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Resumen
Este resumen es generado por máquina.

Los modelos de inteligencia artificial (IA) que integran datos de ecografía y clínicos diferencian con precisión las lesiones mamarias fibroepiteliales benignas de las malignas. Este enfoque asistido por IA mejora la precisión diagnóstica de los tumores filodes (TF), reduciendo los riesgos de clasificación errónea.

Palabras clave:
Inteligencia artificialEcografía mamariaAprendizaje profundoLesión fibroepitelialTumor filodes

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Área de la Ciencia:

  • Radiología e Imagen Médica
  • Inteligencia Artificial en Medicina
  • Oncología

Sus antecedentes:

  • Las lesiones fibroepiteliales mamarias, incluidos los fibroadenomas y los tumores filodes (TF), presentan desafíos diagnósticos en la biopsia con aguja.
  • La clasificación errónea puede provocar cirugías innecesarias para lesiones benignas o retraso en el tratamiento de TF malignos.
  • El programa AI-FLEET tiene como objetivo mejorar la precisión diagnóstica mediante la integración de diversos tipos de datos.

Objetivo del estudio:

  • Desarrollar y evaluar modelos de IA para distinguir los TF benignos de los límites/malignos utilizando datos de ecografía y clínicos.
  • Evaluar el rendimiento de diferentes arquitecturas de aprendizaje profundo en la clasificación de lesiones fibroepiteliales.

Principales métodos:

  • Análisis retrospectivo de 81 pacientes con TF confirmados histológicamente (65 benignos, 16 límites/malignos).
  • Entrenamiento de modelos multimodales de aprendizaje profundo (ConvNeXt, ResNet18) utilizando imágenes de ecografía y variables clínicas (edad, IMC, raza, estado menopáusico, ecogenicidad, tamaño del tumor).
  • Evaluación mediante validación cruzada de cinco pliegues estratificada por sujeto.

Principales resultados:

  • Los modelos multimodales ConvNeXt y ResNet18 lograron una alta precisión (0,91-0,92) y AUC (0,94).
  • Los modelos solo de ecografía y solo clínicos mostraron un rendimiento inferior (AUC de 0,89 y 0,78, respectivamente).
  • La heterogeneidad intratumoral se identificó como una característica predictiva clave mediante análisis de saliency.

Conclusiones:

  • Los modelos multimodales de aprendizaje profundo diferencian eficazmente los TF benignos de los límites/malignos.
  • La evaluación asistida por IA de las lesiones fibroepiteliales es factible y muestra una alta precisión diagnóstica.
  • El trabajo futuro (Fase II) incorporará histopatología y casos de fibroadenoma benigno para una integración mejorada.