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Kar SS, Cetin H, Abraham J, Srivastava SK, Madabhushi A, Ehlers JP. Combination of optical coherence tomography-derived shape and texture features are associated with development of sub-foveal geographic atrophy in dry AMD. Sci Rep 2024; 14:17602. [PMID: 39080402 PMCID: PMC11289404 DOI: 10.1038/s41598-024-68259-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
Geographic atrophy (GA) is an advanced form of dry age-related macular degeneration (AMD) that leads to progressive and irreversible vision loss. Identifying patients with greatest risk of GA progression is important for targeted utilization of emerging therapies. This study aimed to comprehensively evaluate the role of shape-based fractal dimension features ( F fd ) of sub-retinal pigment epithelium (sub-RPE) compartment and texture-based radiomics features ( F t ) of Ellipsoid Zone (EZ)-RPE and sub-RPE compartments for risk stratification for subfoveal GA (sfGA) progression. This was a retrospective study of 137 dry AMD subjects with a 5-year follow-up. Based on sfGA status at year 5, eyes were categorized as Progressors and Non-progressors. A total of 15 shape-based F fd of sub-RPE surface and 494 F t from each of sub-RPE and EZ-RPE compartments were extracted from baseline spectral domain-optical coherence tomography scans. The top nine features were identified from F fd and F t feature pool separately using minimum Redundancy maximum Relevance feature selection and used to train a Random Forest (RF) classifier independently using three-fold cross validation on the training set ( S t , N = 90) to distinguish between sfGA Progressors and Non-progressors. Combined F fd and F t was also evaluated in predicting risk of sfGA progression. The RF classifier yielded AUC of 0.85, 0.79 and 0.89 on independent test set ( S v , N = 47) using F fd , F t , and their combination, respectively. Using combined F fd and F t , the improvement in AUC was statistically significant on S v with p-values of 0.032 and 0.04 compared to using only F fd and only F t , respectively. Combined F fd and F t appears to identify high-risk patients. Our results show that FD and texture features could be potentially used for predicting risk of sfGA progression and future therapeutic response.
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Grants
- R43EB028736 NIBIB NIH HHS
- R01CA208236 NCI NIH HHS
- U01 CA239055 NCI NIH HHS
- R01 HL158071 NHLBI NIH HHS
- R01 HL151277 NHLBI NIH HHS
- IP30EY025585 NIH-NEI P30 Core Gran
- R01HL151277 National Heart, Lung and Blood Institute
- R01CA202752 NCI NIH HHS
- R01 CA216579 NCI NIH HHS
- R01 CA268207 NCI NIH HHS
- IP30EY025585 Unrestricted Grants from The Research to Prevent Blindness, Inc (Cole Eye Institute), Cleveland Eye Bank Foundation awarded to the Cole Eye Institute (Cole Eye)
- R01 CA208236 NCI NIH HHS
- R01CA216579 NCI NIH HHS
- R01 CA202752 NCI NIH HHS
- VA Merit Review Award IBX004121A United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service
- C06 RR012463 NCRR NIH HHS
- U01CA248226 NCI NIH HHS
- P30 EY025585 NEI NIH HHS
- C06 RR12463-01 NCRR NIH HHS
- R01CA268207A1 NCI NIH HHS
- U01 CA248226 NCI NIH HHS
- I01 BX004121 BLRD VA
- R43 EB028736 NIBIB NIH HHS
- R01HL158071 National Heart, Lung and Blood Institute
- R01 CA257612 NCI NIH HHS
- Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345), the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehri Office of the Assistant Secretary of Defense for Health Affairs
- U54 CA254566 NCI NIH HHS
- R01CA220581 NCI NIH HHS
- U54CA254566 NCI NIH HHS
- U01CA239055 NCI NIH HHS
- R01CA257612 NCI NIH HHS
- R01CA249992 NCI NIH HHS
- R01 CA249992 NCI NIH HHS
- R01 CA220581 NCI NIH HHS
- K23 EY022947 NEI NIH HHS
- National Cancer Institute
- National Institute of Biomedical Imaging and Bioengineering
- National Center for Research Resources
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Affiliation(s)
- Sudeshna Sil Kar
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
| | - Hasan Cetin
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, 9500 Euclid Avenue/Desk i32, Cleveland, OH, 44195, USA
| | - Joseph Abraham
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, 9500 Euclid Avenue/Desk i32, Cleveland, OH, 44195, USA
| | - Sunil K Srivastava
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, 9500 Euclid Avenue/Desk i32, Cleveland, OH, 44195, USA
- Vitreoretinal Service, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA.
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
- Wallace H Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1760 Haygood Drive, Suite W212, Atlanta, GA, 30322, USA.
| | - Justis P Ehlers
- The Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, 9500 Euclid Avenue/Desk i32, Cleveland, OH, 44195, USA.
- Vitreoretinal Service, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA.
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Kar SS, Cetin H, Abraham J, Srivastava SK, Whitney J, Madabhushi A, Ehlers JP. Novel Fractal-Based Sub-RPE Compartment OCT Radiomics Biomarkers Are Associated With Subfoveal Geographic Atrophy in Dry AMD. IEEE Trans Biomed Eng 2023; 70:2914-2921. [PMID: 37097804 PMCID: PMC10581743 DOI: 10.1109/tbme.2023.3270201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
OBJECTIVE The purpose of this study was to quantitatively characterize the shape of the sub-retinal pigment epithelium (sub-RPE, i.e., space bounded by RPE and Bruch's membrane) compartment on SD-OCT using fractal dimension (FD) features and evaluate their impact on risk of subfoveal geographic atrophy (sfGA) progression. METHODS This was an IRB-approved retrospective study of 137 subjects with dry age-related macular degeneration (AMD) with subfoveal GA. Based on sfGA status at year five, eyes were categorized as "Progressors" and "Non-progressors". FD analysis allows quantification of the degree of shape complexity and architectural disorder associated with a structure. To characterize the structural irregularities along the sub-RPE surface between the two groups of patients, a total of 15 shape descriptors of FD were extracted from the sub-RPE compartment of baseline OCT scans. The top four features were identified using minimum Redundancy maximum Relevance (mRmR) feature selection method and evaluated with Random Forest (RF) classifier using three-fold cross validation from the training set (N = 90). Classifier performance was subsequently validated on the independent test set (N = 47). RESULTS Using the top four FD features, a RF classifier yielded an AUC of 0.85 on the independent test set. Mean fractal entropy (p-value = 4.8e-05) was identified as the most significant biomarker; higher values of entropy being associated with greater shape disorder and risk for sfGA progression. CONCLUSIONS FD assessment holds promise for identifying high-risk eyes for GA progression. SIGNIFICANCE With further validation, FD features could be potentially used for clinical trial enrichment and assessments for therapeutic response in dry AMD patients.
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Okada T, Fujimoto K, Fushimi Y, Akasaka T, Thuy DHD, Shima A, Sawamoto N, Oishi N, Zhang Z, Funaki T, Nakamoto Y, Murai T, Miyamoto S, Takahashi R, Isa T. Neuroimaging at 7 Tesla: a pictorial narrative review. Quant Imaging Med Surg 2022; 12:3406-3435. [PMID: 35655840 PMCID: PMC9131333 DOI: 10.21037/qims-21-969] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/05/2022] [Indexed: 01/26/2024]
Abstract
Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.
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Affiliation(s)
- Tomohisa Okada
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koji Fujimoto
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Thai Akasaka
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Dinh H. D. Thuy
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Atsushi Shima
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nobukatsu Sawamoto
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Medial Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Zhilin Zhang
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takeshi Funaki
- Department of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Susumu Miyamoto
- Department of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tadashi Isa
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Orlov NV, Makrogiannis S, Ferrucci L, Goldberg IG. Differential Aging Signals in Abdominal CT Scans. Acad Radiol 2017; 24:1535-1543. [PMID: 28927581 DOI: 10.1016/j.acra.2017.07.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 05/30/2017] [Accepted: 07/10/2017] [Indexed: 11/25/2022]
Abstract
RATIONALE AND OBJECTIVES Changes in the composition of body tissues are major aging phenotypes, but they have been difficult to study in depth. Here we describe age-related change in abdominal tissues observable in computed tomography (CT) scans. We used pattern recognition and machine learning to detect and quantify these changes in a model-agnostic fashion. MATERIALS AND METHODS CT scans of abdominal L4 sections were obtained from Baltimore Longitudinal Study of Aging (BLSA) participants. Age-related change in the constituent tissues were determined by training machine classifiers to differentiate age groups within male and female strata ("Younger" at 50-70 years old vs "Older" at 80-99 years old). The accuracy achieved by the classifiers in differentiating the age cohorts was used as a surrogate measure of the aging signal in the different tissues. RESULTS The highest accuracy for discriminating age differences was 0.76 and 0.72 for males and females, respectively. The classification accuracy was 0.79 and 0.71 for adipose tissue, 0.70 and 0.68 for soft tissue, and 0.65 and 0.64 for bone. CONCLUSIONS Using image data from a large sample of well-characterized pool of participants dispersed over a wide age range, we explored age-related differences in gross morphology and texture of abdominal tissues. This technology is advantageous for tracking effects of biological aging and predicting adverse outcomes when compared to the traditional use of specific molecular biomarkers. Application of pattern recognition and machine learning as a tool for analyzing medical images may provide much needed insight into tissue changes occurring with aging and, further, connect these changes with their metabolic and functional consequences.
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Captur G, Karperien AL, Li C, Zemrak F, Tobon-Gomez C, Gao X, Bluemke DA, Elliott PM, Petersen SE, Moon JC. Fractal frontiers in cardiovascular magnetic resonance: towards clinical implementation. J Cardiovasc Magn Reson 2015; 17:80. [PMID: 26346700 PMCID: PMC4562373 DOI: 10.1186/s12968-015-0179-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 08/05/2015] [Indexed: 11/26/2022] Open
Abstract
Many of the structures and parameters that are detected, measured and reported in cardiovascular magnetic resonance (CMR) have at least some properties that are fractal, meaning complex and self-similar at different scales. To date however, there has been little use of fractal geometry in CMR; by comparison, many more applications of fractal analysis have been published in MR imaging of the brain.This review explains the fundamental principles of fractal geometry, places the fractal dimension into a meaningful context within the realms of Euclidean and topological space, and defines its role in digital image processing. It summarises the basic mathematics, highlights strengths and potential limitations of its application to biomedical imaging, shows key current examples and suggests a simple route for its successful clinical implementation by the CMR community.By simplifying some of the more abstract concepts of deterministic fractals, this review invites CMR scientists (clinicians, technologists, physicists) to experiment with fractal analysis as a means of developing the next generation of intelligent quantitative cardiac imaging tools.
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Affiliation(s)
- Gabriella Captur
- UCL Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK.
- Division of Cardiovascular Imaging, The Heart Hospital, part of University College London NHS Foundation Trust, 16-18 Westmoreland Street, London, W1G 8PH, UK.
| | - Audrey L Karperien
- Centre for Research in Complex Systems, School of Community Health, Charles Sturt University, Albury, NSW 2640, Australia.
| | - Chunming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Filip Zemrak
- Division of Cardiovascular Imaging, The Heart Hospital, part of University College London NHS Foundation Trust, 16-18 Westmoreland Street, London, W1G 8PH, UK.
- Cardiovascular Biomedical Research Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Catalina Tobon-Gomez
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London, UK.
| | - Xuexin Gao
- Circle Cardiovascular Imaging Inc., Panarctic Plaza, Suite 250, 815 8th Avenue SW, Calgary, AB T2P 3P2, Canada.
| | - David A Bluemke
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Center Drive, Bethesda, MA, USA.
| | - Perry M Elliott
- UCL Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK.
- Division of Cardiovascular Imaging, The Heart Hospital, part of University College London NHS Foundation Trust, 16-18 Westmoreland Street, London, W1G 8PH, UK.
| | - Steffen E Petersen
- Division of Cardiovascular Imaging, The Heart Hospital, part of University College London NHS Foundation Trust, 16-18 Westmoreland Street, London, W1G 8PH, UK.
- Cardiovascular Biomedical Research Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - James C Moon
- UCL Institute of Cardiovascular Science, University College London, Gower Street, London, WC1E 6BT, UK.
- Division of Cardiovascular Imaging, The Heart Hospital, part of University College London NHS Foundation Trust, 16-18 Westmoreland Street, London, W1G 8PH, UK.
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Duerst Y, Wilm BJ, Wyss M, Dietrich BE, Gross S, Schmid T, Brunner DO, Pruessmann KP. Utility of real-time field control in T2
*-Weighted head MRI at 7T. Magn Reson Med 2015; 76:430-9. [DOI: 10.1002/mrm.25838] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 06/12/2015] [Accepted: 06/19/2015] [Indexed: 01/05/2023]
Affiliation(s)
- Yolanda Duerst
- Institute for Biomedical Engineering; University of Zurich and ETH Zurich; Zurich Switzerland
| | - Bertram J. Wilm
- Institute for Biomedical Engineering; University of Zurich and ETH Zurich; Zurich Switzerland
- Skope Magnetic Resonance Technologies; Zurich Switzerland
| | - Michael Wyss
- Institute for Biomedical Engineering; University of Zurich and ETH Zurich; Zurich Switzerland
| | - Benjamin E. Dietrich
- Institute for Biomedical Engineering; University of Zurich and ETH Zurich; Zurich Switzerland
| | - Simon Gross
- Institute for Biomedical Engineering; University of Zurich and ETH Zurich; Zurich Switzerland
| | - Thomas Schmid
- Institute for Biomedical Engineering; University of Zurich and ETH Zurich; Zurich Switzerland
| | - David O. Brunner
- Institute for Biomedical Engineering; University of Zurich and ETH Zurich; Zurich Switzerland
| | - Klaas P. Pruessmann
- Institute for Biomedical Engineering; University of Zurich and ETH Zurich; Zurich Switzerland
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