Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
Cho et al. use a radiomics-guided deep-learning approach to model the prognosis of lung adenocarcinoma from CT scan data. This study demonstrates the utility of this technology as a predictive approach for stratifying clinical prognostic groups.
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e3df764d36224d0dbccb596f1c5bbbad |
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| Sumario: | Cho et al. use a radiomics-guided deep-learning approach to model the prognosis of lung adenocarcinoma from CT scan data. This study demonstrates the utility of this technology as a predictive approach for stratifying clinical prognostic groups. |
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