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Levenson, R.M.* ; Singh, Y.* ; Rieck, B. ; Hathaway, Q.A.* ; Farrelly, C.* ; Rozenblit, J.* ; Prasanna, P.* ; Erickson, B.* ; Choudhary, A.* ; Carlsson, G.* ; Deepa, D.*

Advancing precision medicine: Algebraic topology and differential geometry in radiology and computational pathology.

Lab. Invest. 104:102060 (2024)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative, and increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Korrespondenzautor
Schlagwörter Precision Medicine ; Geometry ; Pathomics ; Radiomics ; Topological Data Analysis
ISSN (print) / ISBN 0023-6837
e-ISSN 1530-0307
Zeitschrift Laboratory Investigation
Quellenangaben Band: 104, Heft: 6, Seiten: , Artikelnummer: 102060 Supplement: ,
Verlag Nature Publishing Group
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)