35.0%) and was unable to identify metrics that could correctly classify glioma grade or cell type (out-of-bag error rate of 59.6% and 47.5%, respectively). Multiple demographic differences, such as patient age, sex, weight, and breed were identified between disease cohorts and subtypes which may be useful in prioritizing differential diagnoses. TA of MR images with a random forest algorithm provided classification accuracy of inflammatory and neoplastic brain disease approaching the accuracy of previously reported subjective radiologist evaluation.
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Affiliation(s)
- Mason W. Wanamaker
- William R. Pritchard Veterinary Medical Teaching Hospital, University of California, Davis 95616, CA
| | - Karen M. Vernau
- Department of Surgical and Radiological Sciences, University of California, Davis 95616, CA
| | | | - Derek D. Cissell
- Department of Surgical and Radiological Sciences, University of California, Davis 95616, CA
| | - Yasser G. Abdelhafez
- Department of Radiology University of California Davis School of Medicine, Sacramento 95817, CA
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11
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Granata V, Fusco R, Barretta ML, Picone C, Avallone A, Belli A, Patrone R, Ferrante M, Cozzi D, Grassi R, Grassi R, Izzo F, Petrillo A. Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer 2021; 16:39. [PMID: 34078424 PMCID: PMC8173908 DOI: 10.1186/s13027-021-00379-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/12/2021] [Indexed: 02/06/2023] Open
Abstract
Background Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. Methods The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. Results We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. Conclusions In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.
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Affiliation(s)
- Vincenza Granata
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Roberta Fusco
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy.
| | - Maria Luisa Barretta
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Carmine Picone
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Antonio Avallone
- Abdominal Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Andrea Belli
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Renato Patrone
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Marilina Ferrante
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Diletta Cozzi
- Division of Radiology, "Azienda Ospedaliera Universitaria Careggi", Florence, Italy
| | - Roberta Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Roberto Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy.,Italian Society of Medical and Interventional Radiology SIRM, SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Antonella Petrillo
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
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12
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Weber CE, Wittayer M, Kraemer M, Dabringhaus A, Platten M, Gass A, Eisele P. Quantitative MRI texture analysis in chronic active multiple sclerosis lesions. Magn Reson Imaging 2021; 79:97-102. [PMID: 33771609 DOI: 10.1016/j.mri.2021.03.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/12/2021] [Accepted: 03/22/2021] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Recently, there has been an increasing interest in "chronic enlarging" or "chronic active" multiple sclerosis (MS) lesions that are associated with clinical disability. However, investigation of dynamic lesion volume changes requires longitudinal MRI data from two or more time points. The aim of this study was to investigate the application of texture analysis (TA) on baseline T1-weighted 3D magnetization-prepared rapid acquisition gradient-echo (MPRAGE) images to differentiate chronic active from chronic stable MS lesions. MATERIAL AND METHODS To identify chronic active lesions as compared to non-enhancing stable lesions, two MPRAGE datasets acquired on a 3 T MRI at baseline and after 12 months follow-up were applied to the Voxel-Guided Morphometry (VGM) algorithm. TA was performed on the baseline MPRAGE images, 36 texture features were extracted for each lesion. RESULTS Overall, 374 chronic MS lesions (155 chronic active and 219 chronic stable lesions) from 60 MS patients were included in the final analysis. Multiple texture features including "DISCRETIZED_HISTO_Energy", "GLCM_Energy", "GLCM_Contrast" and "GLCM_Dissimilarity" were significantly higher in chronic active as compared to chronic stable lesions. Partial least squares regression yielded an area under the curve of 0.7 to differentiate both lesion types. CONCLUSION Our results suggest that multiple texture features extracted from MPRAGE images indicate higher intralesional heterogeneity, however they demonstrate only a fair accuracy to differentiate chronic active from chronic stable MS lesions.
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Affiliation(s)
- Claudia E Weber
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center for Translational Neurosciences (MCTN), University of Heidelberg, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany
| | - Matthias Wittayer
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center for Translational Neurosciences (MCTN), University of Heidelberg, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany
| | - Matthias Kraemer
- Hospital zum Heiligen Geist, Department of Neurology and Neurological Early Rehabilitation, 47906 Kempen, Germany; Brainalyze GbR, Unterste Sauerwiese 9, 51069 Köln, Germany
| | | | - Michael Platten
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center for Translational Neurosciences (MCTN), University of Heidelberg, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany
| | - Achim Gass
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center for Translational Neurosciences (MCTN), University of Heidelberg, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany
| | - Philipp Eisele
- Department of Neurology, Medical Faculty Mannheim and Mannheim Center for Translational Neurosciences (MCTN), University of Heidelberg, Theodor-Kutzer-Ufer 1 - 3, 68167 Mannheim, Germany.
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13
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Baidya Kayal E, Kandasamy D, Khare K, Bakhshi S, Sharma R, Mehndiratta A. Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging. NMR IN BIOMEDICINE 2021; 34:e4426. [PMID: 33078438 DOI: 10.1002/nbm.4426] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 09/21/2020] [Accepted: 09/22/2020] [Indexed: 06/11/2023]
Abstract
The efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response among patients with osteosarcoma was assessed. Forty patients (male: female = 31:9; age = 17.2 ± 5.7 years) with biopsy-proven osteosarcoma were analyzed in this prospective study. Patients were scheduled for three cycles of neoadjuvant chemotherapy (NACT) and diffusion-weighted MRI acquisition at three time points: at baseline (t0), after the first NACT (t1) and after the third NACT (t2) using a 1.5 T scanner. Eight patients (nonsurvivors) died during NACT while 34 patients (survivors) completed the NACT regimen followed by surgery. Histopathological evaluation was performed in the resected tumor to assess NACT response (responder [≤50% viable tumor] and nonresponder [>50% viable tumor]) and revealed nonresponder: responder = 20:12. Apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters, diffusion coefficient (D), perfusion coefficient (D*) and perfusion fraction (f) were evaluated. A total of 25 textural features were evaluated on ADC, D, D* and f parametric maps and structural T1-weighted (T1W) and T2-weighted (T2W) images in the entire tumor volume using 3D TA methods gray-level cooccurrence matrix (GLCM), neighborhood gray-tone-difference matrix (NGTDM) and run-length matrix (RLM). Receiver-operating-characteristic curve analysis was performed on the selected textural feature set to assess the role of TA features (a) as marker(s) of tumor aggressiveness leading to mortality at baseline and (b) in predicting the NACT response among survivors in the course of treatment. Findings showed that the NGTDM features coarseness, busyness and strength quantifying tumor heterogeneity in D, D* and f maps and T1W and T2W images were useful markers of tumor aggressiveness in identifying the nonsurvivor group (area-under-the-curve [AUC] = 0.82-0.88) at baseline. The GLCM features contrast and correlation, NGTDM features contrast and complexity and RLM feature short-run-low-gray-level-emphasis quantifying homogeneity/terogeneity in tumor were effective markers for predicting chemotherapeutic response using D (AUC = 0.80), D* (AUC = 0.80) and T2W (AUC = 0.70) at t0, and D* (AUC = 0.80) and f (AUC = 0.70) at t1. 3D statistical TA features might be useful as imaging-based markers for characterizing tumor aggressiveness and predicting chemotherapeutic response in patients with osteosarcoma.
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Affiliation(s)
- Esha Baidya Kayal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | | | - Kedar Khare
- Department of Physics, Indian Institute of Technology Delhi, New Delhi, India
| | - Sameer Bakhshi
- Department of Medical Oncology, Dr. B.R. Ambedkar Institute-Rotary Cancer Hospital (IRCH), All India Institute of Medical Sciences, New Delhi, India
| | - Raju Sharma
- Department of Radio Diagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
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14
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Houseni M, Mahmoud MA, Saad S, ElHussiny F, Shihab M. Advanced intra-tumoural structural characterisation of hepatocellular carcinoma utilising FDG-PET/CT: a comparative study of radiomics and metabolic features in 3D and 2D. Pol J Radiol 2021; 86:e64-e73. [PMID: 33708274 PMCID: PMC7934742 DOI: 10.5114/pjr.2021.103239] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 08/12/2020] [Indexed: 12/18/2022] Open
Abstract
PURPOSE The aim of our work is to evaluate the correlation of two-dimensional (2D) and three-dimensional (3D) radiomics and metabolic features of hepatocellular carcinoma (HCC) with tumour diameter, staging, and metabolic tumour volume (MTV). MATERIAL AND METHODS Thirty-three patients with HCC were studied using 18F-fluorodeoxyglucose positron-emission tomography with computed tomography (18F [FDG] PET/CT). The tumours were segmented from the PET images after CT correction. Metabolic parameters and 35 radiomics features were compared using 2D and 3D modes. The metabolic parameters and tumour morphology were compared using 2 different types of software. Tumour heterogeneity was studied in both metabolic parameters and radiomics features. Finally, the correlation between the metabolic and radiomics features in 3D mode, as well as tumour morphology and staging according to the American Joint Committee on Cancer (AJCC) staging were studied. RESULTS Most of the metabolic parameters and radiomics features are statically stable through the 2D and 3D modes. Most of the 3D mode features show a correlation with metabolic parameters; the total lesion glycolysis (TLG) shows the highest correlation, with a Spearman correlation coefficient (rs) of 0.9776. Also, the grey level run length matrix/run length non-uniformity (GLRLM_RLNU) from radiomics features exhibits a correlation with a Spearman correlation coefficient of 0.9733. Maximum tumour diameter is correlated with TLG and GLRLM_RLNU, with rs equal to 0.7461 and 0.7143, respectively. Regarding AJCC staging, some features show a medium but prognostic correlation. In the case of 2D-mode features, all metabolic and radiomics features show no significant correlation with MTV, AJCC staging, and tumour maximum diameter. CONCLUSIONS Most of the normal metabolic parameters and radiomics features are statistically stable through the 3D and 2D modes. 3D radiomics features are significantly correlated with tumour volume, maximum diameter, and staging. Conversely, 2D features have negligible correlation with the same parameters. Therefore, 3D mode features are preferable and can accurately evaluate tumour heterogeneity.
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Affiliation(s)
- Mohamed Houseni
- Department of Medical Imaging, National Liver Institute, Menoufia University, Egypt
| | - Menna Allah Mahmoud
- Department of Medical Imaging, National Liver Institute, Menoufia University, Egypt
| | - Salwa Saad
- Department of Physics, Faculty of Science, Tanta University, Tanta, Egypt
| | - Fathi ElHussiny
- Department of Physics, Faculty of Science, Tanta University, Tanta, Egypt
| | - Mohammed Shihab
- Department of Physics, Faculty of Science, Tanta University, Tanta, Egypt
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15
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Aleithan SH, Mahmoud-Ghoneim D. Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks. Sci Rep 2020; 10:20663. [PMID: 33244137 PMCID: PMC7691502 DOI: 10.1038/s41598-020-77705-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 11/05/2020] [Indexed: 12/02/2022] Open
Abstract
The need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS2 and WS2, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS2 and WS2, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class.
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Affiliation(s)
- Shrouq H Aleithan
- Department of Physics, College of Science, King Faisal University, P. O. Box 400, Al-Ahsa, 31982, Kingdom of Saudi Arabia
| | - Doaa Mahmoud-Ghoneim
- Department of Physics, College of Science, King Faisal University, P. O. Box 400, Al-Ahsa, 31982, Kingdom of Saudi Arabia.
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16
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Caruana G, Pessini LM, Cannella R, Salvaggio G, de Barros A, Salerno A, Auger C, Rovira À. Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions. Eur Radiol 2020; 30:6348-6356. [PMID: 32535736 DOI: 10.1007/s00330-020-06995-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 05/05/2020] [Accepted: 05/29/2020] [Indexed: 01/26/2023]
Abstract
OBJECTIVES To evaluate the diagnostic performance of texture analysis (TA) applied on non-contrast-enhanced susceptibility-weighted imaging (SWI) to differentiate acute (enhancing) from chronic (non-enhancing) multiple sclerosis (MS) lesions. METHODS We analyzed 175 lesions from 58 patients with relapsing-remitting MS imaged on a 3.0 T MRI scanner and applied TA on T2-w and SWI images to extract texture features. We evaluated the presence or absence of lesion enhancement on T1-w post-contrast images and performed a computational statistical analysis to assess if there was any significant correlation between the texture features and the presence of lesion activity. ROC curves and leave-one-out cross-validation were used to evaluate the performance of individual features and multiparametric models in the identification of active lesions. RESULTS Multiple TA features obtained from SWI images showed a significantly different distribution in acute and chronic lesions (AUC, 0.617-0.720). Multiparametric predictive models based on logistic ridge regression and partial least squares regression yielded an AUC of 0.778 and 0.808, respectively. Results from T2-w images did not show any significant predictive ability of neither individual features nor multiparametric models. CONCLUSIONS Texture analysis on SWI sequences may be useful to differentiate acute from chronic MS lesions. The good diagnostic performance could help to reduce the need of intravenous contrast agent administration in follow-up MRI studies. KEY POINTS • Texture analysis applied on SWI sequences may be useful to differentiate acute from chronic multiple sclerosis lesions • The good diagnostic performance could help to minimize the need of intravenous contrast agent administration in follow-up MRI studies.
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Affiliation(s)
- Giovanni Caruana
- Section of Radiology - BiND, Policlinico Universitario "Paolo Giaccone", University of Palermo, Via del Vespro 129, 90127, Palermo, Italy. .,Neuroradiology Section, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Pg. Vall d'Hebron 119-129, 08035, Barcelona, Spain.
| | - Lucas M Pessini
- Neuroradiology Section, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Pg. Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Roberto Cannella
- Section of Radiology - BiND, Policlinico Universitario "Paolo Giaccone", University of Palermo, Via del Vespro 129, 90127, Palermo, Italy
| | - Giuseppe Salvaggio
- Section of Radiology - BiND, Policlinico Universitario "Paolo Giaccone", University of Palermo, Via del Vespro 129, 90127, Palermo, Italy
| | - Andréa de Barros
- Neuroradiology Section, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Pg. Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Annalaura Salerno
- Neuroradiology Section, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Pg. Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Cristina Auger
- Neuroradiology Section, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Pg. Vall d'Hebron 119-129, 08035, Barcelona, Spain
| | - Àlex Rovira
- Neuroradiology Section, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Pg. Vall d'Hebron 119-129, 08035, Barcelona, Spain
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17
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Equilibrium CT Texture Analysis for the Evaluation of Hepatic Fibrosis: Preliminary Evaluation against Histopathology and Extracellular Volume Fraction. J Pers Med 2020; 10:jpm10020046. [PMID: 32485820 PMCID: PMC7354541 DOI: 10.3390/jpm10020046] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/19/2020] [Accepted: 05/28/2020] [Indexed: 12/20/2022] Open
Abstract
Background: Evaluate equilibrium contrast-enhanced CT (EQ-CT) texture analysis (EQ-CTTA) against histologically-quantified fibrosis, serum-based enhanced liver fibrosis panel (ELF) and imaging-based extracellular volume fraction (ECV) in chronic hepatitis. Methods: This study was a re-analysis of image data from a previous prospective study. Pre- and equilibrium-phase post-IV contrast CT datasets were collected from patients with chronic hepatitis with contemporaneous liver biopsy and serum ELF measurement between April 2011 and July 2013. Biopsy samples were analysed to derive collagen proportionate area (CPA). EQ-CTTA was performed with a filtration histogram technique using texture analysis software, with texture quantification using statistical and histogram-based metrics (mean, skewness, standard deviation, entropy, etc.). Association between pre-contrast and EQ-CTTA against CPA, ECV and ELF was evaluated using Spearman’s rank correlation coefficient (rs). Results: Complete datasets collected in 29 patients (16 male; 13 female), mean age (range): 49 (22–66 years). Liver ECV, CPA and ELF had a median (interquartile range) of 0.26 (0.24–0.29); 5.0 (3.0–13.7) and 9.71 (8.39–10.92). Difference in segment VII hepatic CTTA (medium texture scale) between EQ-CT and pre-contrast images was significantly and positively associated with ELF score (mean: rs = 0.69, p < 0.001; skewness: rs = 0.57, p = 0.007). Significant negative associations were observed between pre-contrast and EQ-CT whole hepatic CTTA (coarse texture scale) with CPA (pre-contrast, SD: rs = −0.66, p < 0.001) and ECV (EQ-CT, entropy: rs = −0.58, p = 0.006). Conclusions: Hepatic EQ-CTTA demonstrates significant association with validated markers of liver fibrosis, suggesting a role in non-invasive quantification of severity in diffuse fibrosis.
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Yoon Y, Hwang T, Choi H, Lee H. Classification of radiographic lung pattern based on texture analysis and machine learning. J Vet Sci 2019; 20:e44. [PMID: 31364328 PMCID: PMC6669202 DOI: 10.4142/jvs.2019.20.e44] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 05/08/2019] [Accepted: 07/05/2019] [Indexed: 11/20/2022] Open
Abstract
This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.
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Affiliation(s)
- Youngmin Yoon
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Korea
| | - Taesung Hwang
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Korea
| | - Hojung Choi
- College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Korea
| | - Heechun Lee
- Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Korea.
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Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis. Clin Neurol Neurosurg 2018; 173:84-90. [DOI: 10.1016/j.clineuro.2018.08.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/24/2018] [Accepted: 08/01/2018] [Indexed: 01/08/2023]
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Abstract
BACKGROUND Evidence of cerebral degeneration is not apparent on routine brain MRI in amyotrophic lateral sclerosis (ALS). Texture analysis can detect change in images based on the statistical properties of voxel intensities. Our objective was to test the utility of texture analysis in detecting cerebral degeneration in ALS. A secondary objective was to determine whether the performance of texture analysis is dependent on image resolution. METHODS High-resolution (0.5×0.5 mm2 in-plane) coronal T2-weighted MRI of the brain were acquired from 12 patients with ALS and 19 healthy controls on a 4.7 Tesla MRI system. Image data sets at lower resolutions were created by down-sampling to 1×1, 2×2, 3×3, and 4×4 mm2. Texture features were extracted from a slice encompassing the corticospinal tract at the different resolutions and tested for their discriminatory power and correlations with clinical measures. Subjects were also classified by visual assessment by expert reviewers. RESULTS Texture features were different between ALS patients and healthy controls at 1×1, 2×2, and 3×3 mm2 resolutions. Texture features correlated with measures of upper motor neuron function and disability. Optimal classification performance was achieved when best-performing texture features were combined with visual assessment at 2×2 mm2 resolution (0.851 area under the curve, 83% sensitivity, 79% specificity). CONCLUSIONS Texture analysis can detect subtle abnormalities in MRI of ALS patients. The clinical yield of the method is dependent on image resolution. Texture analysis holds promise as a potential source of neuroimaging biomarkers in ALS.
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Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. Neuroradiology 2018; 60:1297-1305. [DOI: 10.1007/s00234-018-2091-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 08/23/2018] [Indexed: 10/28/2022]
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Ortiz-Ramon R, Larroza A, Arana E, Moratal D. A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:493-496. [PMID: 29059917 DOI: 10.1109/embc.2017.8036869] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictive models were evaluated using a nested cross-validation scheme. The best classification results were achieved using 3D texture features for all the models, obtaining an average AUC > 0.9 in all cases and an AUC = 0.947 ± 0.067 when using the best model (naïve Bayes).
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Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics. J Neurooncol 2018; 139:633-642. [PMID: 29860714 DOI: 10.1007/s11060-018-2908-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 05/19/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas-IDH, 1p/19q, and MGMT status-show distinct quantitative MRI characteristics on FLAIR imaging. METHODS Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. RESULTS Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. CONCLUSION Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.
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Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 2018; 28:4514-4523. [PMID: 29761357 DOI: 10.1007/s00330-018-5463-6] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/07/2018] [Accepted: 04/05/2018] [Indexed: 01/17/2023]
Abstract
OBJECTIVE To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. METHODS Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. RESULTS In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). CONCLUSION Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. KEY POINTS • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.
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Affiliation(s)
- Rafael Ortiz-Ramón
- Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022, Valencia, Spain
| | - Andrés Larroza
- Department of Medicine, Universitat de València, Av. Blasco Ibáñez 15, 46010, Valencia, Spain
| | - Silvia Ruiz-España
- Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022, Valencia, Spain
| | - Estanislao Arana
- Department of Radiology, Fundación Instituto Valenciano de Oncología, Calle Beltrán Báguena 8, 46009, Valencia, Spain
| | - David Moratal
- Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022, Valencia, Spain.
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Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research. J Digit Imaging 2018; 29:716-729. [PMID: 27440183 DOI: 10.1007/s10278-016-9894-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.
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Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol 2018; 28:3050-3058. [PMID: 29404772 DOI: 10.1007/s00330-017-5270-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/10/2017] [Accepted: 12/20/2017] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To determine if texture analysis of non-contrast-enhanced CT (NECT) images is able to predict nonalcoholic steatohepatitis (NASH). METHODS NECT images from 88 patients who underwent a liver biopsy for the diagnosis of suspected NASH were assessed and texture feature parameters were obtained without and with filtration. The patient population was divided into a predictive learning dataset and a validation dataset, and further divided into groups according to the prediction of liver fibrosis as assessed by hyaluronic acid levels. The reference standard was the histological result of a liver biopsy. A predictive model for NASH was developed using parameters derived from the learning dataset that demonstrated areas under the receiver operating characteristic curve (AUC) of >0.65. The resulting model was then applied to the validation dataset. RESULTS In patients without suspected fibrosis, the texture parameter mean without filter and skewness with a 2-mm filter were selected for the NASH prediction model. The AUC of the predictive model for the validation dataset was 0.94 and the accuracy was 94%. In patients with suspicion of fibrosis, the mean without filtration and kurtosis with a 4-mm filter were selected for the NASH prediction model. The AUC for the validation dataset was 0.60 and the accuracy was 42%. CONCLUSIONS In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. KEY POINTS • In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. • The mean without filtration and skewness with a 2-mm filter were modest predictors of NASH in patients without suspicion of liver fibrosis. • Hepatic fibrosis masks the characteristic texture features of NASH.
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Chen Z, Chen X, Liu M, Liu S, Yu S, Ma L. Magnetic Resonance Image Texture Analysis of the Periaqueductal Gray Matter in Episodic Migraine Patients without T2-Visible Lesions. Korean J Radiol 2018; 19:85-92. [PMID: 29354004 PMCID: PMC5768512 DOI: 10.3348/kjr.2018.19.1.85] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 07/16/2017] [Indexed: 01/03/2023] Open
Abstract
Objective The periaqueductal gray matter (PAG), a small midbrain structure, presents dysfunction in migraine. However, the precise neurological mechanism is still not well understood. Herein, the aim of this study was to investigate the texture characteristics of altered PAG in episodic migraine (EM) patients based on high resolution brain structural magnetic resonance (MR) images. Materials and Methods The brain structural MR images were obtained from 18 normal controls (NC), 18 EM patients and 16 chronic migraine (CM) patients using a 3T MR system. A PAG template was created using the International Consortium Brain Mapping 152 gray matter model, and the individual PAG segment was developed by applying the deformation field from the structural image segment to the PAG template. A grey level co-occurrence matrix was used to calculate the texture parameters including the angular second moment (ASM), contrast, correlation, inverse difference moment (IDM) and entropy. Results There was a significant difference for ASM, IDM and entropy in the EM group (998.629 ± 0.162 × 10−3, 999.311 ± 0.073 × 10−3, 916.354 ± 0.947 × 10−5) compared to that found in the NC group (998.760 ± 0.110 × 10−3, 999.358 ± 0.037 × 10−3 and 841.198 ± 0.575 × 10−5) (p < 0.05). The entropy was significantly lower among the patients with CM (864.116 ± 0.571 × 10−5) than that found among patients with EM (p < 0.05). The area under the receiver operating characteristic curve was 0.776 and 0.750 for ASM and entropy in the distinction of the EM from NC groups, respectively. ASM was negatively related to disease duration (DD) and the Migraine Disability Assessment Scale (MIDAS) scores in the EM group, and entropy was positively related to DD and MIDAS in the EM group (p < 0.05). Conclusion The present study identified altered MR image texture characteristics of the PAG in EM. The identified texture characteristics could be considered as imaging biomarkers for EM.
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Affiliation(s)
- Zhiye Chen
- Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China.,Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China.,Department of Radiology, Hainan Branch of Chinese PLA General Hospital, Sanya 572013, China
| | - Xiaoyan Chen
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
| | - Mengqi Liu
- Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China.,Department of Radiology, Hainan Branch of Chinese PLA General Hospital, Sanya 572013, China
| | - Shuangfeng Liu
- Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
| | - Shengyuan Yu
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
| | - Lin Ma
- Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
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Banzato T, Bernardini M, Cherubini GB, Zotti A. Texture analysis of magnetic resonance images to predict histologic grade of meningiomas in dogs. Am J Vet Res 2017; 78:1156-1162. [DOI: 10.2460/ajvr.78.10.1156] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Molina D, Pérez-Beteta J, Martínez-González A, Martino J, Velasquez C, Arana E, Pérez-García VM. Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization. PLoS One 2017; 12:e0178843. [PMID: 28586353 PMCID: PMC5460822 DOI: 10.1371/journal.pone.0178843] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 05/19/2017] [Indexed: 01/11/2023] Open
Abstract
Purpose Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols. Materials and methods Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size. Results No textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases). Conclusion Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images.
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Affiliation(s)
- David Molina
- Mathematical Oncology Laboratory (MÔLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
- * E-mail:
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MÔLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | | | - Juan Martino
- Neurosurgery Department, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | - Carlos Velasquez
- Neurosurgery Department, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | - Estanislao Arana
- Radiology Department, Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - Víctor M. Pérez-García
- Mathematical Oncology Laboratory (MÔLAB), Universidad de Castilla-La Mancha, Ciudad Real, Spain
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Chen Z, Chen X, Liu M, Liu S, Ma L, Yu S. Texture features of periaqueductal gray in the patients with medication-overuse headache. J Headache Pain 2017; 18:14. [PMID: 28155029 PMCID: PMC5289934 DOI: 10.1186/s10194-017-0727-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 01/20/2017] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Periaqueductal gray (PAG) is the descending pain modulatory center, and PAG dysfunction had been recognized in migraine. Here we propose to investigate altered PAG texture features (quantitative approach for extracting texture descriptors for images) in the patients with medication-overuse headache (MOH) based on high resolution brain structural image to understand the MOH pathogenesis. METHODS The brain structural images were obtained from 32 normal controls (NC) and 44 MOH patients on 3.0 T MR system. PAG template was created based on the ICBM152 gray matter template, and the individual PAG segment was performed by applying the deformation field to the PAG template after structural image segment. Grey-level co-occurrence matrix (GLCM) was performed to measure the texture parameters including angular second moment (ASM), Contrast, Correlation, inverse difference moment (IDM) and Entropy. RESULTS Contrast was increased in MOH patients (9.28 ± 3.11) compared with that in NC (7.94 ± 0.65) (P < 0.05), and other texture features showed no significant difference between MOH and NC (P > 0.05). The area under the ROC curve was 0.697 for Contrast in the distinction of MOH from NC, and the cut-off value of Contrast was 8.11 with sensitivity 70.5% and specificity 62.5%. The contrast was negatively with the sleep scores (r = -0.434, P = 0.003). CONCLUSION Texture Contrast could be used to identify the altered MR imaging characteristics in MOH in understanding the MOH pathogenesis, and it could also be considered as imaging biomarker in for MOH diagnosis.
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Affiliation(s)
- Zhiye Chen
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China.,Department of Neurology, Chinese PLA General Hospital, Beijing, 100853, China.,Department of Radiology, Hainan Branch of Chinese PLA General Hospital, Beijing, 100853, China
| | - Xiaoyan Chen
- Department of Neurology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Mengqi Liu
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China.,Department of Radiology, Hainan Branch of Chinese PLA General Hospital, Beijing, 100853, China
| | - Shuangfeng Liu
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Lin Ma
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Shengyuan Yu
- Department of Neurology, Chinese PLA General Hospital, Beijing, 100853, China.
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Nketiah G, Elschot M, Kim E, Teruel JR, Scheenen TW, Bathen TF, Selnæs KM. T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol 2016; 27:3050-3059. [PMID: 27975146 DOI: 10.1007/s00330-016-4663-1] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Revised: 11/01/2016] [Accepted: 11/16/2016] [Indexed: 12/19/2022]
Abstract
PURPOSE To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers. MATERIALS AND METHODS 3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (Ktrans and Ve) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically. RESULTS ASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with Ktrans and Ve. GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, Ktrans, and Ve. The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets. CONCLUSION T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers. KEY POINTS • T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.
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Affiliation(s)
- Gabriel Nketiah
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Eugene Kim
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jose R Teruel
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tom W Scheenen
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Kirsten M Selnæs
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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Ferreira de Lucena DJ, Ferreira Junior JR, Machado AP, Oliveira MC. Automatic weighing attribute to retrieve similar lung cancer nodules. BMC Med Inform Decis Mak 2016; 16 Suppl 2:79. [PMID: 27460071 PMCID: PMC4965736 DOI: 10.1186/s12911-016-0313-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Cancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task for specialists and it presents some big challenges as medical image interpretation process, pulmonary nodule detection and classification. In order to aid specialists in the early diagnosis of lung cancer, computer assistance must be integrated in the imaging interpretation and pulmonary nodule classification processes. Methods of Content-Based Image Retrieval (CBIR) have been described as one promising technique to computer-aided diagnosis and is expected to aid radiologists on image interpretation with a second opinion. However, CBIR presents some limitations: image feature extraction process and appropriate similarity measure. The efficiency of CBIR systems depends on calculating image features that may be relevant to the case similarity analysis. When specialists classify a nodule, they are supported by information from exams, images, etc. But each information has more or less weight over decision making about nodule malignancy. Thus, finding a way to measure the weight allows improvement of image retrieval process through the assignment of higher weights to that attributes that best characterize the nodules. METHODS In this context, the aim of this work is to present a method to automatically calculate attribute weights based on local learning to reflect the interpretation on image retrieval process. The process consists of two stages that are performed sequentially and cyclically: Evaluation Stage and Training Stage. At each iteration the weights are adjusted according to retrieved nodules. After some iterations, it is possible reach a set of attribute weights that optimize the recovery of similar nodes. RESULTS The results achieved by updated weights were promising because was possible increase precision by 10% to 6% on average to retrieve of benign and malignant nodules, respectively, with recall of 25% compared with tests without weights associated to attributes in similarity metric. The best result, we reaching values over 100% of precision average until thirtieth lung cancer nodule retrieved. CONCLUSIONS Based on the results, WED applied to the three vectors used attributes (3D TA, 3D MSA and InV), with weights adjusted by the process, always achieved better results than those found with ED. With the weights, the Precision was increased on average by 17.3% compared with using ED.
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Affiliation(s)
- David Jones Ferreira de Lucena
- Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil.
| | - José Raniery Ferreira Junior
- Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil
| | - Aydano Pamponet Machado
- Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil
| | - Marcelo Costa Oliveira
- Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil
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Abstract
Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.
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Affiliation(s)
- Stephen S F Yip
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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Parekh V, Jacobs MA. Radiomics: a new application from established techniques. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016; 1:207-226. [PMID: 28042608 PMCID: PMC5193485 DOI: 10.1080/23808993.2016.1164013] [Citation(s) in RCA: 217] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.
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Affiliation(s)
- Vishwa Parekh
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Department of Computer Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
| | - Michael A. Jacobs
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
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Santos TA, Maistro CEB, Silva CB, Oliveira MS, França MC, Castellano G. MRI Texture Analysis Reveals Bulbar Abnormalities in Friedreich Ataxia. AJNR Am J Neuroradiol 2015; 36:2214-8. [PMID: 26359147 PMCID: PMC7964265 DOI: 10.3174/ajnr.a4455] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Accepted: 05/18/2015] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND PURPOSE Texture analysis is an image processing technique that can be used to extract parameters able to describe meaningful features of an image or ROI. Texture analysis based on the gray level co-occurrence matrix gives a second-order statistical description of the image or ROI. In this work, the co-occurrence matrix texture approach was used to extract information from brain MR images of patients with Friedreich ataxia and a control group, to see whether texture parameters were different between these groups. A longitudinal analysis was also performed. MATERIALS AND METHODS Twenty patients and 21 healthy controls participated in the study. Both groups had 2 sets of T1-weighted MR images obtained 1 year apart for every subject. ROIs chosen for analysis were the medulla oblongata and pons. Texture parameters were obtained for these ROIs for every subject, for the 2 sets of images. These parameters were compared longitudinally within groups and transversally between groups. RESULTS The comparison between patients and the control group showed a significant differences for the medulla oblongata (t test, P < .05, Bonferroni-corrected) but did not show a statistically significant difference for the pons. Longitudinal comparison of images obtained 1 year apart did not show differences for either patients or for controls, in any of the analyzed structures. CONCLUSIONS Gray level co-occurrence matrix-based texture analysis showed statistically significant differences for the medulla oblongata of patients with Friedreich ataxia compared with controls. These results highlight the medulla as an important site of damage in Friedreich ataxia.
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Affiliation(s)
- T A Santos
- From the Neurophysics Group (T.A.S., C.E.B.M., M.S.O., G.C.), Gleb Wataghin Physics Institute Brazilian Institute of Neuroscience and Neurotechnology (BRAINN) (São Paulo Research Foundation) (T.A.S., C.E.B.M., C.B.S., M.S.O., M.C.F., G.C.), Campinas, São Paulo, Brazil
| | - C E B Maistro
- From the Neurophysics Group (T.A.S., C.E.B.M., M.S.O., G.C.), Gleb Wataghin Physics Institute Brazilian Institute of Neuroscience and Neurotechnology (BRAINN) (São Paulo Research Foundation) (T.A.S., C.E.B.M., C.B.S., M.S.O., M.C.F., G.C.), Campinas, São Paulo, Brazil
| | - C B Silva
- Department of Neurology (C.B.S., M.C.F.), Medical Sciences School, University of Campinas, Brazil Brazilian Institute of Neuroscience and Neurotechnology (BRAINN) (São Paulo Research Foundation) (T.A.S., C.E.B.M., C.B.S., M.S.O., M.C.F., G.C.), Campinas, São Paulo, Brazil
| | - M S Oliveira
- From the Neurophysics Group (T.A.S., C.E.B.M., M.S.O., G.C.), Gleb Wataghin Physics Institute Brazilian Institute of Neuroscience and Neurotechnology (BRAINN) (São Paulo Research Foundation) (T.A.S., C.E.B.M., C.B.S., M.S.O., M.C.F., G.C.), Campinas, São Paulo, Brazil
| | - M C França
- Department of Neurology (C.B.S., M.C.F.), Medical Sciences School, University of Campinas, Brazil Brazilian Institute of Neuroscience and Neurotechnology (BRAINN) (São Paulo Research Foundation) (T.A.S., C.E.B.M., C.B.S., M.S.O., M.C.F., G.C.), Campinas, São Paulo, Brazil
| | - G Castellano
- From the Neurophysics Group (T.A.S., C.E.B.M., M.S.O., G.C.), Gleb Wataghin Physics Institute Brazilian Institute of Neuroscience and Neurotechnology (BRAINN) (São Paulo Research Foundation) (T.A.S., C.E.B.M., C.B.S., M.S.O., M.C.F., G.C.), Campinas, São Paulo, Brazil.
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Wolski M, Podsiadlo P, Stachowiak GW. Directional fractal signature methods for trabecular bone texture in hand radiographs: data from the Osteoarthritis Initiative. Med Phys 2015; 41:081914. [PMID: 25086545 DOI: 10.1118/1.4890101] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
PURPOSE To develop directional fractal signature methods for the analysis of trabecular bone (TB) texture in hand radiographs. Problems associated with the small size of hand bones and the orientation of fingers were addressed. METHODS An augmented variance orientation transform (AVOT) and a quadrant rotating grid (QRG) methods were developed. The methods calculate fractal signatures (FSs) in different directions. Unlike other methods they have the search region adjusted according to the size of bone region of interest (ROI) to be analyzed and they produce FSs defined with respect to any chosen reference direction, i.e., they work for arbitrary orientation of fingers. Five parameters at scales ranging from 2 to 14 pixels (depending on image size and method) were derived from rose plots of Hurst coefficients, i.e., FS in dominating roughness (FSSta), vertical (FSV) and horizontal (FSH) directions, aspect ratio (StrS), and direction signatures (StdS), respectively. The accuracy in measuring surface roughness and isotropy/anisotropy was evaluated using 3600 isotropic and 800 anisotropic fractal surface images of sizes between 20 × 20 and 64 × 64 pixels. The isotropic surfaces had FDs ranging from 2.1 to 2.9 in steps of 0.1, and the anisotropic surfaces had two dominating directions of 30° and 120°. The methods were used to find differences in hand TB textures between 20 matched pairs of subjects with (cases: approximate Kellgren-Lawrence (KL) grade ≥ 2) and without (controls: approximate KL grade <2) radiographic hand osteoarthritis (OA). The OA Initiative public database was used and 20 × 20 pixel bone ROIs were selected on 5th distal and middle phalanges. The performance of the AVOT and QRG methods was compared against a variance orientation transform (VOT) method developed earlier [M. Wolski, P. Podsiadlo, and G. W. Stachowiak, "Directional fractal signature analysis of trabecular bone: evaluation of different methods to detect early osteoarthritis in knee radiographs," Proc. Inst. Mech. Eng., Part H 223, 211-236 (2009)]. RESULTS The AVOT method correctly quantified the isotropic and anisotropic surfaces for all image sizes and scales. Values of FSSta were significantly different (P < 0.05) between the isotropic surfaces. Using the VOT and QRG methods no differences were found at large scales for the isotropic surfaces that are smaller than 64 × 64 and 48 × 48 pixels, respectively, and at some scales for the anisotropic surfaces with size 48 × 48 pixels. Compared to controls, using the AVOT and QRG methods the authors found that OA TB textures were less rough (P < 0.05) in the dominating and horizontal directions (i.e., lower FSSta and FSH), rougher in the vertical direction (i.e., higher FSV) and less anisotropic (i.e., higher StrS) than controls. No differences were found using the VOT method. CONCLUSIONS The AVOT method is well suited for the analysis of bone texture in hand radiographs and it could be potentially useful for early detection and prediction of hand OA.
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Affiliation(s)
- M Wolski
- Tribology Laboratory, School of Civil and Mechanical Engineering, Curtin University, Bentley, Western Australia 6102, Australia
| | - P Podsiadlo
- Tribology Laboratory, School of Civil and Mechanical Engineering, Curtin University, Bentley, Western Australia 6102, Australia
| | - G W Stachowiak
- Tribology Laboratory, School of Civil and Mechanical Engineering, Curtin University, Bentley, Western Australia 6102, Australia
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Fetit AE, Novak J, Peet AC, Arvanitits TN. Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours. NMR IN BIOMEDICINE 2015; 28:1174-1184. [PMID: 26256809 DOI: 10.1002/nbm.3353] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Revised: 06/03/2015] [Accepted: 06/04/2015] [Indexed: 06/04/2023]
Abstract
The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used.
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Affiliation(s)
- Ahmed E Fetit
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Jan Novak
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- School of Cancer Sciences, University of Birmingham, Birmingham, UK
| | - Andrew C Peet
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- School of Cancer Sciences, University of Birmingham, Birmingham, UK
| | - Theodoros N Arvanitits
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
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Hodgdon T, McInnes MDF, Schieda N, Flood TA, Lamb L, Thornhill RE. Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology 2015; 276:787-96. [PMID: 25906183 DOI: 10.1148/radiol.2015142215] [Citation(s) in RCA: 211] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
PURPOSE To determine the accuracy of texture analysis to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC) on unenhanced computed tomography (CT) images. MATERIALS AND METHODS In this institutional review board-approved retrospective case-control study, patients with AML and RCC were identified from the pathology database: there were 16 patients with fp-AML (no visible fat at unenhanced CT) and 84 patients with RCC. Axial unenhanced CT images were contoured manually by two independent analysts. Texture analysis was performed for each lesion, and reproducibility was assessed. Texture features related to the gray-level histogram, gray-level co-occurrence, and run-length matrix statistics were evaluated. The most discriminative features were used to generate support vector machine (SVM) classifiers. Diagnostic accuracy of textural features was assessed and 10-fold cross validation was performed. Unenhanced CT images for each patient were independently reviewed by two blinded radiologists who subjectively graded lesion heterogeneity on a five-point scale. Differences in area under the receiver operating characteristic curve (AUC) between subjective heterogeneity ratings and textural features were evaluated by using the DeLong method. RESULTS There was lower lesion homogeneity and higher lesion entropy in RCCs (P ≤ .01). A model incorporating several texture features resulted in an AUC of 0.89 ± 0.04. The average SVM accuracy of textural features ranged from 83% to 91% (after 10-fold cross validation). An optimal subjective heterogeneity rating of 2 or higher was identified as a predictor of RCC for both readers, with no significant difference in AUC between readers (P = .06). Each of the three textural-based classifiers was more accurate than either radiologists' subjective heterogeneity ratings for the models incorporating a subset of the top three textural features (difference in AUC between textural features and subjective visual heterogeneity, 0.25; 95% confidence interval: 0.02, 0.47; P = .03). CONCLUSION CT texture analysis can be used to accurately differentiate fp-AML from RCC on unenhanced CT images.
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Affiliation(s)
- Taryn Hodgdon
- From the Departments of Radiology (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), University of Ottawa, 451 Smyth Rd, Ottawa, ON, Canada K1H 8M5; Departments of Medical Imaging (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), The Ottawa Hospital, Ottawa, Ontario, Canada; and Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada (M.D.F.M., N.S., R.E.T.)
| | - Matthew D F McInnes
- From the Departments of Radiology (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), University of Ottawa, 451 Smyth Rd, Ottawa, ON, Canada K1H 8M5; Departments of Medical Imaging (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), The Ottawa Hospital, Ottawa, Ontario, Canada; and Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada (M.D.F.M., N.S., R.E.T.)
| | - Nicola Schieda
- From the Departments of Radiology (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), University of Ottawa, 451 Smyth Rd, Ottawa, ON, Canada K1H 8M5; Departments of Medical Imaging (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), The Ottawa Hospital, Ottawa, Ontario, Canada; and Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada (M.D.F.M., N.S., R.E.T.)
| | - Trevor A Flood
- From the Departments of Radiology (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), University of Ottawa, 451 Smyth Rd, Ottawa, ON, Canada K1H 8M5; Departments of Medical Imaging (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), The Ottawa Hospital, Ottawa, Ontario, Canada; and Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada (M.D.F.M., N.S., R.E.T.)
| | - Leslie Lamb
- From the Departments of Radiology (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), University of Ottawa, 451 Smyth Rd, Ottawa, ON, Canada K1H 8M5; Departments of Medical Imaging (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), The Ottawa Hospital, Ottawa, Ontario, Canada; and Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada (M.D.F.M., N.S., R.E.T.)
| | - Rebecca E Thornhill
- From the Departments of Radiology (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), University of Ottawa, 451 Smyth Rd, Ottawa, ON, Canada K1H 8M5; Departments of Medical Imaging (T.H., M.D.F.M., N.S., L.L., R.E.T.) and Anatomical Pathology (T.A.F.), The Ottawa Hospital, Ottawa, Ontario, Canada; and Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada (M.D.F.M., N.S., R.E.T.)
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Nachimuthu DS, Baladhandapani A. Multidimensional texture characterization: on analysis for brain tumor tissues using MRS and MRI. J Digit Imaging 2015; 27:496-506. [PMID: 24496552 DOI: 10.1007/s10278-013-9669-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
This paper investigates the efficacy of automated pattern recognition methods on magnetic resonance data with the objective of assisting radiologists in the clinical diagnosis of brain tissue tumors. In this paper, the sciences of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are combined to improve the accuracy of the classifier, based on the multidimensional co-occurrence matrices to assess the detection of pathological tissues (tumor and edema), normal tissues (white matter - WM and gray matter - GM), and fluid (cerebrospinal fluid - CSF). The results show the ability of the classifier with iterative training to automatically and simultaneously recover tissue-specific spectral and structural patterns and achieve segmentation of tumor and edema and grading of high and low glioma tumor. Here, extreme learning machine - improved particle swarm optimization (ELM-IPSO) neural network classifier is trained with the feature descriptions in brain magnetic resonance (MR) spectra. This has the characteristics of varying the normal spectral pattern associated with tumor patterns along with imaging features. Validation was performed considering 35 clinical studies. The volumetric features extracted from the vectors of this matrix articulate some important elementary structures, which along with spectroscopic metabolite ratios discriminate the tumor grades and tissue classes. The quantitative 3D analysis reveals significant improvement in terms of global accuracy rate for automatic classification in brain tissues and discriminating pathological tumor tissue from structural healthy brain tissue.
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Vignati A, Mazzetti S, Giannini V, Russo F, Bollito E, Porpiglia F, Stasi M, Regge D. Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys Med Biol 2015; 60:2685-701. [PMID: 25768265 DOI: 10.1088/0031-9155/60/7/2685] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
To explore contrast (C) and homogeneity (H) gray-level co-occurrence matrix texture features on T2-weighted (T2w) Magnetic Resonance (MR) images and apparent diffusion coefficient (ADC) maps for predicting prostate cancer (PCa) aggressiveness, and to compare them with traditional ADC metrics for differentiating low- from intermediate/high-grade PCas. The local Ethics Committee approved this prospective study of 93 patients (median age, 65 years), who underwent 1.5 T multiparametric endorectal MR imaging before prostatectomy. Clinically significant (volume ≥0.5 ml) peripheral tumours were outlined on histological sections, contoured on T2w and ADC images, and their pathological Gleason Score (pGS) was recorded. C, H, and traditional ADC metrics (mean, median, 10th and 25th percentile) were calculated on the largest lesion slice, and correlated with the pGS through the Spearman correlation coefficient. The area under the receiver operating characteristic curve (AUC) assessed how parameters differentiate pGS = 6 from pGS ≥ 7. The dataset included 49 clinically significant PCas with a balanced distribution of pGS. The Spearman ρ and AUC values on ADC were: -0.489, 0.823 (mean); -0.522, 0.821 (median); -0.569, 0.854 (10th percentile); -0.556, 0.854 (25th percentile); -0.386, 0.871 (C); 0.533, 0.923 (H); while on T2w they were: -0.654, 0.945 (C); 0.645, 0.962 (H). AUC of H on ADC and T2w, and C on T2w were significantly higher than that of the mean ADC (p = 0.05). H and C calculated on T2w images outperform ADC parameters in correlating with pGS and differentiating low- from intermediate/high-risk PCas, supporting the role of T2w MR imaging in assessing PCa biological aggressiveness.
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Affiliation(s)
- A Vignati
- Department of Radiology of Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060 Candiolo, Italy
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Ávila MM, Caballero D, Durán ML, Caro A, Pérez-Palacios T, Antequera T. Including 3D-textures in a Computer Vision System to Analyze Quality Traits of Loin. LECTURE NOTES IN COMPUTER SCIENCE 2015. [DOI: 10.1007/978-3-319-20904-3_41] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Dang M, Lysack JT, Wu T, Matthews TW, Chandarana SP, Brockton NT, Bose P, Bansal G, Cheng H, Mitchell JR, Dort JC. MRI texture analysis predicts p53 status in head and neck squamous cell carcinoma. AJNR Am J Neuroradiol 2014; 36:166-70. [PMID: 25258367 DOI: 10.3174/ajnr.a4110] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
BACKGROUND AND PURPOSE Head and neck cancer is common, and understanding the prognosis is an important part of patient management. In addition to the Tumor, Node, Metastasis staging system, tumor biomarkers are becoming more useful in understanding prognosis and directing treatment. We assessed whether MR imaging texture analysis would correctly classify oropharyngeal squamous cell carcinoma according to p53 status. MATERIALS AND METHODS A cohort of 16 patients with oropharyngeal squamous cell carcinoma was prospectively evaluated by using standard clinical, histopathologic, and imaging techniques. Tumors were stained for p53 and scored by an anatomic pathologist. Regions of interest on MR imaging were selected by a neuroradiologist and then analyzed by using our 2D fast time-frequency transform tool. The quantified textures were assessed by using the subset-size forward-selection algorithm in the Waikato Environment for Knowledge Analysis. Features found to be significant were used to create a statistical model to predict p53 status. The model was tested by using a Bayesian network classifier with 10-fold stratified cross-validation. RESULTS Feature selection identified 7 significant texture variables that were used in a predictive model. The resulting model predicted p53 status with 81.3% accuracy (P < .05). Cross-validation showed a moderate level of agreement (κ = 0.625). CONCLUSIONS This study shows that MR imaging texture analysis correctly predicts p53 status in oropharyngeal squamous cell carcinoma with ∼80% accuracy. As our knowledge of and dependence on tumor biomarkers expand, MR imaging texture analysis warrants further study in oropharyngeal squamous cell carcinoma and other head and neck tumors.
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Affiliation(s)
- M Dang
- Department of Radiology (M.D., J.T.L.), University of Calgary, Calgary, Alberta, Canada
| | - J T Lysack
- Department of Radiology (M.D., J.T.L.), University of Calgary, Calgary, Alberta, Canada
| | - T Wu
- School of Computing, Informatics, Decision Systems Engineering (G.B., T.W.), Arizona State University, Tempe, Arizona
| | - T W Matthews
- From the Section of Otolaryngology-Head and Neck Surgery (T.W.M., S.P.C., P.B., J.C.D.)
| | - S P Chandarana
- From the Section of Otolaryngology-Head and Neck Surgery (T.W.M., S.P.C., P.B., J.C.D.)
| | - N T Brockton
- Department of Population Health Research (N.T.B.), Alberta Health Services, Calgary, Alberta, Canada
| | - P Bose
- From the Section of Otolaryngology-Head and Neck Surgery (T.W.M., S.P.C., P.B., J.C.D.)
| | - G Bansal
- School of Computing, Informatics, Decision Systems Engineering (G.B., T.W.), Arizona State University, Tempe, Arizona
| | - H Cheng
- Department of Radiology (H.C., J.R.M.), Mayo Clinic College of Medicine, Scottsdale, Arizona
| | - J R Mitchell
- Department of Radiology (H.C., J.R.M.), Mayo Clinic College of Medicine, Scottsdale, Arizona
| | - J C Dort
- From the Section of Otolaryngology-Head and Neck Surgery (T.W.M., S.P.C., P.B., J.C.D.)
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Zhao Q, Shi CZ, Luo LP. Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes. Chin J Cancer Res 2014; 26:451-8. [PMID: 25232219 DOI: 10.3978/j.issn.1000-9604.2014.08.07] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2014] [Accepted: 08/09/2014] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE To explore the role of the texture features of images in the diagnosis of solitary pulmonary nodules (SPNs) in different sizes. MATERIALS AND METHODS A total of 379 patients with pathologically confirmed SPNs were enrolled in this study. They were divided into three groups based on the SPN sizes: ≤10, 11-20, and >20 mm. Their texture features were segmented and extracted. The differences in the image features between benign and malignant SPNs were compared. The SPNs in these three groups were determined and analyzed with the texture features of images. RESULTS These 379 SPNs were successfully segmented using the 2D Otsu threshold method and the self-adaptive threshold segmentation method. The texture features of these SPNs were obtained using the method of grey level co-occurrence matrix (GLCM). Of these 379 patients, 120 had benign SPNs and 259 had malignant SPNs. The entropy, contrast, energy, homogeneity, and correlation were 3.5597±0.6470, 0.5384±0.2561, 0.1921±0.1256, 0.8281±0.0604, and 0.8748±0.0740 in the benign SPNs and 3.8007±0.6235, 0.6088±0.2961, 0.1673±0.1070, 0.7980±0.0555, and 0.8550±0.0869 in the malignant SPNs (all P<0.05). The sensitivity, specificity, and accuracy of the texture features of images were 83.3%, 90.0%, and 86.8%, respectively, for SPNs sized ≤10 mm, and were 86.6%, 88.2%, and 87.1%, respectively, for SPNs sized
11-20 mm and 94.7%, 91.8%, and 93.9%, respectively, for SPNs sized >20 mm. CONCLUSIONS The entropy and contrast of malignant pulmonary nodules have been demonstrated to be higher in comparison to those of benign pulmonary nodules, while the energy, homogeneity correlation of malignant pulmonary nodules are lower than those of benign pulmonary nodules. The texture features of images can reflect the tissue features and have high sensitivity, specificity, and accuracy in differentiating SPNs. The sensitivity and accuracy increase for larger SPNs.
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Affiliation(s)
- Qian Zhao
- 1 Department of Statistics, School of Public Health, Guangzhou Medical University, Guangzhou 510182, China ; 2 Medical Imaging Center, the First Affiliated hospital of Jinan University, Guangzhou 510630, China
| | - Chang-Zheng Shi
- 1 Department of Statistics, School of Public Health, Guangzhou Medical University, Guangzhou 510182, China ; 2 Medical Imaging Center, the First Affiliated hospital of Jinan University, Guangzhou 510630, China
| | - Liang-Ping Luo
- 1 Department of Statistics, School of Public Health, Guangzhou Medical University, Guangzhou 510182, China ; 2 Medical Imaging Center, the First Affiliated hospital of Jinan University, Guangzhou 510630, China
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Torheim T, Malinen E, Kvaal K, Lyng H, Indahl UG, Andersen EKF, Futsaether CM. Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1648-1656. [PMID: 24802069 DOI: 10.1109/tmi.2014.2321024] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Dynamic contrast enhanced MRI (DCE-MRI) provides insight into the vascular properties of tissue. Pharmacokinetic models may be fitted to DCE-MRI uptake patterns, enabling biologically relevant interpretations. The aim of our study was to determine whether treatment outcome for 81 patients with locally advanced cervical cancer could be predicted from parameters of the Brix pharmacokinetic model derived from pre-chemoradiotherapy DCE-MRI. First-order statistical features of the Brix parameters were used. In addition, texture analysis of Brix parameter maps was done by constructing gray level co-occurrence matrices (GLCM) from the maps. Clinical factors and first- and second-order features were used as explanatory variables for support vector machine (SVM) classification, with treatment outcome as response. Classification models were validated using leave-one-out cross-model validation. A random value permutation test was used to evaluate model significance. Features derived from first-order statistics could not discriminate between cured and relapsed patients (specificity 0%-20%, p-values close to unity). However, second-order GLCM features could significantly predict treatment outcome with accuracies (~70%) similar to the clinical factors tumor volume and stage (69%). The results indicate that the spatial relations within the tumor, quantified by texture features, were more suitable for outcome prediction than first-order features.
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Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A. The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol 2014; 87:20140369. [PMID: 25051978 DOI: 10.1259/bjr.20140369] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Predicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.
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Affiliation(s)
- S Alobaidli
- 1 Centre for Vision, Speech and Signal Processing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK
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Holli-Helenius K, Luoto TM, Brander A, Wäljas M, Iverson GL, Ohman J. Structural integrity of medial temporal lobes of patients with acute mild traumatic brain injury. J Neurotrauma 2014; 31:1153-60. [PMID: 24579770 DOI: 10.1089/neu.2013.2978] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Post-traumatic amnesia (PTA) is an acute characteristic of traumatic brain injury (TBI) and the duration of PTA is commonly used to estimate the severity of brain injury. In the context of mild traumatic brain injury (MTBI), PTA is an essential part of the routine clinical assessment. Macroscopic lesions in temporal lobes, especially hippocampal regions, are thought to be connected to memory loss. However, conventional neuroimaging has failed to reveal neuropathological correlates of PTA in MTBI. Texture analysis (TA) is an image analysis technique that quantifies the minor MRI signal changes among image pixels and, therefore, the variations in intensity patterns within the image. The objective of this work was to apply the TA technique to MR images of MTBI patients and control subjects, and to assess the microstructural damage in medial temporal lobes of patients with MTBI with definite PTA. TA was performed for fluid-attenuated inversion recovery (FLAIR) images of 50 MTBI patients and 50 age- and gender-matched controls in the regions of the amygdala, hippocampus, and thalamus. It was hypothesized that 1) there would be statistically significant differences in TA parameters between patients with MTBIs and controls, and 2) the duration of PTA would be related to TA parameters in patients with MTBI. No significant textural differences were observed between patients and controls in the regions of interest (p>0.01). No textural features were observed to correlate with the duration of PTA. Subgroup analyses were conducted on patients with PTA of>1 h, (n=33) and compared the four TA parameters to the age- and gender-matched controls (n=33). The findings were similar. This study did not reveal significant textural changes in medial temporal structures that could be related to the duration of PTA.
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Affiliation(s)
- Kirsi Holli-Helenius
- 1 Medical Imaging Centre and Hospital Pharmacy, Department of Radiology, Tampere University Hospital , Tampere, Finland
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Yiu EM, Laughlin S, Verhey LH, Banwell BL. Clinical and magnetic resonance imaging (MRI) distinctions between tumefactive demyelination and brain tumors in children. J Child Neurol 2014; 29:654-65. [PMID: 24092896 DOI: 10.1177/0883073813500713] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Tumefactive demyelinating lesions can be difficult to distinguish from tumors. Clinical and magnetic resonance imaging features of children with tumefactive demyelination and supratentorial brain tumors were compared. Patients were identified through a 23-site national demyelinating disease study, and from a single-site neuroradiology database. For inclusion, lesions met at least 1 of 3 criteria: maximal cross-sectional diameter >20 mm, local or global cerebral mass effect, or presence of perilesional edema. Thirty-one children with tumefactive demyelination (5 with solitary lesions) were identified: 27 of 189 (14.3%) from the demyelinating disease study and 4 from the database. Thirty-three children with tumors were identified. Children with tumefactive demyelination were more likely to have an abnormal neurologic examination and polyfocal neurologic deficits compared to children with tumors. Tumefactive demyelination was distinguished from tumor by the presence of multiple lesions, absence of cortical involvement, and decrease in lesion size or detection of new lesions on serial imaging.
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Affiliation(s)
- Eppie M Yiu
- 1Children's Neuroscience Centre, Royal Children's Hospital Melbourne, Parkville, Australia
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Abstract
Brain tumors are one of the most challenging disorders encountered, and early and accurate diagnosis is essential for the management and treatment of these tumors. In this article, diagnostic modalities including single-photon emission computed tomography, positron emission tomography, magnetic resonance imaging, and optical imaging are reviewed. We mainly focus on the newly emerging, specific imaging probes, and their potential use in animal models and clinical settings.
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Affiliation(s)
- Huile Gao
- Key Laboratory of Smart Drug Delivery, Ministry of Education & PLA, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China
| | - Xinguo Jiang
- Key Laboratory of Smart Drug Delivery, Ministry of Education & PLA, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China
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50
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Sikiö M, Harrison LCV, Nikander R, Ryymin P, Dastidar P, Eskola HJ, Sievänen H. Influence of exercise loading on magnetic resonance image texture of thigh soft tissues. Clin Physiol Funct Imaging 2013; 34:370-6. [DOI: 10.1111/cpf.12107] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2013] [Accepted: 10/30/2013] [Indexed: 12/19/2022]
Affiliation(s)
- Minna Sikiö
- Department of Radiology; Medical Imaging Center and Hospital Pharmacy; Tampere University Hospital; Tampere Finland
- Department of Electronics and Communications Engineering; Tampere University of Technology; Tampere Finland
| | - Lara C. V. Harrison
- Department of Electronics and Communications Engineering; Tampere University of Technology; Tampere Finland
- Department of Anaesthesia; Tampere University Hospital; Tampere Finland
| | - Riku Nikander
- Department of Health Sciences; University of Jyväskylä; Tampere Finland
- GeroCenter Foundation for Aging Research and Development; Jyväskylä Finland
- Jyväskylä Central Hospital; Jyväskylä Finland
| | - Pertti Ryymin
- Department of Radiology; Medical Imaging Center and Hospital Pharmacy; Tampere University Hospital; Tampere Finland
| | - Prasun Dastidar
- Department of Radiology; Medical Imaging Center and Hospital Pharmacy; Tampere University Hospital; Tampere Finland
- Tampere Medical School; University of Tampere; Tampere Finland
| | - Hannu J. Eskola
- Department of Radiology; Medical Imaging Center and Hospital Pharmacy; Tampere University Hospital; Tampere Finland
- Department of Electronics and Communications Engineering; Tampere University of Technology; Tampere Finland
| | - Harri Sievänen
- Bone Research Group; UKK Intstitute for Health Promotion Research; Tampere Finland
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