Interpretable machine learning model integrating CT radiomics, CTR, and clinical features for EGFR mutation prediction in ≤3 cm lung adenocarcinoma nodules

Background Non-invasive prediction of EGFR mutation status in lung adenocarcinoma (LUAD) is critical for treatment planning, particularly in small pulmonary nodules where tissue genotyping is limited. However, the consolidation-to-tumor ratio (CTR), a clinically relevant imaging biomarker, has rarel...

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Ngā kaituhi matua:	Wenhan Cai, Yiming Liu, Kai Zhao, Zirui Zhu, Jiamei Jin, Herui Han, Mingchuan Hu, Xiangming Qiu, Jiaxin Wen, Zhiqiang Xue
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Taylor & Francis Group 2025-12-01
Rangatū:	Annals of Medicine
Ngā marau:	Lung adenocarcinoma EGFR mutation radiomics consolidation-to-tumor ratio CT imaging machine learning
Urunga tuihono:	https://www.tandfonline.com/doi/10.1080/07853890.2025.2607160
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!