An Integrated Nomogram Combining Deep Learning and Radiomics for Predicting Malignancy of Pulmonary Nodules Using CT‐Derived Nodules and Adipose Tissue: A Multicenter Study

An Integrated Nomogram Combining Deep Learning and Radiomics for Predicting Malignancy of Pulmonary Nodules Using CT‐Derived Nodules and Adipose Tissue: A Multicenter Study

ABSTRACT Background Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules. Methods One thousand and ninety‐eig...

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Bibliographic Details
Main Authors: Shidi Miao, Qifan Xuan, Hanbing Xie, Yuyang Jiang, Mengzhuo Sun, Wenjuan Huang, Jing Li, Hongzhuo Qi, Ao Li, Qiujun Wang, Zengyao Liu, Ruitao Wang
Format: Article
Language:English
Published: Wiley 2024-11-01
Series:Cancer Medicine
Subjects:
adipose tissue
computed tomography
deep learning
multicenter
multimodal
nomogram
Online Access:https://doi.org/10.1002/cam4.70372
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