Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules

Purpose To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs).MethodsWe retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in o...

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Библиографические подробности

Главные авторы:	Fen-hua Zhao, Hong-jie Fan, Kang-fei Shan, Long Zhou, Zhen-zhu Pang, Chun-long Fu, Ze-bin Yang, Mei-kang Wu, Ji-hong Sun, Xiao-ming Yang, Zhao-hui Huang
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Frontiers Media S.A. 2022-05-01
Серии:	Frontiers in Oncology
Предметы:	lung tumor ground-glass nodules radiomics random forest diagnosis
Online-ссылка:	https://www.frontiersin.org/articles/10.3389/fonc.2022.872503/full
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