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Luccas R, Riguetto CM, Alves M, Zantut-Wittmann DE, Reis F. Computed tomography and magnetic resonance imaging approaches to Graves' ophthalmopathy: a narrative review. Front Endocrinol (Lausanne) 2024; 14:1277961. [PMID: 38260158 PMCID: PMC10801040 DOI: 10.3389/fendo.2023.1277961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Graves' ophthalmopathy (GO) affects up to 50% of patients with Graves' disease (GD) ranging from mild ocular irritation to vision loss. The initial diagnosis is based on clinical findings and laboratory tests. Orbital imaging, such as magnetic resonance imaging (MRI) and computed tomography (CT), is an important tool to assess orbital changes, being also useful for understanding disease progression and surgical planning. In this narrative review, we included 92 studies published from 1979 to 2020 that used either MRI and/or CT to diagnose and investigate GO, proposing new methods and techniques. Most of the methods used still need to be corroborated and validated, and, despite the different methods and approaches for thyroid eye disease (TED) evaluation, there is still a lack of standardization of measurements and outcome reports; therefore, additional studies should be performed to include these methods in clinical practice, facilitating the diagnosis and approach for the treatment of TED.
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Affiliation(s)
- Rafael Luccas
- Graduate Program of Neuroscience, Faculty of Medical Sciences, State University of Campinas, Campinas, Brazil
- Department of Anesthesiology, Oncology and Radiology, Faculty of Medical Sciences, State University of Campinas, Campinas, Brazil
| | - Cinthia Minatel Riguetto
- Division of Endocrinology, Faculty of Medical Sciences, State University of Campinas, Campinas, Brazil
- Waikato Regional Diabetes Service, Te Whatu Ora Health New Zealand, Hamilton, New Zealand
| | - Monica Alves
- Department of Ophthalmology and Otorhinolaryngology, Faculty of Medical Sciences, State University of Campinas, Campinas, Brazil
| | | | - Fabiano Reis
- Department of Anesthesiology, Oncology and Radiology, Faculty of Medical Sciences, State University of Campinas, Campinas, Brazil
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Assessment of Orbital Computed Tomography (CT) Imaging Biomarkers in Patients with Thyroid Eye Disease. J Digit Imaging 2021; 32:987-994. [PMID: 31197558 DOI: 10.1007/s10278-019-00195-2] [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] [Indexed: 10/26/2022] Open
Abstract
To understand potential orbital biomarkers generated from computed tomography (CT) imaging in patients with thyroid eye disease. This is a retrospective cohort study. From a database of an ongoing thyroid eye disease research study at our institution, we identified 85 subjects who had both clinical examination and laboratory records supporting the diagnosis of thyroid eye disease and concurrent imaging prior to any medical or surgical intervention. Patients were excluded if imaging quality or type was not amenable to segmentation. The images of 170 orbits were analyzed with the developed automated segmentation tool. The main outcome measure was to cross 25 CT structural metrics for each eye with nine clinical markers using a Kendall rank correlation test to identify significant relationships. The Kendall rank correlation test between automatically calculated CT metrics and clinical data demonstrated numerous correlations. Extraocular rectus muscle metrics, such as the average diameter of the superior, medial, and lateral rectus muscles, showed a strong correlation (p < 0.05) with loss of visual acuity and presence of ocular motility defects. Hertel measurements demonstrated a strong correlation (p < 0.05) with volumetric measurements of the optic nerve and other orbital metrics such as the crowding index and proptosis. Optic neuropathy was strongly correlated (p < 0.05) with an increase in the maximum diameter of the superior muscle. This novel method of automated imaging metrics may provide objective, rapid clinical information. This data may be useful for appreciation of severity of thyroid eye disease and recognition of risk factors of visual impairment from dysthyroid optic neuropathy from CT imaging.
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Chaganti S, Mawn LA, Kang H, Egan J, Resnick SM, Beason-Held LL, Landman BA, Lasko TA. Electronic Medical Record Context Signatures Improve Diagnostic Classification Using Medical Image Computing. IEEE J Biomed Health Inform 2018; 23:2052-2062. [PMID: 30602428 DOI: 10.1109/jbhi.2018.2890084] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Composite models that combine medical imaging with electronic medical records (EMR) improve predictive power when compared to traditional models that use imaging alone. The digitization of EMR provides potential access to a wealth of medical information, but presents new challenges in algorithm design and inference. Previous studies, such as Phenome Wide Association Study (PheWAS), have shown that EMR data can be used to investigate the relationship between genotypes and clinical conditions. Here, we introduce Phenome-Disease Association Study to extend the statistical capabilities of the PheWAS software through a custom Python package, which creates diagnostic EMR signatures to capture system-wide co-morbidities for a disease population within a given time interval. We investigate the effect of integrating these EMR signatures with radiological data to improve diagnostic classification in disease domains known to have confounding factors because of variable and complex clinical presentation. Specifically, we focus on two studies: First, a study of four major optic nerve related conditions; and second, a study of diabetes. Addition of EMR signature vectors to radiologically derived structural metrics improves the area under the curve (AUC) for diagnostic classification using elastic net regression, for diseases of the optic nerve. For glaucoma, the AUC improves from 0.71 to 0.83, for intrinsic optic nerve disease it increases from 0.72 to 0.91, for optic nerve edema it increases from 0.95 to 0.96, and for thyroid eye disease from 0.79 to 0.89. The EMR signatures recapitulate known comorbidities with diabetes, such as abnormal glucose, but do not significantly modulate image-derived features. In summary, EMR signatures present a scalable and readily applicable.
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Chaganti S, Nelson K, Mundy K, Harrigan R, Galloway R, Mawn LA, Landman B. Imaging biomarkers in thyroid eye disease and their clinical associations. J Med Imaging (Bellingham) 2018; 5:044001. [PMID: 30345325 PMCID: PMC6191037 DOI: 10.1117/1.jmi.5.4.044001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 08/23/2018] [Indexed: 04/14/2024] Open
Abstract
The purpose of this study is to understand the phenotypes of thyroid eye disease (TED) through data derived from a multiatlas segmentation of computed tomography (CT) imaging. Images of 170 orbits of 85 retrospectively selected TED patients were analyzed with the developed automated segmentation tool. Twenty-five bilateral orbital structural metrics were used to perform principal component analysis (PCA). PCA of the 25 structural metrics identified the two most dominant structural phenotypes or characteristics, the "big volume phenotype" and the "stretched optic nerve phenotype," that accounted for 60% of the variance. Most of the subjects in the study have either of these characteristics or a combination of both. A Kendall rank correlation between the principal components (phenotypes) and clinical data showed that the big volume phenotype was very strongly correlated ( p - value < 0.05 ) with motility defects, and loss of visual acuity. Whereas, the stretched optic nerve phenotype was strongly correlated ( p - value < 0.05 ) with an increased Hertel measurement, relatively better visual acuity, and smoking. Two clinical subtypes of TED, type 1 with enlarged muscles and type 2 with proptosis, are recognizable in CT imaging. Our automated algorithm identifies the phenotypes and finds associations with clinical markers.
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Affiliation(s)
- Shikha Chaganti
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Katrina Nelson
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
| | - Kevin Mundy
- Vanderbilt University, School of Medicine, Vanderbilt Eye Institute, Nashville, Tennessee, United States
| | - Robert Harrigan
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
| | - Robert Galloway
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Louise A. Mawn
- Vanderbilt University, School of Medicine, Vanderbilt Eye Institute, Nashville, Tennessee, United States
| | - Bennett Landman
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
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Chaganti S, Landman BA. QUADRATIC: Quality of Dice in Registration Circuits. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2018; 10574. [PMID: 29887660 DOI: 10.1117/12.2293642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Image registration involves identification of a transformation to fit a target image to a reference image space. The success of the registration process is vital for correct interpretation of the results of many medical image-processing applications, including multi-atlas segmentation. While there are several validation metrics employed in rigid registration to examine the accuracy of the method, non-rigid registrations (NRR) are validated subjectively in most cases, validated in offline cases, or based on image similarity metrics, all of which have been shown to poorly correlate with true registration quality. In this paper, we model the error for each target scan by expanding on the idea of Assessing Quality Using Image Registration Circuits (AQUIRC), which created a model for error "quality" associated with NRR. In this paper, we model the Dice similarity coefficient (DSC) error in the network, for a more interpretable measure. We test four functional models using a leave-one-out strategy to evaluate the relationship between edge DSC and circuit DSC: linear, quadratic, third order, or multiplicative models. We found that the quadratic model most accurately learns the NRR-DSC, with a median correlation coefficient of 0.58 with the true NRR-DSC, we call this the QUADRATIC (QUAlity of Dice in RegistrATIon Circuits) model. The QUADRATIC model is used for multi-atlas segmentation based on majority vote. Choosing the four best atlases predicted from the QUDRATIC model resulted in a 7% increase in the DSC between segmented image and true labels.
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Affiliation(s)
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University.,Department of Electrical Engineering, Vanderbilt University
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EMR-Radiological Phenotypes in Diseases of the Optic Nerve and their Association with Visual Function. ACTA ACUST UNITED AC 2017; 2017:373-381. [PMID: 29392245 DOI: 10.1007/978-3-319-67558-9_43] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Multi-modal analyses of diseases of the optic nerve, that combine radiological imaging with other electronic medical records (EMR), improve understanding of visual function. We conducted a study of 55 patients with glaucoma and 32 patients with thyroid eye disease (TED). We collected their visual assessments, orbital CT imaging, and EMR data. We developed an image-processing pipeline that segmented and extracted structural metrics from CT images. We derive EMR phenotype vectors with the help of PheWAS (from diagnostic codes) and ProWAS (from treatment codes). Next, we performed a principal component analysis and multiple-correspondence analysis to identify their association with visual function scores. We find that structural metrics derived from CT imaging are significantly associated with functional visual score for both glaucoma (R2=0.32) and TED (R2=0.4). Addition of EMR phenotype vectors to the model significantly improved (p<1E-04) the R2 to 0.4 for glaucoma and 0.54 for TED.
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Chaganti S, Nabar KP, Nelson KM, Mawn LA, Landman BA. Phenotype Analysis of Early Risk Factors from Electronic Medical Records Improves Image-Derived Diagnostic Classifiers for Optic Nerve Pathology. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10138. [PMID: 28736474 DOI: 10.1117/12.2254618] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
We examine imaging and electronic medical records (EMR) of 588 subjects over five major disease groups that affect optic nerve function. An objective evaluation of the role of imaging and EMR data in diagnosis of these conditions would improve understanding of these diseases and help in early intervention. We developed an automated image-processing pipeline that identifies the orbital structures within the human eyes from computed tomography (CT) scans, calculates structural size, and performs volume measurements. We customized the EMR-based phenome-wide association study (PheWAS) to derive diagnostic EMR phenotypes that occur at least two years prior to the onset of the conditions of interest from a separate cohort of 28,411 ophthalmology patients. We used random forest classifiers to evaluate the predictive power of image-derived markers, EMR phenotypes, and clinical visual assessments in identifying disease cohorts from a control group of 763 patients without optic nerve disease. Image-derived markers showed more predictive power than clinical visual assessments or EMR phenotypes. However, the addition of EMR phenotypes to the imaging markers improves the classification accuracy against controls: the AUC improved from 0.67 to 0.88 for glaucoma, 0.73 to 0.78 for intrinsic optic nerve disease, 0.72 to 0.76 for optic nerve edema, 0.72 to 0.77 for orbital inflammation, and 0.81 to 0.85 for thyroid eye disease. This study illustrates the importance of diagnostic context for interpretation of image-derived markers and the proposed PheWAS technique provides a flexible approach for learning salient features of patient history and incorporating these data into traditional machine learning analyses.
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Affiliation(s)
- Shikha Chaganti
- Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Kunal P Nabar
- Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Katrina M Nelson
- Electrical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Louise A Mawn
- Vanderbilt Eye Institute, Vanderbilt University School of Medicine, 2311 Pierce Avenue, Nashville, TN USA 37232
| | - Bennett A Landman
- Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235.,Electrical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
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Yao X, Chaganti S, Nabar KP, Nelson K, Plassard A, Harrigan RL, Mawn LA, Landman BA. Structural-Functional Relationships Between Eye Orbital Imaging Biomarkers and Clinical Visual Assessments. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10133. [PMID: 28736470 DOI: 10.1117/12.2254613] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Eye diseases and visual impairment affect millions of Americans and induce billions of dollars in annual economic burdens. Expounding upon existing knowledge of eye diseases could lead to improved treatment and disease prevention. This research investigated the relationship between structural metrics of the eye orbit and visual function measurements in a cohort of 470 patients from a retrospective study of ophthalmology records for patients (with thyroid eye disease, orbital inflammation, optic nerve edema, glaucoma, intrinsic optic nerve disease), clinical imaging, and visual function assessments. Orbital magnetic resonance imaging (MRI) and computed tomography (CT) images were retrieved and labeled in 3D using multi-atlas label fusion. Based on the 3D structures, both traditional radiology measures (e.g., Barrett index, volumetric crowding index, optic nerve length) and novel volumetric metrics were computed. Using stepwise regression, the associations between structural metrics and visual field scores (visual acuity, functional acuity, visual field, functional field, and functional vision) were assessed. Across all models, the explained variance was reasonable (R2 ~ 0.1-0.2) but highly significant (p < 0.001). Instead of analyzing a specific pathology, this study aimed to analyze data across a variety of pathologies. This approach yielded a general model for the connection between orbital structural imaging biomarkers and visual function.
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Affiliation(s)
- Xiuya Yao
- Department of Biomedical Informatics, Vanderbilt University, 2525 West End Ave #1475, Nashville, TN 37203
| | - Shikha Chaganti
- Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Kunal P Nabar
- Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Katrina Nelson
- Electrical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Andrew Plassard
- Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Rob L Harrigan
- Electrical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Louise A Mawn
- Vanderbilt Eye Institute, Vanderbilt University School of Medicine, 2311 Pierce Avenue, Nashville, TN USA 37232
| | - Bennett A Landman
- Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235.,Electrical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
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