image: 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.
Credit: Impact Journals, LLC
“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: https://doi.org/10.18632/oncotarget.28635
Correspondence to: Yashbir Singh - singh.yashbir@mayo.edu
Video short: https://www.youtube.com/watch?v=3cUJEeRnQWY
Keywords: cancer, graph filtration learning, hepatocellular carcinoma, medical imaging, topological data analysis, tumor characterization
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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.
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Journal
Oncotarget
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.