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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] [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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Reyes D, Sánchez J. Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging. Heliyon 2024; 10:e25468. [PMID: 38352765 PMCID: PMC10862681 DOI: 10.1016/j.heliyon.2024.e25468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/09/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024] Open
Abstract
Brain tumors are a diverse group of neoplasms that are challenging to detect and classify due to their varying characteristics. Deep learning techniques have proven to be effective in tumor classification. However, there is a lack of studies that compare these techniques using a common methodology. This work aims to analyze the performance of convolutional neural networks in the classification of brain tumors. We propose a network consisting of a few convolutional layers, batch normalization, and max-pooling. Then, we explore recent deep architectures, such as VGG, ResNet, EfficientNet, or ConvNeXt. The study relies on two magnetic resonance imaging datasets with over 3000 images of three types of tumors -gliomas, meningiomas, and pituitary tumors-, as well as images without tumors. We determine the optimal hyperparameters of the networks using the training and validation sets. The training and test sets are used to assess the performance of the models from different perspectives, including training from scratch, data augmentation, transfer learning, and fine-tuning. The experiments are performed using the TensorFlow and Keras libraries in Python. We compare the accuracy of the models and analyze their complexity based on the capacity of the networks, their training times, and image throughput. Several networks achieve high accuracy rates on both datasets, with the best model achieving 98.7% accuracy, which is on par with state-of-the-art methods. The average precision for each type of tumor is 94.3% for gliomas, 93.8% for meningiomas, 97.9% for pituitary tumors, and 95.3% for images without tumors. VGG is the largest model with over 171 million parameters, whereas MobileNet and EfficientNetB0 are the smallest ones with 3.2 and 5.9 million parameters, respectively. These two neural networks are also the fastest to train with 23.7 and 25.4 seconds per epoch, respectively. On the other hand, ConvNext is the slowest model with 58.2 seconds per epoch. Our custom model obtained the highest image throughput with 234.37 images per second, followed by MobileNet with 226 images per second. ConvNext obtained the smallest throughput with 97.35 images per second. ResNet, MobileNet, and EfficientNet are the most accurate networks, with MobileNet and EfficientNet demonstrating superior performance in terms of complexity. Most models achieve the best accuracy using transfer learning followed by a fine-tuning step. However, data augmentation does not contribute to increasing the accuracy of the models in general.
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Affiliation(s)
- Daniel Reyes
- Dr. Stetter ITQ S.L.U., Parque Científico Tecnológico, Las Palmas de Gran Canaria, 35017, Spain
- Department of Computer Science, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, 35017, Spain
| | - Javier Sánchez
- Department of Computer Science, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, 35017, Spain
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You S, Reyes M. Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation. FRONTIERS IN NEUROIMAGING 2022; 1:1012639. [PMID: 37555149 PMCID: PMC10406260 DOI: 10.3389/fnimg.2022.1012639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/12/2022] [Indexed: 08/10/2023]
Abstract
Contrast and texture modifications applied during training or test-time have recently shown promising results to enhance the generalization performance of deep learning segmentation methods in medical image analysis. However, a deeper understanding of this phenomenon has not been investigated. In this study, we investigated this phenomenon using a controlled experimental setting, using datasets from the Human Connectome Project and a large set of simulated MR protocols, in order to mitigate data confounders and investigate possible explanations as to why model performance changes when applying different levels of contrast and texture-based modifications. Our experiments confirm previous findings regarding the improved performance of models subjected to contrast and texture modifications employed during training and/or testing time, but further show the interplay when these operations are combined, as well as the regimes of model improvement/worsening across scanning parameters. Furthermore, our findings demonstrate a spatial attention shift phenomenon of trained models, occurring for different levels of model performance, and varying in relation to the type of applied image modification.
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Affiliation(s)
- Suhang You
- Medical Image Analysis Group, ARTORG, Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
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Hussain L, Malibari AA, Alzahrani JS, Alamgeer M, Obayya M, Al-Wesabi FN, Mohsen H, Hamza MA. Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI. Sci Rep 2022; 12:15389. [PMID: 36100621 PMCID: PMC9470580 DOI: 10.1038/s41598-022-19563-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractAccurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.
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Abstract
AbstractBrain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.
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Use of machine learning to select texture features in investigating the effects of axial loading on T 2-maps from magnetic resonance imaging of the lumbar discs. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2021; 31:1979-1991. [PMID: 34718864 DOI: 10.1007/s00586-021-07036-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/20/2021] [Accepted: 10/18/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND Recent advances in texture analysis and machine learning offer new opportunities to improve the application of imaging to intervertebral disc biomechanics. This study employed texture analysis and machine learning on MRIs to investigate the lumbar disc's response to loading. METHODS Thirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain spent 20 min lying in a relaxed unloaded supine position, followed by 20 min loaded in compression, and then 20 min with traction applied. T2-weighted MR images were acquired during the last 5 min of each loading condition. Custom image analysis software was used to segment discs from adjacent tissues semi-automatically and segment each disc into the nucleus, anterior and posterior annulus automatically. A grey-level, co-occurrence matrix with one to four pixels offset in four directions (0°, 45°, 90° and 135°) was then constructed (320 feature/tissue). The Random Forest Algorithm was used to select the most promising classifiers. Linear mixed-effect models and Cohen's d compared loading conditions. FINDINGS All statistically significant differences (p < 0.001) were observed in the nucleus and posterior annulus in the 135° offset direction at the L4-5 level between lumbar compression and traction. Correlation (P2-Offset, P4-Offset) and information measure of correlation 1 (P3-Offset, P4-Offset) detected significant changes in the nucleus. Statistically significant changes were also observed for homogeneity (P2-Offset, P3-Offset), contrast (P2-Offset), and difference variance (P4-Offset) of the posterior annulus. INTERPRETATION MRI textural features may have the potential of identifying the disc's response to loading, particularly in the nucleus and posterior annulus, which appear most sensitive to loading. LEVEL OF EVIDENCE Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.
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Rodrigues A, Loman K, Nawrocki J, Hoang JK, Chang Z, Mowery YM, Oyekunle T, Niedzwiecki D, Brizel DM, Craciunescu O. Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma. Front Oncol 2021; 11:708398. [PMID: 34540674 PMCID: PMC8444263 DOI: 10.3389/fonc.2021.708398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III–IV (7th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.
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Affiliation(s)
- Anna Rodrigues
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Kelly Loman
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Jeff Nawrocki
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Jenny K Hoang
- Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
| | - Zheng Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Yvonne M Mowery
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Taofik Oyekunle
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Donna Niedzwiecki
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - David M Brizel
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.,Department of Head and Neck Surgery and Communication Sciences, Duke University Medical Center, Durham, NC, United States
| | - Oana Craciunescu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
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Lee D, Lee HJ. Magnetic resonance imaging texture analysis for the evaluation of viable ovarian tissue in patients with ovarian endometriosis. Yeungnam Univ J Med 2021; 39:24-30. [PMID: 34261207 PMCID: PMC8895959 DOI: 10.12701/yujm.2021.01165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 06/25/2021] [Indexed: 11/28/2022] Open
Abstract
Background Texture analysis has been used as a method for quantifying image properties based on textural features. The aim of the present study was to evaluate the usefulness of magnetic resonance imaging (MRI) texture analysis for the evaluation of viable ovarian tissue on the perfusion map of ovarian endometriosis. Methods To generate a normalized perfusion map, subtracted T1-weighted imaging (T1WI), T1WI and contrast-enhanced T1W1 with sequences were performed using the same parameters in 25 patients with surgically confirmed ovarian endometriosis. Integrated density is defined as the sum of the values of the pixels in the image or selection. We investigated the parameters for texture analysis in ovarian endometriosis, including angular second moment (ASM), contrast, correlation, inverse difference moment (IDM), and entropy, which is equivalent to the product of area and mean gray value. Results The perfusion ratio and integrated density of normal ovary were 0.52±0.05 and 238.72±136.21, respectively. Compared with the normal ovary, the affected ovary showed significant differences in total size (p<0.001), fractional area ratio (p<0.001), and perfusion ratio (p=0.010) but no significant differences in perfused tissue area (p=0.158) and integrated density (p=0.112). In comparison of parameters for texture analysis between the ovary with endometriosis and the contralateral normal ovary, ASM (p=0.004), contrast (p=0.002), IDM (p<0.001), and entropy (p=0.028) showed significant differences. A linear regression analysis revealed that fractional area had significant correlations with ASM (r2=0.211), IDM (r2=0.332), and entropy (r2=0.289). Conclusion MRI texture analysis could be useful for the evaluation of viable ovarian tissues in patients with ovarian endometriosis.
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Affiliation(s)
- Dayong Lee
- Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Hyun Jung Lee
- Department of Obstetrics and Gynecology, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
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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: 7] [Impact Index Per Article: 2.3] [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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Anjum S, Hussain L, Ali M, Abbasi AA, Duong TQ. Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:2882-2908. [PMID: 33892576 DOI: 10.3934/mbe.2021146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.
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Affiliation(s)
- Sadia Anjum
- Department of IT, Hazara University, Mansehra 21120, KPK, Pakistan
| | - Lal Hussain
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, Muzaffarabad 13100, Pakistan
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, Neelum Campus, Athmuqam 13230, Pakistan
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467, USA
| | - Mushtaq Ali
- Department of IT, Hazara University, Mansehra 21120, KPK, Pakistan
| | - Adeel Ahmed Abbasi
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, Muzaffarabad 13100, Pakistan
- School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China
| | - Tim Q. Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467, USA
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Cong M, Qiu S, Li R, Sun H, Cong L, Hou Z. Development of a predictive model of growth hormone deficiency and idiopathic short stature in children. Exp Ther Med 2021; 21:494. [PMID: 33791003 PMCID: PMC8005695 DOI: 10.3892/etm.2021.9925] [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: 02/18/2020] [Accepted: 08/19/2020] [Indexed: 12/14/2022] Open
Abstract
The aim of the present study was to develop predictive models using clinical features and MRI texture features for distinguishing between growth hormone deficiency (GHD) and idiopathic short stature (ISS) in children with short stature. This retrospective study included 362 children with short stature from Children's Hospital of Hebei Province. GHD and ISS were identified via the GH stimulation test using arginine. Overall, there were 190 children with GHD and 172 with ISS. A total of 57 MRI texture features were extracted from the pituitary gland region of interest using C++ language and Matlab software. In addition, the laboratory examination data were collected. Receiver operating characteristic (ROC) regression curves were generated for the predictive performance of clinical features and MRI texture features. Logistic regression models based on clinical and texture features were established for discriminating children with GHD and ISS. Two clinical features [IGF-1 (insulin growth factor-1) and IGFBP-3 (IGF binding protein-3) levels] were used to build the clinical predictive model, whereas the three best MRI textures were used to establish the MRI texture predictive model. The ROC analysis of the two models revealed predictive performance for distinguishing GHD from ISS. The accuracy of predicting ISS from GHD was 64.5% in ROC analysis [area under the curve (AUC), 0.607; sensitivity, 57.6%; specificity, 72.1%] of the clinical model. The accuracy of predicting ISS from GHD was 80.4% in ROC analysis (AUC, 0.852; sensitivity, 93.6%; specificity, 65.8%) of the MRI texture predictive model. In conclusion, these findings indicated that a texture predictive model using MRI texture features was superior for distinguishing children with GHD from those with ISS compared with the model developed using clinical features.
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Affiliation(s)
- Mengdi Cong
- Department of Computed Tomography and Magnetic Resonance, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050031, P.R. China
| | - Shi Qiu
- Key Laboratory of Spectral Imaging Technology Chinese Academy of Science, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi 710119, P.R. China
| | - Rongpin Li
- Department of Computed Tomography and Magnetic Resonance, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050031, P.R. China
| | - Haiyan Sun
- Department of Computed Tomography and Magnetic Resonance, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050031, P.R. China
| | - Lining Cong
- Department of Radiology, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050031, P.R. China
| | - Zhenzhou Hou
- Department of Computed Tomography and Magnetic Resonance, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050031, P.R. China
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Jiang H, Shu Z, Luo X, Wu M, Wang M, Feng Q, Chen J, Lin C, Ding Z. Noninvasive radiomics-based method for evaluating idiopathic central precocious puberty in girls. J Int Med Res 2021; 49:300060521991023. [PMID: 33596690 PMCID: PMC7897833 DOI: 10.1177/0300060521991023] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Objective Traditional approaches that involve measuring the height and volume of the pituitary by magnetic resonance imaging (MRI) are unreliable. We investigated the use of a more accurate method using texture analysis to evaluate idiopathic central precocious puberty (ICPP) by MRI. Methods In total, 352 texture features of the pituitary were extracted from 12 healthy girls and 18 girls with ICPP. A LASSO regression model and linear regression model were used to create the prediction model. Pearson’s correlation analysis and receiver operating characteristic curves were used to evaluate the predictive performance. Results The radiomics score had a significant linear relationship with the luteinizing hormone concentration and the luteinizing hormone/follicle-stimulating hormone ratio. The radiomics score showed better predictive performance than traditional pituitary measurements. The area under the curve of the radiomics score, pituitary height, and variable combinations was 0.759 (95% confidence interval [CI], 0.583–0.936), 0.681 (95% CI, 0.483–0.878), and 0.829 (95% CI, 0.681–0.976), respectively. Conclusion Combination of the radiomics score with pituitary height measurements allows for better evaluation of the pituitary during diagnostic imaging, indicating satisfactory potential for efficacy assessments.
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Affiliation(s)
- Hongyang Jiang
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Xiaoming Luo
- Department of Pediatrics, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Meizhen Wu
- Department of Pediatrics, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Mei Wang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qi Feng
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junfa Chen
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Chunmiao Lin
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Wang G, Wang B, Wang Z, Li W, Xiu J, Liu Z, Han M. Radiomics signature of brain metastasis: prediction of EGFR mutation status. Eur Radiol 2021; 31:4538-4547. [PMID: 33439315 DOI: 10.1007/s00330-020-07614-x] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/05/2020] [Accepted: 12/07/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To predict epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma using MR-based radiomics signature of brain metastasis and explore the optimal MR sequence for prediction. METHODS Data from 52 patients with brain metastasis from lung adenocarcinoma (28 with mutant EGFR, 24 with wild-type EGFR) were retrospectively reviewed. Contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR), T2WI, and DWI sequences were selected for radiomics features extraction. A total of 438 radiomics features were extracted from each MR sequence. All sequences were randomly divided into training and validation cohorts. The least absolute shrinkage selection operator was used to select informative features, a radiomics signature was built with the logistic regression model of the training cohort, and the radiomics signature performance was evaluated using the validation cohort and an independent testing data set. RESULTS The radiomics signature built on 9 selected features showed good discrimination in both the training and validation cohorts for T2-FLAIR. The radiomics signature of T2-FLAIR yielded an AUC of 0.987, a classification accuracy of 0.991, sensitivity of 1.000, and specificity of 0.980 in the validation cohort. The AUC was 0.871 in the independent testing data set. The AUCs of our radiomics signature to differentiate exon 19 and exon 21 mutations were 0.529, 0.580, 0.645, and 0.406 for T1-CE, T2-FLAIR, T2WI, and DWI, respectively. CONCLUSIONS We developed a T2-FLAIR radiomics signature that can be used as a noninvasive auxiliary tool for predicting EGFR mutation status in lung adenocarcinoma, which is helpful to guide therapeutic strategies. KEY POINTS • MR-based radiomics signature of brain metastasis may help predict EGFR mutation status in lung adenocarcinoma, especially using T2-FLAIR. • Nine radiomics features extracted from T2-FLAIR sequence strongly correlate with EGFR mutation status. • Radiomics features reflect tumor heterogeneity through potential changes in tissue morphology caused by EGFR mutation.
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Affiliation(s)
- Guangyu Wang
- Cancer Therapy and Research Center, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, People's Republic of China
| | - Bomin Wang
- School of Information Science and Engineering, Shandong University, 27 Shanda South Road, Jinan, 250100, Shandong, People's Republic of China
| | - Zhou Wang
- Medical Imaging Department, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, People's Republic of China
| | - Wenchao Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University and Healthcare Big Data Institute of Shandong University, Jinan, 250012, People's Republic of China
| | - Jianjun Xiu
- Medical Imaging Department, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, People's Republic of China
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, 27 Shanda South Road, Jinan, 250100, Shandong, People's Republic of China.
| | - Mingyong Han
- Cancer Therapy and Research Center, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jingwuweiqi Road, Jinan, 250021, Shandong, People's Republic of China.
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Wu H, Han X, Wang Z, Mo L, Liu W, Guo Y, Wei X, Jiang X. Prediction of the Ki-67 marker index in hepatocellular carcinoma based on CT radiomics features. Phys Med Biol 2020; 65:235048. [PMID: 32756021 DOI: 10.1088/1361-6560/abac9c] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The noninvasive detection of tumor proliferation is of great value and the Ki-67 is a biomarker of tumor proliferation. We hypothesized that radiomics characteristics may be related to tumor proliferation. To evaluate whether computed tomography (CT) radiomics feature analyses could aid in assessing the Ki-67 marker index in hepatocellular carcinoma (HCC), we retrospectively analyzed preoperative CT findings of 74 patients with HCC. The texture feature calculations were computed from MaZda 4.6 software, and the sequential forward selection algorithm was used as the selection method. The correlation between radiomics features and the Ki-67 marker index, as well as the difference between low Ki-67 (<10%) and high Ki-67 (≥10%) groups were evaluated. A simple logistic regression model was used to evaluate the associations between texture features and high Ki-67, and receiver operating characteristic analysis was performed on important parameters to assess the ability of radiomics characteristics to distinguish the high Ki-67 group from the low Ki-67 group. Contrast, correlation, and inverse difference moment (IDM) were significantly different (P < 0.001) between the low and high Ki-67 groups. Contrast (odds ratio [OR] = 0.957; 95% confidence interval [CI]: 0.926-0.990, P = 0.01) and correlation (OR = 2.5☆105; 95% CI: 7.560-8.9☆109; P = 0.019) were considered independent risk factors for combined model building with logistic regression. Angular second moment (r = -0.285, P = 0.014), contrast (r = -0.449, P < 0.001), correlation (r = 0.552, P < 0.001), IDM (r = 0.458, P < 0.001), and entropy (r = 0.285, P = 0.014) strongly correlated with the Ki-67 scores. Contrast, correlation, and the combined predictor were predictive of Ki-67 status (P < 0.001), with areas under the curve ranging from 0.777 to 0.836. The radiomics characteristics of CT have potential as biomarkers for predicting Ki-67 status in patients with HCC. These findings suggest that the radiomics features of CT might be used as a noninvasive measure of cellular proliferation in HCC.
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Affiliation(s)
- Hongzhen Wu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510180, People's Republic of China. Jinan University, Guangzhou 510632, Guangdong, People's Republic of China
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Simpson G, Ford JC, Llorente R, Portelance L, Yang F, Mellon EA, Dogan N. Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features. Phys Med 2020; 80:209-220. [PMID: 33190077 DOI: 10.1016/j.ejmp.2020.10.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE The purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images. METHODS Texture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design. RESULTS The phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination. CONCLUSION Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.
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Affiliation(s)
- Garrett Simpson
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Ricardo Llorente
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Lorraine Portelance
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Fei Yang
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA.
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Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy. PLoS One 2020; 15:e0240043. [PMID: 33017440 PMCID: PMC7535039 DOI: 10.1371/journal.pone.0240043] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 09/18/2020] [Indexed: 01/28/2023] Open
Abstract
Background We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI. Methods A total of 14 T4NxM0 NPC patients with histologically proven “in field” recurrence in the post nasal space following curative intent IMRT were included in this study. Pretreatment MRI were co-registered with MRI at the time of recurrence for the delineation of gross tumor volume at diagnosis(GTV) and at recurrence(GTVr). A total of 7 histogram features and 40 texture features were computed from the recurrent(GTVr) and non-recurrent region(GTV-GTVr). Paired t-tests and Wilcoxon signed-rank tests were carried out on the 47 quantified radiomics features. Results A total of 7 features were significantly different between recurrent and non-recurrent regions. Other than the variance from intensity-based histogram, the remaining six significant features were either from the gray-level size zone matrix (GLSZM) or the neighbourhood gray-tone difference matrix (NGTDM). Conclusions The radiomic features extracted from pre-treatment MRI can potentially reflect the difference between recurrent and non-recurrent regions within a tumor and has a potential role in pre-treatment identification of intra-tumoral radio-resistance for selective dose escalation.
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Differentiation of Endometriomas from Ovarian Hemorrhagic Cysts at Magnetic Resonance: The Role of Texture Analysis. ACTA ACUST UNITED AC 2020; 56:medicina56100487. [PMID: 32977428 PMCID: PMC7598287 DOI: 10.3390/medicina56100487] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/14/2020] [Accepted: 09/20/2020] [Indexed: 11/16/2022]
Abstract
Background and Objectives: To assess ovarian cysts with texture analysis (TA) in magnetic resonance (MRI) images for establishing a differentiation criterion for endometriomas and functional hemorrhagic cysts (HCs) that could potentially outperform their classic MRI diagnostic features. Materials and Methods: Forty-three patients with known ovarian cysts who underwent MRI were retrospectively included (endometriomas, n = 29; HCs, n = 14). TA was performed using dedicated software based on T2-weighted images, by incorporating the whole lesions in a three-dimensional region of interest. The most discriminative texture features were highlighted by three selection methods (Fisher, probability of classification error and average correlation coefficients, and mutual information). The absolute values of these parameters were compared through univariate, multivariate, and receiver operating characteristic analyses. The ability of the two classic diagnostic signs ("T2 shading" and "T2 dark spots") to diagnose endometriomas was assessed by quantifying their sensitivity (Se) and specificity (Sp), following their conventional assessment on T1-and T2-weighted images by two radiologists. Results: The diagnostic power of the one texture parameter that was an independent predictor of endometriomas (entropy, 75% Se and 100% Sp) and of the predictive model composed of all parameters that showed statistically significant results at the univariate analysis (100% Se, 100% Sp) outperformed the ones shown by the classic MRI endometrioma features ("T2 shading", 75.86% Se and 35.71% Sp; "T2 dark spots", 55.17% Se and 64.29% Sp). Conclusion: Whole-lesion MRI TA has the potential to offer a superior discrimination criterion between endometriomas and HCs compared to the classic evaluation of the two lesions' MRI signal behaviors.
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Csutak C, Ștefan PA, Lenghel LM, Moroșanu CO, Lupean RA, Șimonca L, Mihu CM, Lebovici A. Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone. Brain Sci 2020; 10:brainsci10090638. [PMID: 32947822 PMCID: PMC7565295 DOI: 10.3390/brainsci10090638] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/03/2020] [Accepted: 09/14/2020] [Indexed: 11/16/2022] Open
Abstract
High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.
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Affiliation(s)
- Csaba Csutak
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Paul-Andrei Ștefan
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeș Street, number 8, Cluj-Napoca, 400012 Cluj, Romania
- Correspondence: ; Tel.: +40-743-957-206
| | - Lavinia Manuela Lenghel
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Cezar Octavian Moroșanu
- Department of Neurosurgery, North Bristol Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol BS2 8BJ, UK;
| | - Roxana-Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Larisa Șimonca
- Department of Paediatric Surgery, Bristol Royal Hospital for Children, Upper Maudlin Street, Bristol BS2 8BJ, UK;
| | - Carmen Mihaela Mihu
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
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Loizou CP, Pantzaris M, Pattichis CS. Normal appearing brain white matter changes in relapsing multiple sclerosis: Texture image and classification analysis in serial MRI scans. Magn Reson Imaging 2020; 73:192-202. [PMID: 32890673 DOI: 10.1016/j.mri.2020.08.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 08/20/2020] [Accepted: 08/27/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVE There is a clinical interest in identifying normal appearing white matter (NAWM) areas in brain T2-weighted (T2W) MRI scans in multiple sclerosis (MS) subjects. These areas are susceptible to disease development and areas need to be studied in order to find potential associations between texture feature changes and disease progression. METHODS The subjects investigated had a first demyelinating event (Clinically Isolated Syndrome-CIS) at baseline (Time0), and the NAWM0 (i.e. NAWM at Time0) of the brain tissue was subsequently converted to demyelinating plaques (as evaluated in a follow up MRI at Time6-12). 38 untreated subjects that had developed a CIS, had brain MRI scans within an interval of 6-12 months (Time6-12 at follow-up). An experienced MS neurologist manually delineated the demyelinating lesions at Time0 (L0) and at Time6-12 (L6-12). Areas in the Time6-12 MRI scans, where new lesions had been developed, were mapped back to their corresponding NAWM areas on the Time0 MR scans (ROIS0). In addition, contralateral ROIs of similar size and shape were segmented on the same images at Time0 (ROISC0) to form an intra-subject control group. Following that, texture features were extracted from all prescribed areas and MS lesions. RESULTS Texture features were used as input into Support Vector Machine (SVM) models to differentiate between the following: NAWM0 vs ROISC0, NAWM0 vs NAWM6-12, NAWM0 vs L0, NAWM6-12 vs L6-12, ROIS0 vs L0, ROIS0 vs L6-12 and ROIS0 vs ROISC0, where the corresponding % correct classifications scores were 89%, 95%, 98%, 92%, 85%, 90% and 65% respectively. CONCLUSIONS Texture features may provide complementary information for following up the development and progression of MS disease. Future work will investigate the proposed method on more subjects.
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Affiliation(s)
- Christos P Loizou
- Faculty of Engineering & Technology, Department of Electrical Engineering and Computer Engineering and Informatics, Cyprus University of Technology, 30 Arch. Kyprianos Str., Limassol CY-3036, Cyprus.
| | - Marios Pantzaris
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
| | - Constandinos S Pattichis
- Departement of Computer Science, University of Cyprus, Nicosia, Cyprus; Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE CoE), Nicosia, Cyprus.
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Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI. PLoS One 2020; 15:e0234800. [PMID: 32555662 PMCID: PMC7299320 DOI: 10.1371/journal.pone.0234800] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 06/02/2020] [Indexed: 01/10/2023] Open
Abstract
Objective To investigate whether texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with human epidermal growth factor receptor type 2 (HER2) 2+ status of breast cancer. Materials and methods 92 MRI cases including 52 HER2 2+ positive and 40 negative patients confirmed by fluorescence in situ hybridization were retrospectively selected. The lesion area was semi-automatically delineated, and a total of 488 texture features were respectively extracted from precontrast, postcontrast, and subtraction images. The Student’s t-test or Mann-Whitney U test was performed to identify statistically significant features between different HER2 2+ amplification groups. Least absolute shrinkage and selection operator (LASSO) was used to search for the optimal feature subsets. Three machine learning classifiers, logistic regression analysis (LRA), quadratic discriminant analysis (QDA), and support vector machine (SVM), were used with a leave-one-out cross validation method to establish the classification models of HER2 2+ status. Classification performance was evaluated by receiver operating characteristic (ROC) analysis. Results Based on the texture analysis with SVM model, the areas under the ROC curve (AUCs) were 0.890 for subtraction images, 0.736 for postcontrast images, and 0.672 for precontrast images, respectively. For LRA model, the AUCs were 0.884, 0.733, and 0.623, respectively. For QDA model, the AUCs were 0.831, 0.726, and 0.568, respectively. LRA and the SVM model with subtraction images reached significantly better performance than the QDA model (P = 0.0227 and P = 0.0088, respectively). Conclusion Texture features of breast cancer extracted from DCE-MRI are associated with HER2 2+ status. Additional studies are necessary to confirm the present preliminary findings.
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Xu J, Cui X, Wang B, Wang G, Han M, Li R, Qi Y, Xiu J, Yang Q, Liu Z, Han M. Texture analysis of early cerebral tissue damage in magnetic resonance imaging of patients with lung cancer. Oncol Lett 2020; 19:3089-3100. [PMID: 32256809 PMCID: PMC7074325 DOI: 10.3892/ol.2020.11426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 10/23/2019] [Indexed: 12/24/2022] Open
Abstract
Primary tumors can secrete many cytokines, inducing tissue damage or microstructural changes in distant organs. The purpose of this study was to investigate changes in texture features in the cerebral tissue of patients with lung cancer without brain metastasis. In this study, 50 patients with lung cancers underwent 3.0-T magnetic resonance imaging (MRI) within 2 weeks of being diagnosed with lung cancer. Texture analysis (TA) was carried out in 8 gray matter areas, including bilateral frontal cortices, parietal cortices, occipital cortices and temporal cortices, as well as 2 areas of bilateral frontoparietal white matter. The same procedure was performed for 57 healthy controls. A total of 32 texture parameters were separately compared between the patients and controls in the different cerebral tissue sites. Texture features among patients based on histological type and clinical stage were also compared. Of the 32 texture parameters, 27 showed significant differences between patients with lung cancer and healthy controls. There were significant differences in cerebral tissue, both gray matter and white matter between patients and controls, especially in several wavelet-based parameters. However, there were no significant differences between tissue at homologous sites in bilateral hemispheres, either in patients or controls. TA detected overt changes in the texture features of cerebral tissue in patients with lung cancer without brain metastasis compared with those of healthy controls. TA may be considered as a novel and adjunctive approach to conventional brain MRI to reveal cerebral tissue changes invisible on MRI alone in patients with lung cancer.
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Affiliation(s)
- Jiying Xu
- Cancer Therapy and Research Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China
| | - Xiaoxiao Cui
- School of Information Science and Engineering, Shandong University, Jinan, Shandong 250100, P.R. China
| | - Bomin Wang
- School of Information Science and Engineering, Shandong University, Jinan, Shandong 250100, P.R. China
| | - Guangyu Wang
- Cancer Therapy and Research Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China
| | - Meng Han
- Cancer Therapy and Research Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China
| | - Ranran Li
- Cancer Therapy and Research Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China
| | - Yana Qi
- Cancer Therapy and Research Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China
| | - Jianjun Xiu
- Cancer Therapy and Research Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China
| | - Qianlong Yang
- Cancer Therapy and Research Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, Jinan, Shandong 250100, P.R. China
| | - Mingyong Han
- Cancer Therapy and Research Center, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China
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Schick U, Lucia F, Dissaux G, Visvikis D, Badic B, Masson I, Pradier O, Bourbonne V, Hatt M. MRI-derived radiomics: methodology and clinical applications in the field of pelvic oncology. Br J Radiol 2019; 92:20190105. [PMID: 31538516 DOI: 10.1259/bjr.20190105] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Personalized medicine aims at offering optimized treatment options and improved survival for cancer patients based on individual variability. The success of precision medicine depends on robust biomarkers. Recently, the requirement for improved non-biologic biomarkers that reflect tumor biology has emerged and there has been a growing interest in the automatic extraction of quantitative features from medical images, denoted as radiomics. Radiomics as a methodological approach can be applied to any image and most studies have focused on PET, CT, ultrasound, and MRI. Here, we aim to present an overview of the radiomics workflow as well as the major challenges with special emphasis on the use of multiparametric MRI datasets. We then reviewed recent studies on radiomics in the field of pelvic oncology including prostate, cervical, and colorectal cancer.
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Affiliation(s)
- Ulrike Schick
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - François Lucia
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Gurvan Dissaux
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Dimitris Visvikis
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Bogdan Badic
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Department of General and Digestive Surgery, University Hospital, Brest, France
| | - Ingrid Masson
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Olivier Pradier
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France.,Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - Vincent Bourbonne
- Radiation Oncology department, University Hospital, Brest, France.,LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, ISBAM, UBO, UBL, Brest, France
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Texture analysis of placental MRI: can it aid in the prenatal diagnosis of placenta accreta spectrum? Abdom Radiol (NY) 2019; 44:3175-3184. [PMID: 31240328 DOI: 10.1007/s00261-019-02104-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE To determine if texture analysis can differentiate placenta accreta spectrum (PAS) from normal placenta on MRI. METHODS We performed retrospective image analysis of 80 patients, comprised of 46 patients with PAS and 34 patients without PAS. Histopathology was used as the reference standard. Sagittal single shot fast spin echo T2-weighted MRI sequences acquired from a single institution were analyzed. Placental heterogeneity was quantified using in-house software on a Matlab platform, including the standard deviation of pixel intensity, coefficient of variation, gray-level co-occurrence matrices (GLCM), histogram-oriented gradients (HOG), and fractal analysis with box sizes from 2 to 512. Two-tailed unpaired Student's t test was used with statistical significance of p < 0.05. RESULTS PAS was associated with higher values for standard deviation of pixel intensity and fractal analysis at every box size. Fractal analysis at box sizes 256 (p = 0.011) and 32 (p = 0.021), and standard deviation of pixel intensity (p = 0.023) were the most statistically significant. Fractal values at box size 256 for PAS was 0.13 versus 0.090 for patients without PAS, while standard deviation of pixel intensity was 3.7 for PAS versus 2.5 for patients without PAS. No statistically significant association between PAS and GLCM, coefficient of variation, and HOG was found. CONCLUSION Statistically significant differences were found between normal and abnormal groups using standard deviation of pixel intensity and fractal analysis.
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Akakuru OU, Iqbal MZ, Saeed M, Liu C, Paunesku T, Woloschak G, Hosmane NS, Wu A. The Transition from Metal-Based to Metal-Free Contrast Agents for T1 Magnetic Resonance Imaging Enhancement. Bioconjug Chem 2019; 30:2264-2286. [PMID: 31380621 DOI: 10.1021/acs.bioconjchem.9b00499] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Magnetic resonance imaging (MRI) has received significant attention as the noninvasive diagnostic technique for complex diseases. Image-guided therapeutic strategy for diseases such as cancer has also been at the front line of biomedical research, thanks to the innovative MRI, enhanced by the prior delivery of contrast agents (CAs) into patients' bodies through injection. These CAs have contributed a great deal to the clinical utility of MRI but have been based on metal-containing compounds such as gadolinium, manganese, and iron oxide. Some of these CAs have led to cytotoxicities such as the incurable Nephrogenic Systemic Fibrosis (NSF), resulting in their removal from the market. On the other hand, CAs based on organic nitroxide radicals, by virtue of their structural composition, are metal free and without the aforementioned drawbacks. They also have improved biocompatibility, ease of functionalization, and long blood circulation times, and have been proven to offer tissue contrast enhancement with longitudinal relaxivities comparable with those for the metal-containing CAs. Thus, this Review highlights the recent progress in metal-based CAs and their shortcomings. In addition, the remarkable goals achieved by the organic nitroxide radical CAs in the enhancement of MR images have also been discussed extensively. The focal point of this Review is to emphasize or demonstrate the crucial need for transition into the use of organic nitroxide radicals-metal-free CAs-as against the metal-containing CAs, with the aim of achieving safer application of MRI for early disease diagnosis and image-guided therapy.
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Affiliation(s)
- Ozioma Udochukwu Akakuru
- Cixi Institute of Biomedical Engineering, CAS Key Laboratory of Magnetic Materials and Devices, & Key Laboratory of Additive Manufacturing Materials of Zhejiang Province , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201 , P.R. China.,University of Chinese Academy of Sciences , No. 19(A) Yuquan Road , Shijingshan District, Beijing 100049 , P.R. China
| | - M Zubair Iqbal
- Cixi Institute of Biomedical Engineering, CAS Key Laboratory of Magnetic Materials and Devices, & Key Laboratory of Additive Manufacturing Materials of Zhejiang Province , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201 , P.R. China.,Department of Materials Engineering, College of Materials and Textiles , Zhejiang Sci-Tech University , No. 2 Road of Xiasha , Hangzhou 310018 , P.R. China
| | - Madiha Saeed
- Cixi Institute of Biomedical Engineering, CAS Key Laboratory of Magnetic Materials and Devices, & Key Laboratory of Additive Manufacturing Materials of Zhejiang Province , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201 , P.R. China.,University of Chinese Academy of Sciences , No. 19(A) Yuquan Road , Shijingshan District, Beijing 100049 , P.R. China
| | - Chuang Liu
- Cixi Institute of Biomedical Engineering, CAS Key Laboratory of Magnetic Materials and Devices, & Key Laboratory of Additive Manufacturing Materials of Zhejiang Province , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201 , P.R. China.,University of Chinese Academy of Sciences , No. 19(A) Yuquan Road , Shijingshan District, Beijing 100049 , P.R. China
| | - Tatjana Paunesku
- Department of Radiation Oncology , Northwestern University , Chicago , Illinois 60611 , United States
| | - Gayle Woloschak
- Department of Radiation Oncology , Northwestern University , Chicago , Illinois 60611 , United States
| | - Narayan S Hosmane
- Department of Chemistry and Biochemistry , Northern Illinois University , DeKalb , Illinois 60115 , United States
| | - Aiguo Wu
- Cixi Institute of Biomedical Engineering, CAS Key Laboratory of Magnetic Materials and Devices, & Key Laboratory of Additive Manufacturing Materials of Zhejiang Province , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo 315201 , P.R. China
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Ta D, Khan M, Ishaque A, Seres P, Eurich D, Yang Y, Kalra S. Reliability of 3D texture analysis: A multicenter MRI study of the brain. J Magn Reson Imaging 2019; 51:1200-1209. [DOI: 10.1002/jmri.26904] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 08/04/2019] [Accepted: 08/06/2019] [Indexed: 12/14/2022] Open
Affiliation(s)
- Daniel Ta
- Neuroscience and Mental Health Institute, University of Alberta Edmonton Alberta Canada
| | - Muhammad Khan
- Neuroscience and Mental Health Institute, University of Alberta Edmonton Alberta Canada
- Department of Computing SciencesUniversity of Alberta Edmonton Alberta Canada
| | - Abdullah Ishaque
- Neuroscience and Mental Health Institute, University of Alberta Edmonton Alberta Canada
| | - Peter Seres
- Department of Biomedical EngineeringUniversity of Alberta Edmonton Alberta Canada
| | - Dean Eurich
- School of Public HealthUniversity of Alberta Edmonton Alberta Canada
| | - Yee‐Hong Yang
- Neuroscience and Mental Health Institute, University of Alberta Edmonton Alberta Canada
- Department of Computing SciencesUniversity of Alberta Edmonton Alberta Canada
| | - Sanjay Kalra
- Neuroscience and Mental Health Institute, University of Alberta Edmonton Alberta Canada
- Department of Biomedical EngineeringUniversity of Alberta Edmonton Alberta Canada
- Division of Neurology, Department of MedicineUniversity of Alberta Edmonton Alberta Canada
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26
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Korn RL, Rahmanuddin S, Borazanci E. Use of Precision Imaging in the Evaluation of Pancreas Cancer. Cancer Treat Res 2019; 178:209-236. [PMID: 31209847 DOI: 10.1007/978-3-030-16391-4_8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Pancreas cancer is an aggressive and fatal disease that will become one of the leading causes of cancer mortality by 2030. An all-out effort is underway to better understand the basic biologic mechanisms of this disease ranging from early development to metastatic disease. In order to change the course of this disease, diagnostic radiology imaging may play a vital role in providing a precise, noninvasive method for early diagnosis and assessment of treatment response. Recent progress in combining medical imaging, advanced image analysis and artificial intelligence, termed radiomics, can offer an innovate approach in detecting the earliest changes of tumor development as well as a rapid method for the detection of response. In this chapter, we introduce the principles of radiomics and demonstrate how it can provide additional information into tumor biology, early detection, and response assessments advancing the goals of precision imaging to deliver the right treatment to the right person at the right time.
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Affiliation(s)
- Ronald L Korn
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA. .,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA. .,Imaging Endpoints Core Lab, Scottsdale, AZ, USA.
| | | | - Erkut Borazanci
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA.,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA
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27
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Mahon RN, Hugo GD, Weiss E. Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome. Phys Med Biol 2019; 64:145007. [PMID: 30978707 DOI: 10.1088/1361-6560/ab18d3] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE To evaluate the repeatability of MRI and CT derived texture features and to investigate the feasibility of use in predictive single and multi-modality models for radiotherapy of non-small cell lung cancer.
Methods: Fifty-nine texture features were extracted from unfiltered and wavelet filtered images. Repeatability of test-retest features from helical 4D CT scans, true fast MRI with steady state precession (TRUFISP), and volumetric interpolation breath-hold examination (VIBE) was determined by the concordance correlation coefficient (CCC). A workflow was developed to predict overall survival at 12, 18, and 24 months and tumour response at end of treatment for tumour features, and normal muscle tissue features as a control. Texture features were reduced to repeatable and stable features before clustering. Cluster representative feature selection was performed by univariate or medoid analysis before model selection. P-values were corrected for false discovery rate.
Results: Repeatable (CCC ≥ 0.9) features were found for both tumour and normal muscle tissue: CT: 54.4% for tumour and 78.5% for normal tissue, TRUFISP: 64.4% for tumour and 67.8% for normal tissue, and VIBE: 52.6% for tumour and 72.9% for normal muscle tissue. Muscle tissue control analysis found 7 significant models with 6 of 7 models utilizing the univariate representative feature selection technique. Tumour analysis revealed 12 significant models for overall survival and none for tumour response at end of treatment. The accuracy of significant single modality was about the same for MR and CT. Multi-modality tumour models had comparable performance to single modality models.
Conclusion: MR derived texture features may add value to predictive models and should be investigated in a larger cohort. Control analysis demonstrated that the medoid representative feature selection method may result in more robust models.
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Affiliation(s)
- Rebecca Nichole Mahon
- Radiation Oncology, Virginia Commonwealth University Health System, Richmond, Virginia, UNITED STATES
| | - Geoffrey D Hugo
- Radiation Oncology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, UNITED STATES
| | - Elisabeth Weiss
- Radiation Oncology, Virginia Commonwealth University Health System, Richmond, Virginia, UNITED STATES
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Guo CG, Ren S, Chen X, Wang QD, Xiao WB, Zhang JF, Duan SF, Wang ZQ. Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance. Cancer Manag Res 2019; 11:1933-1944. [PMID: 30881119 PMCID: PMC6407516 DOI: 10.2147/cmar.s195376] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance. Patients and methods PNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher’s exact tests or Kruskal–Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades. Results There were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703–0.989. Conclusion Our data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.
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Affiliation(s)
- Chuan-Gen Guo
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Shuai Ren
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
| | - Xiao Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
| | - Qi-Dong Wang
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Wen-Bo Xiao
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | - Jing-Feng Zhang
- Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China
| | | | - Zhong-Qiu Wang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China,
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29
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Viswanath SE, Chirra PV, Yim MC, Rofsky NM, Purysko AS, Rosen MA, Bloch BN, Madabhushi A. Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. BMC Med Imaging 2019; 19:22. [PMID: 30819131 PMCID: PMC6396464 DOI: 10.1186/s12880-019-0308-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 01/10/2019] [Indexed: 11/10/2022] Open
Abstract
Background For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. Methods Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve. Results The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods. Conclusions Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.
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Affiliation(s)
- Satish E Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Prathyush V Chirra
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Michael C Yim
- College of Medicine, Northeast Ohio Medical University, Rootstown, OH, USA
| | - Neil M Rofsky
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Mark A Rosen
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - B Nicolas Bloch
- Department of Radiology, Boston University School of Medicine, Boston, MA, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
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30
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Selvaganesan K, Whitehead E, DeAlwis PM, Schindler MK, Inati S, Saad ZS, Ohayon JE, Cortese ICM, Smith B, Steven Jacobson, Nath A, Reich DS, Inati S, Nair G. Robust, atlas-free, automatic segmentation of brain MRI in health and disease. Heliyon 2019; 5:e01226. [PMID: 30828660 PMCID: PMC6383003 DOI: 10.1016/j.heliyon.2019.e01226] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 01/11/2019] [Accepted: 02/07/2019] [Indexed: 12/20/2022] Open
Abstract
Background Brain- and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlas-free brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center. Methods Proton-density, T2-weighted, T1-weighted, brain-free water, 3D FLAIR, 3D T2-weighted, and 3D T2*-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV + subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV + subjects (cohort two), and the results were compared with that of FreeSurfer and Lesion-TOADS. Finally, C-DEF segmentation was evaluated on subjects clinically diagnosed with various other neurological diseases (cohort three). Results C-DEF algorithm was optimized using leave-one-out cross validation on five healthy subjects (age 36 ± 11 years), and five subjects infected with HIV (age 57 ± 2.6 years) in cohort one. The optimized C-DEF algorithm outperformed FreeSurfer and Lesion-TOADS segmentation in 49 other subjects infected with HIV (cohort two, age 54 ± 6 years) in qualitative and quantitative comparisons. Although trained only on HIV brains, sensitivity to detect lesions using C-DEF increased by 45% in HTLV-I-associated myelopathy/tropical spastic paraparesis (n = 5; age 58 ± 7 years), 33% in multiple sclerosis (n = 5; 42 ± 9 years old), and 4% in subjects with polymorphism of the cytotoxic T-lymphocyte-associated protein 4 gene (n = 5; age 24 ± 12 years) compared to Lesion-TOADS. Conclusion C-DEF outperformed other segmentation algorithms in the various neurological diseases explored herein, especially in lesion segmentation. While the results reported are from routine images acquired at the NIH, the algorithm can be easily trained and optimized for any set of contrasts and protocols for wider application. We are currently exploring various technical aspects of optimal implementation of CDEF in a clinical setting and evaluating a larger cohort of patients with other neurological diseases. Improving the accuracy of brain segmentation methodology will help better understand the relationship of imaging abnormalities to clinical and neuropsychological markers in disease.
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Affiliation(s)
- Kartiga Selvaganesan
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Emily Whitehead
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Paba M DeAlwis
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Matthew K Schindler
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | | | - Ziad S Saad
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20893, USA
| | - Joan E Ohayon
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Irene C M Cortese
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Bryan Smith
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Steven Jacobson
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Avindra Nath
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Daniel S Reich
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Sara Inati
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
| | - Govind Nair
- National Institute of Neurological Disorders and Stroke (NINDS), Bethesda, MD, 20893, USA
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Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics 2019; 9:1303-1322. [PMID: 30867832 PMCID: PMC6401507 DOI: 10.7150/thno.30309] [Citation(s) in RCA: 461] [Impact Index Per Article: 92.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 01/10/2019] [Indexed: 12/14/2022] Open
Abstract
Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.
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Affiliation(s)
- Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Cheng Fang
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Xuezhi Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Kai Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Longfei Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
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Choi MH, Lee YJ, Yoon SB, Choi JI, Jung SE, Rha SE. MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome. Abdom Radiol (NY) 2019; 44:122-130. [PMID: 29980829 DOI: 10.1007/s00261-018-1681-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE To assess the association between T2-weighted imaging (T2WI) texture-analysis parameters and the pathological aggressiveness or long-term outcomes in pancreatic ductal adenocarcinoma (PDAC) patients. METHODS A total of 66 patients (mean age 65.3 ± 9.0 years) who underwent preoperative MRI followed by pancreatectomy for PDAC between 2013 and 2015 were included in this study. A radiologist performed a texture analysis twice on one axial image using commercial software. Differences in the tex parameters, according to pathological factors, were analyzed using a Student's t test or an ANOVA with Tukey's test. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the association between tex parameters and recurrence-free survival (RFS) or overall survival (OS). RESULTS The mean follow-up time was 18.5 months, and there were 58 recurrences and 39 deaths. The mean of the positive pixel (MPP)-related factors was significantly lower in poorly differentiated tumors than in well-differentiated tumors as well as in cases with perineural invasion. The univariate Cox proportional hazards analysis showed a significant association between the tex parameters and RFS or OS. However, only tumor size was statistically significant after the multivariate analysis. Only tumor size and entropy with medium texture were significantly associated with OS after the multivariate analysis. CONCLUSIONS Tumor size was a significant predictive factor for RFS and OS in PDAC patients. Although entropy with medium texture analysis was significantly associated with OS, there were also limitations in the texture analysis; thus, further study is necessary.
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Seow P, Wong JHD, Ahmad-Annuar A, Mahajan A, Abdullah NA, Ramli N. Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br J Radiol 2018; 91:20170930. [PMID: 29902076 PMCID: PMC6319852 DOI: 10.1259/bjr.20170930] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 05/25/2018] [Accepted: 06/07/2018] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE: The diversity of tumour characteristics among glioma patients, even within same tumour grade, is a big challenge for disease outcome prediction. A possible approach for improved radiological imaging could come from combining information obtained at the molecular level. This review assembles recent evidence highlighting the value of using radiogenomic biomarkers to infer the underlying biology of gliomas and its correlation with imaging features. METHODS: A literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles relevant to molecular biomarkers linked to imaging. RESULTS: Genes found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). A summary of the relationship amongst glioma morphology, gene expressions, imaging characteristics, prognosis and therapeutic response are presented. CONCLUSION: The use of radiogenomics can provide insights to understanding tumour biology and the underlying molecular pathways. Certain MRI characteristics that show strong correlations with EGFR, MGMT and IDH1 could be used as imaging biomarkers. Knowing the pathways involved in tumour progression and their associated imaging patterns may assist in diagnosis, prognosis and treatment management, while facilitating personalised medicine. ADVANCES IN KNOWLEDGE: Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.
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Affiliation(s)
| | | | - Azlina Ahmad-Annuar
- Department of Biomedical Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Abhishek Mahajan
- Department of Radiodiagnosis and Imaging, Tata Memorial Hospital, Mumbai, India
| | - Nor Aniza Abdullah
- Department of Computer System and Technology, University of Malaya, Kuala Lumpur, Malaysia
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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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Kwon D, Reis IM, Breto AL, Tschudi Y, Gautney N, Zavala-Romero O, Lopez C, Ford JC, Punnen S, Pollack A, Stoyanova R. Classification of suspicious lesions on prostate multiparametric MRI using machine learning. J Med Imaging (Bellingham) 2018; 5:034502. [PMID: 30840719 DOI: 10.1117/1.jmi.5.3.034502] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 08/06/2018] [Indexed: 01/09/2023] Open
Abstract
We present a radiomics-based approach developed for the SPIE-AAPM-NCI PROSTATEx challenge. The task was to classify clinically significant prostate cancer in multiparametric (mp) MRI. Data consisted of a "training dataset" (330 suspected lesions from 204 patients) and a "test dataset" (208 lesions/140 patients). All studies included T2-weighted (T2-W), proton density-weighted, dynamic contrast enhanced, and diffusion-weighted imaging. Analysis of the images was performed using the MIM imaging platform (MIM Software, Cleveland, Ohio). Prostate and peripheral zone contours were manually outlined on the T2-W images. A workflow for rigid fusion of the aforementioned images to T2-W was created in MIM. The suspicious lesion was outlined using the high b-value image. Intensity and texture features were extracted on four imaging modalities and characterized using nine histogram descriptors: 10%, 25%, 50%, 75%, 90%, mean, standard deviation, kurtosis, and skewness (216 features). Three classification methods were used: classification and regression trees (CART), random forests, and adaptive least absolute shrinkage and selection operator (LASSO). In the held out by the organizers test dataset, the areas under the curve (AUCs) were: 0.82 (random forests), 0.76 (CART), and 0.76 (adaptive LASSO). AUC of 0.82 was the fourth-highest score of 71 entries (32 teams) and the highest for feature-based methods.
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Affiliation(s)
- Deukwoo Kwon
- University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Biostatistics and Bioinformatics Shared Resource, Miami, Florida, United States
| | - Isildinha M Reis
- University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Biostatistics and Bioinformatics Shared Resource, Miami, Florida, United States.,University of Miami Miller School of Medicine, Department of Public Health Sciences, Miami, Florida, United States
| | - Adrian L Breto
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Yohann Tschudi
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Nicole Gautney
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Olmo Zavala-Romero
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Christopher Lopez
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - John C Ford
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Sanoj Punnen
- University of Miami Miller School of Medicine, Department of Urology, Miami, Florida, United States
| | - Alan Pollack
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Radka Stoyanova
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
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Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain. CONTRAST MEDIA & MOLECULAR IMAGING 2018; 2018:1729071. [PMID: 30154684 PMCID: PMC6091359 DOI: 10.1155/2018/1729071] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Revised: 04/13/2018] [Accepted: 06/25/2018] [Indexed: 02/07/2023]
Abstract
Objectives Radiomic features extracted from diverse MRI modalities have been investigated regarding their predictive and/or prognostic value in a variety of cancers. With the aid of a 3D realistic digital MRI phantom of the brain, the aim of this study was to examine the impact of pulse sequence parameter selection on MRI-based textural parameters of the brain. Methods MR images of the employed digital phantom were realized with SimuBloch, a simulation package made for fast generation of image sequences based on the Bloch equations. Pulse sequences being investigated consisted of spin echo (SE), gradient echo (GRE), spoiled gradient echo (SP-GRE), inversion recovery spin echo (IR-SE), and inversion recovery gradient echo (IR-GRE). Twenty-nine radiomic textural features related, respectively, to gray-level intensity histograms (GLIH), cooccurrence matrices (GLCOM), zone size matrices (GLZSM), and neighborhood difference matrices (GLNDM) were evaluated for the obtained MR realizations, and differences were identified. Results It was found that radiomic features vary considerably among images generated by the five different T1-weighted pulse sequences, and the deviations from those measured on the T1 map vary among features, from a few percent to over 100%. Radiomic features extracted from T1-weighted spin-echo images with TR varying from 360 ms to 620 ms and TE = 3.4 ms showed coefficients of variation (CV) up to 45%, while up to 70%, for T2-weighted spin-echo images with TE varying over the range 60-120 ms and TR = 6400 ms. Conclusion Variability of radiologic textural appearance on MR realizations with respect to the choice of pulse sequence and imaging parameters is feature-dependent and can be substantial. It calls for caution in employing MRI-derived radiomic features especially when pooling imaging data from multiple institutions with intention of correlating with clinical endpoints.
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Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Med Biol Eng Comput 2018; 56:2287-2300. [DOI: 10.1007/s11517-018-1858-4] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 05/27/2018] [Indexed: 12/19/2022]
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Yang F, Ford JC, Dogan N, Padgett KR, Breto AL, Abramowitz MC, Dal Pra A, Pollack A, Stoyanova R. Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol 2018; 7:445-458. [PMID: 30050803 PMCID: PMC6043736 DOI: 10.21037/tau.2018.06.05] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2018] [Accepted: 06/05/2018] [Indexed: 11/25/2022] Open
Abstract
In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. "Radiomics", as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.
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Affiliation(s)
- Fei Yang
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - John C. Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Kyle R. Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Adrian L. Breto
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Matthew C. Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
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Yang F, Dogan N, Stoyanova R, Ford JC. Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth. Phys Med 2018; 50:26-36. [PMID: 29891091 DOI: 10.1016/j.ejmp.2018.05.017] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 04/04/2018] [Accepted: 05/16/2018] [Indexed: 01/05/2023] Open
Abstract
The purpose of this study was to examine the dependence of image texture features on MR acquisition parameters and reconstruction using a digital MR imaging phantom. MR signal was simulated in a parallel imaging radiofrequency coil setting as well as a single element volume coil setting, with varying levels of acquisition noise, three acceleration factors, and four image reconstruction algorithms. Twenty-six texture features were measured on the simulated images, ground truth images, and clinical brain images. Subtle algorithm-dependent errors were observed on reconstructed phantom images, even in the absence of added noise. Sources of image error include Gibbs ringing at image edge gradients (tissue interfaces) and well-known artifacts due to high acceleration; two of the iterative reconstruction algorithms studied were able to mitigate these image errors. The difference of the texture features from ground truth, and their variance over reconstruction algorithm and parallel imaging acceleration factor, were compared to the clinical "effect size", i.e., the feature difference between high- and low-grade tumors on T1- and T2-weighted brain MR images of twenty glioma patients. The measured feature error (difference from ground truth) was small for some features, but substantial for others. The feature variance due to reconstruction algorithm and acceleration factor were generally smaller than the clinical effect size. Certain texture features may be preserved by MR imaging, but adequate precautions need to be taken regarding their validity and reliability. We present a general simulation framework for assessing the robustness and accuracy of radiomic textural features under various MR acquisition/reconstruction scenarios.
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Affiliation(s)
- Fei Yang
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, United States
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, United States
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, United States
| | - John Chetley Ford
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, United States.
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Wang S, Meng M, Zhang X, Wu C, Wang R, Wu J, Sami MU, Xu K. Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest. Oncol Lett 2018; 15:7297-7304. [PMID: 29731887 DOI: 10.3892/ol.2018.8232] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 02/23/2018] [Indexed: 12/21/2022] Open
Abstract
The present study aimed to explore the role of texture analysis with apparent diffusion coefficient (ADC) maps based on different regions of interest (ROI) in determining glioma grade. Thirty patients with glioma underwent diffusion-weighted imaging (DWI). ADC values were determined from the following three ROIs: i) whole tumor; ii) solid portion; and iii) peritumoral edema. Texture features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs) using the non-parametric Wilcoxon rank-sum test or the unpaired Student's t-test. Receiver operating characteristic (ROC) curves were constructed to determine the optimum threshold for inhomogeneity values in discrimination of HGGs from LGGs. With a spearman rank correlation model, the aforementioned ADC inhomogeneity values were correlated with the Ki-67 labeling index. With whole tumor ROI, inhomogeneity values proved to be significantly different between HGGs and LGGs (P<0.001). With solid portion ROI, inhomogeneity and median values showed significant difference between HGGs and LGGs (P=0.001 and P=0.043, respectively). With peritumoral edema ROI, entropy and edema volume demonstrated positive results (P=0.016, P<0.001). The whole tumor inhomogeneity parameter performed with better diagnostic accuracy (P=0.048) than selecting the solid portion ROI. The association between inhomogeneity and Ki-67 labeling index was significantly positive in whole tumor and solid portion ROI (R=0.628, P<0.001 and R=0.470, P=0.009). Texture analysis of DWI based on different ROI can provide various significant parameters to evaluate tumor heterogeneity, which were correlated with tumor grade. Particularly, the inhomogeneity value derived from whole tumor ROI provided high diagnostic value and predicting the status of tumor proliferation.
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Affiliation(s)
- Shan Wang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China.,Department of Radiology, Jiangsu Jiangyin People's Hospital, Jiangyin, Jiangsu 214400, P.R. China
| | - Meng Meng
- School of Medical Imaging, Guizhou Medical University, Guiyang, Guizhou 550004, P.R. China
| | - Xue Zhang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
| | - Chen Wu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
| | - Ru Wang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
| | - Jiangfen Wu
- GE Healthcare (Shanghai) Co., Ltd., Shanghai 201203, P.R. China
| | - Muhammad Umair Sami
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China
| | - Kai Xu
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu 221004, P.R. China.,Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, P.R. China
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Abstract
OBJECTIVES Analysis of a single slice of a tumor to extract biomarkers for texture analysis may result in loss of information. We investigated correlation of fractional volumes to entire tumor volumes and introduced expanded regions of interest (ROIs) outside the visual tumor borders in glioblastoma. MATERIALS AND METHODS Retrospective slice-by-slice volumetric texture analysis on 46 brain magnetic resonance imaging subjects with histologically confirmed glioblastoma was performed. Fractional volumes were analyzed for correlation to total volume. Expanded ROIs were analyzed for significant differences to conservative ROIs. RESULTS As fractional tumor volumes increased, correlation with total volume values for mean, SD, mean of positive pixels, skewness, and kurtosis increased. Expanding ROI by 2 mm resulted in significant differences in all textural values. CONCLUSIONS Fractional volumes may provide an optimal trade-off for texture analysis in the clinical setting. All texture parameters proved significantly different with minimal expansion of the ROI, underlining the susceptibility of texture analysis to generating misrepresentative tumor information.
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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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Thompson ES, Saveyn P, Declercq M, Meert J, Guida V, Eads CD, Robles ESJ, Britton MM. Characterisation of heterogeneity and spatial autocorrelation in phase separating mixtures using Moran's I. J Colloid Interface Sci 2017; 513:180-187. [PMID: 29153711 DOI: 10.1016/j.jcis.2017.10.115] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 10/30/2017] [Accepted: 10/31/2017] [Indexed: 11/29/2022]
Abstract
In complex colloidal systems, particle-poor regions can develop within particle-rich phases during sedimentation or creaming. These particle-poor regions are overlooked by 1D profiles, which are typically used to assess particle distributions in a sample. Alternative methods to visualise and quantify these regions are required to better understand phase separation, which is the focus of this paper. Magnetic resonance imaging has been used to monitor the development of compositional heterogeneity in a vesicle-polymer mixture undergoing creaming. T2 relaxation time maps were used to identify the distribution of vesicles, with vesicle-poor regions exhibiting higher T2 relaxation times than regions richer in vesicles. Phase separated structures displayed a range of different morphologies and a variety of image analysis methods, including first-order statistics, Fourier transformation, grey level co-occurrence matrices and Moran's I spatial autocorrelation, were used to characterise these structures, and quantify their heterogeneity. Of the image analysis techniques used, Moran's I was found to be the most effective at quantifying the degree and morphology of phase separation, providing a robust, quantitative measure by which comparisons can be made between a diverse range of systems undergoing phase separation. The sensitivity of Moran's I can be enhanced by the choice of weight matrices used.
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Affiliation(s)
- Emma S Thompson
- School of Chemistry, University of Birmingham, Birmingham B15 2TT, UK
| | - Pieter Saveyn
- Procter & Gamble Brussels Innovation Center, 1853 Strombeek Bever Temselaan 100, Belgium
| | - Marc Declercq
- Procter & Gamble Brussels Innovation Center, 1853 Strombeek Bever Temselaan 100, Belgium
| | - Joris Meert
- Procter & Gamble Brussels Innovation Center, 1853 Strombeek Bever Temselaan 100, Belgium
| | - Vincenzo Guida
- Procter & Gamble Brussels Innovation Center, 1853 Strombeek Bever Temselaan 100, Belgium
| | | | - Eric S J Robles
- Procter & Gamble Company, Newcastle Innovation Center, Newcastle-Upon-Tyne NE12 9TS, UK
| | - Melanie M Britton
- School of Chemistry, University of Birmingham, Birmingham B15 2TT, UK.
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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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Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, Li B. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging 2017; 17:42. [PMID: 28705145 PMCID: PMC5508617 DOI: 10.1186/s12880-017-0212-x] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 06/19/2017] [Indexed: 12/14/2022] Open
Abstract
Background To assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic metastases (HM) and hepatocellular carcinoma (HCC). Methods The SPAIR T2W-MRI data of 162 patients with HH (n=55), HM (n=67) and HCC (n=40) were retrospectively analyzed. We used two independent cohorts for training (n = 112 patients) and validation (n = 50 patients). The TA was performed and textual parameters derived from the gray level co-occurrence matrix (GLCM), gray level gradient co-occurrence matrix (GLGCM), gray-level run-length matrix (GLRLM), Gabor wavelet transform (GWTF), intensity-size-zone matrix (ISZM), and histogram features were calculated. The capacity of each parameter to classify three types of single liver lesions was assessed using the Kruskal-Wallis test. Specificity and sensitivity for each of the studied parameters were derived using ROC curves. Four supervised classification algorithms were trained with the most influential textural features in the classification of tumor types. The test datasets validated the reliability of the models. Results The texture analyses showed that the HH versus HM, HM versus HCC, and HH versus HCC could be differentiated by 9, 16 and 10 feature parameters, respectively. The model’s misclassification rates were 11.7, 9.6 and 9.7% respectively. No texture feature was able to adequately distinguish among the three types of single liver lesions at the same time. The BP-ANN model had better predictive ability. Conclusion Texture features of SPAIR T2W-MRI can classify the three types of single liver lesions (HH, HM and HCC) and may serve as an adjunct tool for accurate diagnosis of these diseases. Electronic supplementary material The online version of this article (doi:10.1186/s12880-017-0212-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhenjiang Li
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yu Mao
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Wei Huang
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Hongsheng Li
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Jian Zhu
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Wanhu Li
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Baosheng Li
- Shandong Cancer Hospital affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, Shandong, China.
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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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Hsieh KLC, Lo CM, Hsiao CJ. Computer-aided grading of gliomas based on local and global MRI features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 139:31-38. [PMID: 28187893 DOI: 10.1016/j.cmpb.2016.10.021] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 09/11/2016] [Accepted: 10/18/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVES A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors. METHODS The acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model. RESULTS Performances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy (p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement κ = 0.698, p < 0.001. CONCLUSIONS Numerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.
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Affiliation(s)
- Kevin Li-Chun Hsieh
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan; Translational Imaging Research Center, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
| | - Chih-Jou Hsiao
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
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Kammoun M, Meme S, Meme W, Subramaniam M, Hawse JR, Canon F, Bensamoun SF. Impact of TIEG1 on the structural properties of fast- and slow-twitch skeletal muscle. Muscle Nerve 2016; 55:410-416. [PMID: 27421714 DOI: 10.1002/mus.25252] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2016] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Transforming growth factor-beta (TGF-β)-inducible early gene-1 (TIEG1) is a transcription factor that is highly expressed in skeletal muscle. The purpose of this study was to characterize the structural properties of both fast-twitch (EDL) and slow-twitch (soleus) muscles in the hindlimb of TIEG1-deficient (TIEG1-/- ) mice. METHODS Ten slow and 10 fast muscles were analyzed from TIEG1-/- and wild-type (WT) mice using MRI texture (MRI-TA) and histological analyses. RESULTS MRI-TA could discriminate between WT slow and fast muscles. Deletion of the TIEG1 gene led to changes in the texture profile within both muscle types. Specifically, muscle isolated from TIEG1-/- mice displayed hypertrophy, hyperplasia, and a modification of fiber area distribution. CONCLUSIONS We demonstrated that TIEG1 plays an important role in the structural properties of skeletal muscle. This study further implicates important roles for TIEG1 in the development of skeletal muscle and suggests that defects in TIEG1 expression and/or function may be associated with muscle disease. Muscle Nerve 55: 410-416, 2017.
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Affiliation(s)
- Malek Kammoun
- Université de Technologie de Compiègne, Centre de Recherches de Royallieu, Laboratoire de Biomécanique et de BioIngénierie, UMR CNRS 7338, BP 20529, 60205, Compiègne Cedex, France
| | - Sandra Meme
- Centre de Biophysique Moléculaire, CNRS UPR4301, Orléans, France
| | - William Meme
- Centre de Biophysique Moléculaire, CNRS UPR4301, Orléans, France
| | - Malayannan Subramaniam
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, USA
| | - John R Hawse
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, USA
| | - Francis Canon
- Université de Technologie de Compiègne, Centre de Recherches de Royallieu, Laboratoire de Biomécanique et de BioIngénierie, UMR CNRS 7338, BP 20529, 60205, Compiègne Cedex, France
| | - Sabine F Bensamoun
- Université de Technologie de Compiègne, Centre de Recherches de Royallieu, Laboratoire de Biomécanique et de BioIngénierie, UMR CNRS 7338, BP 20529, 60205, Compiègne Cedex, France
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Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data. PLoS One 2016; 11:e0159088. [PMID: 27421116 PMCID: PMC4946789 DOI: 10.1371/journal.pone.0159088] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 06/27/2016] [Indexed: 11/19/2022] Open
Abstract
Medical diagnostics is often a multi-attribute problem, necessitating sophisticated tools for analyzing high-dimensional biomedical data. Mining this data often results in two crucial bottlenecks: 1) high dimensionality of features used to represent rich biological data and 2) small amounts of labelled training data due to the expense of consulting highly specific medical expertise necessary to assess each study. Currently, no approach that we are aware of has attempted to use active learning in the context of dimensionality reduction approaches for improving the construction of low dimensional representations. We present our novel methodology, AdDReSS (Adaptive Dimensionality Reduction with Semi-Supervision), to demonstrate that fewer labeled instances identified via AL in embedding space are needed for creating a more discriminative embedding representation compared to randomly selected instances. We tested our methodology on a wide variety of domains ranging from prostate gene expression, ovarian proteomic spectra, brain magnetic resonance imaging, and breast histopathology. Across these various high dimensional biomedical datasets with 100+ observations each and all parameters considered, the median classification accuracy across all experiments showed AdDReSS (88.7%) to outperform SSAGE, a SSDR method using random sampling (85.5%), and Graph Embedding (81.5%). Furthermore, we found that embeddings generated via AdDReSS achieved a mean 35.95% improvement in Raghavan efficiency, a measure of learning rate, over SSAGE. Our results demonstrate the value of AdDReSS to provide low dimensional representations of high dimensional biomedical data while achieving higher classification rates with fewer labelled examples as compared to without active learning.
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