Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences

Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences

Abstract The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A total of 126 patients with thymomas and 5 patients with thymic carcinoma treated at our institution, including 65 low-risk patients and 66 high...

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Bibliographic Details
Main Authors: Zhu Liang, Jiamin Li, Yihan Tang, Yaxuan Zhang, Chunyuan Chen, Siyuan Li, Xuefeng Wang, Xinyan Xu, Ziye Zhuang, Shuyan He, Biao Deng
Format: Article
Language:English
Published: Nature Portfolio 2024-08-01
Series:Scientific Reports
Subjects:
Machine learning
CT
Thymoma
Online Access:https://doi.org/10.1038/s41598-024-69735-3
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