Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy

Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy

<b>Background/Objectives:</b> Deep inspiration breath-hold (DIBH) is a commonly used technique to reduce the mean heart dose (MHD), which is critical for minimizing late cardiac side effects in breast cancer patients undergoing radiation therapy (RT). Although previous studies have explo...

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Bibliographic Details
Main Authors: Wlla E. Al-Hammad, Masahiro Kuroda, Ghaida Al Jamal, Mamiko Fujikura, Ryo Kamizaki, Kazuhiro Kuroda, Suzuka Yoshida, Yoshihide Nakamura, Masataka Oita, Yoshinori Tanabe, Kohei Sugimoto, Irfan Sugianto, Majd Barham, Nouha Tekiki, Miki Hisatomi, Junichi Asaumi
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Diagnostics
Subjects:
breast cancer
radiation therapy
heart dose
cut-off value
machine learning
robustness
Online Access:https://www.mdpi.com/2075-4418/15/6/668
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