A Radiomics-Based Machine Learning Model for Predicting Pneumonitis During Durvalumab Treatment in Locally Advanced NSCLC

<b>Introduction:</b> Pneumonitis represents one of the clinically significant adverse events observed in patients with non-small-cell lung cancer (NSCLC) who receive durvalumab as consolidation therapy after chemoradiotherapy (CRT). Although clinical factors such as radiation dose (e.g., V20) and in...

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Autors principals:	Takeshi Masuda, Daisuke Kawahara, Wakako Daido, Nobuki Imano, Naoko Matsumoto, Kosuke Hamai, Yasuo Iwamoto, Yusuke Takayama, Sayaka Ueno, Masahiko Sumii, Hiroyasu Shoda, Nobuhisa Ishikawa, Masahiro Yamasaki, Yoshifumi Nishimura, Shigeo Kawase, Naoki Shiota, Yoshikazu Awaya, Soichi Kitaguchi, Yuji Murakami, Yasushi Nagata, Noboru Hattori
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2026-01-01
Col·lecció:	AI
Matèries:	radiomics artificial intelligence chemoradiotherapy durvalumab pneumonitis
Accés en línia:	https://www.mdpi.com/2673-2688/7/1/32
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