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Graph filtration learning reveals new dimensions in hepatocellular carcinoma imaging

August 30, 2024
in Technology and Engineering
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Beyond pixels: Graph filtration learning unveils new dimensions in hepatocellular carcinoma imaging
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“In medical imaging, our understanding of hepatocellular carcinoma (HCC) has long been constrained by the limitations of pixel-based analysis.”

Beyond pixels: Graph filtration learning unveils new dimensions in hepatocellular carcinoma imaging

Credit: Impact Journals, LLC

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“In medical imaging, our understanding of hepatocellular carcinoma (HCC) has long been constrained by the limitations of pixel-based analysis.”

BUFFALO, NY- August 30, 2024 – A new editorial was published in Oncotarget’s Volume 15 on July 24, 2024, entitled, “Beyond pixels: Graph filtration learning unveils new dimensions in hepatocellular carcinoma imaging.”

As traditional pixel-based methods reach their limits, Graph Filtration Learning (GFL) offers a novel approach to capturing complex topological features in medical images. By representing imaging data as graphs and leveraging persistent homology, GFL unveils new dimensions of information that were previously inaccessible.

In this editorial, researcher Yashbir Singh from the Department of Radiology, Mayo Clinic, in Rochester, Minnesota, explores the emerging role of GFL in revolutionizing Hepatocellular carcinoma (HCC) imaging analysis.

In medical imaging, the understanding of HCC has long been constrained by the limitations of pixel-based analysis. While traditional methods are valuable, they often struggle to capture the full complexity of tumor heterogeneity, vascular patterns, and tissue architecture that characterize this aggressive liver cancer.

“We discuss the principles of GFL, its potential applications in HCC imaging, and the challenges in translating this innovative technique into clinical practice.”

Continue reading: DOI:

Correspondence to: Yashbir Singh – singh.yashbir@mayo.edu

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Keywords: cancer, graph filtration learning, hepatocellular carcinoma, medical imaging, topological data analysis, tumor characterization

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About Oncotarget:

Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science.

Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

To learn more about Oncotarget, visit Oncotarget.com and connect with us on social media:

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For media inquiries, please contact media@impactjournals.com. 

Oncotarget Journal Office
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Journal

Oncotarget

DOI

10.18632/oncotarget.28635

Method of Research

Commentary/editorial

Subject of Research

Not applicable

Article Title

Beyond pixels: Graph filtration learning unveils new dimensions in hepatocellular carcinoma imaging

Article Publication Date

24-Jul-2024

COI Statement

Author has no conflicts of interest to declare.

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