Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are pred...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Buda, Mateusz [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Umfang: |
8 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Sa1349 Impact of Water Load Test on the Gastric Myoelectric Activity in Experimental Pigs - Tacheci, Ilja ELSEVIER, 2014, an international journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:109 ; year:2019 ; pages:218-225 ; extent:8 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.compbiomed.2019.05.002 |
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association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm |
| title_auth |
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm |
| abstract |
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes. |
| abstractGer |
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes. |
| abstract_unstemmed |
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes. |
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| title_short |
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm |
| url |
https://doi.org/10.1016/j.compbiomed.2019.05.002 |
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| author2 |
Saha, Ashirbani Mazurowski, Maciej A. |
| author2Str |
Saha, Ashirbani Mazurowski, Maciej A. |
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| doi_str |
10.1016/j.compbiomed.2019.05.002 |
| up_date |
2024-07-06T21:47:39.164Z |
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