Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take...

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Bibliographic Details
Main Authors: Kiersten Preuss, Nate Thach, Xiaoying Liang, Michael Baine, Justin Chen, Chi Zhang, Huijing Du, Hongfeng Yu, Chi Lin, Michael A. Hollingsworth, Dandan Zheng
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Cancers
Subjects:
radiomics
quantitative imaging
pancreatic cancer
machine learning
deep learning
Online Access:https://www.mdpi.com/2072-6694/14/7/1654

Internet

https://www.mdpi.com/2072-6694/14/7/1654

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