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Modelo Fundacional Basado en Resonancia Magnética Rutinaria para Perfil Molecular y Predicción de Progresión de

Junxian Li¹, Renhe Liu², Yuchen Xing³

¹Department of Blood Transfusion, Key Laboratory of Cancer Prevention and Therapy, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin, China.

JCO precision oncology

|February 20, 2026

Resumen

Un nuevo modelo fundacional de resonancia magnética (RM) autosupervisado, UMBIF, apoya eficazmente las aplicaciones de glioblastoma. Ayuda en la evaluación de resultados post-terapéuticos y la predicción de marcadores moleculares, mejorando los flujos de trabajo de diagnóstico.

Sus antecedentes:

Los modelos fundacionales están emergiendo como herramientas poderosas en imágenes médicas.
El aprendizaje autosupervisado (SSL) reduce la necesidad de anotaciones manuales extensas.
El diagnóstico de glioblastoma y el monitoreo del tratamiento requieren un análisis de imagen preciso y un perfilado molecular.

Conclusiones:

UMBIF muestra una fuerte transferibilidad para la evaluación de imágenes post-terapéuticas y la predicción del estado molecular en glioblastoma.
El preentrenamiento autosupervisado mejora el rendimiento y reduce la dependencia de las anotaciones manuales.
El marco UMBIF puede optimizar los flujos de trabajo de diagnóstico para mejorar la eficiencia y la confiabilidad.

Palabras clave:

inteligencia artificial aprendizaje automático resonancia magnética tumores cerebrales glioblastoma medicina de precisión diagnóstico por imágenes aprendizaje autosupervisado

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