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Updated: Jan 20, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Predicción de metástasis de ganglios linfáticos en cáncer colorrectal mediante aprendizaje de múltiples instancias a

Ling-Feng Zou1, Xuan-Bing Wang2,3, Jing-Wen Li1

  • 1Department of Pathology, Chongqing Traditional Chinese Medicine Hospital, Chongqing 400021, China.

World journal of gastroenterology
|January 19, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo marco de aprendizaje de múltiples instancias (MIL) a nivel de caso mejora significativamente la predicción de metástasis de ganglios linfáticos (LNM) en el cáncer colorrectal avanzado (CCR). Este enfoque de IA, que integra datos de patología y clínicos, supera a los métodos tradicionales para una mejor estratificación del riesgo del paciente.

Palabras clave:
Cáncer colorrectalAprendizaje profundoHistopatologíaMetástasis de ganglios linfáticosAprendizaje de múltiples instancias

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Área de la Ciencia:

  • Patología digital
  • Inteligencia artificial en oncología
  • Investigación sobre cáncer colorrectal

Sus antecedentes:

  • La predicción precisa de la metástasis de ganglios linfáticos (LNM) es vital para el manejo del cáncer colorrectal (CCR) localmente avanzado (T3/T4).
  • La histopatología tradicional y el aprendizaje profundo a nivel de portaobjetos luchan con características metastásicas escasas y críticas.

Objetivo del estudio:

  • Desarrollar y validar un marco de aprendizaje de múltiples instancias (MIL) a nivel de caso.
  • Imitar la revisión integral del patólogo para mejorar la predicción de LNM de CCR T3/T4.

Principales métodos:

  • Análisis retrospectivo de imágenes de portaobjetos completos de 130 pacientes con CCR T3/T4.
  • Marco MIL a nivel de caso que utiliza los modelos de aprendizaje profundo CONCH v1.5 y UNI2-h.
  • Integración de características patológicas con datos clínicos; rendimiento evaluado por AUC.

Principales resultados:

  • El marco MIL a nivel de caso superó al entrenamiento a nivel de portaobjetos (CONCH v1.5 AUC: 0.899 frente a 0.814).
  • La integración de datos de patología y clínicos mejoró la predicción (AUC del modelo superior: 0.904 frente a AUC solo clínico: 0.584).
  • Las regiones identificadas por el modelo se alinearon con características histopatológicas de alto riesgo confirmadas por el patólogo.

Conclusiones:

  • El marco MIL a nivel de caso ofrece un método superior para la predicción de LNM en CCR avanzado.
  • Este enfoque muestra potencial para la estratificación del riesgo y la guía de las decisiones terapéuticas.
  • Se justifica una mayor validación del marco.