Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules

Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules

Abstract Purpose To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. Methods A retrospective analysis was conducted on a cohort comprising 500 pa...

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Bibliographic Details
Main Authors: Haitao Sun, Chunling Zhang, Aimei Ouyang, Zhengjun Dai, Peiji Song, Jian Yao
Format: Article
Language:English
Published: BMC 2023-11-01
Series:BioMedical Engineering OnLine
Subjects:
Computed tomography
Radiomics
Machine learning
Pulmonary nodule
Multi-classification
Online Access:https://doi.org/10.1186/s12938-023-01180-1

Internet

https://doi.org/10.1186/s12938-023-01180-1

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