Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using <sup>18</sup>F-FDG PET Images

Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using <sup>18</sup>F-FDG PET Images

Background: This study aimed to propose a machine learning model to predict the local response of resectable locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated by neoadjuvant chemoradiotherapy (NCRT) using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) i...

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Bibliographic Details
Main Authors: Yuji Murakami, Daisuke Kawahara, Shigeyuki Tani, Katsumaro Kubo, Tsuyoshi Katsuta, Nobuki Imano, Yuki Takeuchi, Ikuno Nishibuchi, Akito Saito, Yasushi Nagata
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Diagnostics
Subjects:
esophageal cancer
squamous cell carcinoma
neoadjuvant chemoradiotherapy
pathological response
machine learning
radiomics
Online Access:https://www.mdpi.com/2075-4418/11/6/1049

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