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Khabaz K, Yuan K, Pugar J, Jiang D, Sankary S, Dhara S, Kim J, Kang J, Nguyen N, Cao K, Washburn N, Bohr N, Lee CJ, Kindlmann G, Milner R, Pocivavsek L. The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature. PLoS Comput Biol 2024; 20:e1011815. [PMID: 38306397 PMCID: PMC10866512 DOI: 10.1371/journal.pcbi.1011815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/14/2024] [Accepted: 01/09/2024] [Indexed: 02/04/2024] Open
Abstract
Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature (δK) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing δK forming the hallmark of aortic disease. Divergent δK is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8±1.7%. The analysis of δK can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.
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Affiliation(s)
- Kameel Khabaz
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Karen Yuan
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Joseph Pugar
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
- Departments of Material Science and Engineering, Biomedical Engineering, and Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - David Jiang
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Seth Sankary
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Sanjeev Dhara
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Junsung Kim
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Janet Kang
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Nhung Nguyen
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Kathleen Cao
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Newell Washburn
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Nicole Bohr
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Cheong Jun Lee
- Department of Surgery, NorthShore University Health System, Evanston, Illinois, United States of America
| | - Gordon Kindlmann
- Department of Computer Science, The University of Chicago, Chicago, Illinois, United States of America
| | - Ross Milner
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Luka Pocivavsek
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
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Isaeva VV, Kasyanov NV, Presnov EV. Topological singularities and symmetry breaking in development. Biosystems 2012; 109:280-98. [PMID: 22609746 DOI: 10.1016/j.biosystems.2012.05.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Revised: 05/10/2012] [Accepted: 05/10/2012] [Indexed: 11/18/2022]
Abstract
The review presents a topological interpretation of some morphogenetic events through the use of well-known mathematical concepts and theorems. Spatial organization of the biological fields is analyzable in topological terms. Topological singularities inevitably emerging in biological morphogenesis are retained and transformed during pattern formation. It is the topological language that can provide strict and adequate description of various phenomena in developmental and evolutionary transformations. The relationship between local and global orders in metazoan development, i.e., between local morphogenetic processes and integral developmental patterns, is established. A topological inevitability of some developmental events through the use of classical topological concepts is discussed. This methodology reveals a topological imperative as a certain set of topological rules that constrains and directs embryogenesis. A breaking of spatial symmetry of preexisting pattern plays a critical role in biological morphogenesis in development and evolution.
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Affiliation(s)
- Valeria V Isaeva
- A.N. Severtsov Institute of Ecology and Evolution of the Russian Academy of Science, 119071 Moscow, Russia.
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Isaeva V, Presnov E, Chernyshev A. Topological patterns in metazoan evolution and development. Bull Math Biol 2006; 68:2053-67. [PMID: 16850353 DOI: 10.1007/s11538-006-9063-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2005] [Accepted: 12/06/2005] [Indexed: 11/25/2022]
Abstract
Topological patterns in the development and evolution of metazoa, from sponges to chordates, are considered by means of previously elaborated methodology, with the genus of the surface used as a topological invariant. By this means metazoan morphogenesis may be represented as topological modification(s) of the epithelial surfaces of an animal body. The animal body surface is an interface between an organism and its environment, and topological transformations of the body surface during metazoan development and evolution results in better distribution of flows to and from the external medium, regarded as the source of nutrients and oxygen and the sink of excreta, so ensuring greater metabolic intensity. In sponges and some Cnidaria, the increase of this genus up to high values and the shaping of topologically complicated fractal-like systems are evident. In most Bilateria, a stable topological pattern with a through digestive tube is formed, and the subsequent topological complications of other systems can also appear. The present paper provides a topological interpretation of some developmental events through the use of well-known mathematical concepts and theorems; the relationship between local and global orders in metazoan development, i.e., between local morphogenetic processes and integral developmental patterns, is established. Thus, this methodology reveals a "topological imperative": A certain set of topological rules that constrains and directs biological morphogenesis.
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Affiliation(s)
- Valeria Isaeva
- Institute of Marine Biology, Far East Branch of Russian Academy of Sciences, Palchevskii St, 17, Vladivostok, 690041, Russia.
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