Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures.
Cancer Cell
; 42(7): 1239-1257.e7, 2024 Jul 08.
Article
en En
| MEDLINE
| ID: mdl-38942025
ABSTRACT
Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we established an international molecularly characterized database encompassing 934 medulloblastoma patients from thirteen centers across China and the United States. We demonstrate how image-based machine learning strategies have the potential to create an alternative pathway for non-invasive, presurgical, and low-cost molecular subgroup prediction in the clinical management of medulloblastoma. Our robust validation strategies-including cross-validation, external validation, and consecutive validation-demonstrate the model's efficacy as a generalizable molecular diagnosis classifier. The detailed analysis of MRI characteristics replenishes the understanding of medulloblastoma through a nuanced radiographic lens. Additionally, comparisons between East Asia and North America subsets highlight critical management implications. We made this comprehensive dataset, which includes MRI signatures, clinicopathological features, treatment variables, and survival data, publicly available to advance global medulloblastoma research.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Imagen por Resonancia Magnética
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Neoplasias Cerebelosas
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Meduloblastoma
Límite:
Adolescent
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Adult
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Child
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Child, preschool
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Female
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Humans
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Infant
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Male
País/Región como asunto:
America do norte
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Asia
Idioma:
En
Revista:
Cancer Cell
Asunto de la revista:
NEOPLASIAS
Año:
2024
Tipo del documento:
Article