Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Patients Using Harmonized Radiomics of Multcenter <sup>18</sup>F-FDG-PET Image

Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Patients Using Harmonized Radiomics of Multcenter <sup>18</sup>F-FDG-PET Image

We developed machine and deep learning models to predict chemoradiotherapy in rectal cancer using <sup>18</sup>F-FDG PET images and harmonized image features extracted from <sup>18</sup>F-FDG PET/CT images. Patients diagnosed with pathologic T-stage III rectal cancer with a t...

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Bibliographic Details
Main Authors: Hye-Min Ju, Jingyu Yang, Jung-Mi Park, Joon-Ho Choi, Hyejin Song, Byung-Il Kim, Ui-Sup Shin, Sun Mi Moon, Sangsik Cho, Sang-Keun Woo
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Cancers
Subjects:
harmonized radiomics
machine learning
deep learning
radiochemotherapy
<sup>18</sup>F-FDG PET
Online Access:https://www.mdpi.com/2072-6694/15/23/5662

Internet

https://www.mdpi.com/2072-6694/15/23/5662

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