Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground‐Glass Nodules Based on AI‐Extracted Radiomic Features

Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground‐Glass Nodules Based on AI‐Extracted Radiomic Features

ABSTRACT Background With the widespread adoption of low‐dose CT screening, the detection of pulmonary ground‐glass nodules (GGNs) has risen markedly, presenting diagnostic challenges in distinguishing preinvasive lesions from invasive adenocarcinomas (IAC). This study aimed to develop a machine lear...

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Bibliographic Details
Main Authors: Guozhen Yang, Yuanheng Huang, Huiguo Chen, Weibin Wu, Yonghui Wu, Kai Zhang, Xiaojun Li, Jiannan Xu, Jian Zhang
Format: Article
Language:English
Published: Wiley 2025-08-01
Series:Thoracic Cancer
Subjects:
artificial intelligence
invasiveness
pulmonary ground‐glass nodules
radiomics
Online Access:https://doi.org/10.1111/1759-7714.70128
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https://doi.org/10.1111/1759-7714.70128

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