Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy
Abstract Objectives To explore the role of radiomics in predicting the prognosis of proximal esophageal cancer and to investigate the biological underpinning of radiomics in identifying different prognoses. Methods A total of 170 patients with pathologically and endoscopically confirmed proximal eso...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-11-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-024-01853-y |
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