Table 1_Machine learning-based differentiation of lung squamous cell carcinoma and adenocarcinoma using clinical-semantic and radiomic features.docx

Table 1_Machine learning-based differentiation of lung squamous cell carcinoma and adenocarcinoma using clinical-semantic and radiomic features.docx

Purpose<p>To evaluate and compare the predictive performance of machine learning methods using clinical-semantic, radiomic, and combined features in distinguishing squamous cell carcinoma (SCC) from adenocarcinoma (ADC) in non-small cell lung cancer (NSCLC).</p>Methods<p>A total of...

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Bibliographische Detailangaben
1. Verfasser: Yanju Li (9697993) (author)
Weitere Verfasser: Xiaomeng Yang (702951) (author), Yingjin Zhang (20532682) (author), Jing Liang (32441) (author), Jiaxin Liu (833821) (author), Xinru Zheng (16801562) (author), Jing Wang (6206297) (author), Zhaoxiang Ye (4468483) (author)
Veröffentlicht: 2025
Schlagworte:
Oncology and Carcinogenesis not elsewhere classified
squamous cell carcinoma
adenocarcinoma
radiomics
machine learning
computed tomography
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