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Videos de Conceptos Relacionados

The Electromagnetic Spectrum02:37

The Electromagnetic Spectrum

The electromagnetic spectrum consists of all the types of electromagnetic radiation arranged according to their frequency and wavelength. Each of the various colors of visible light has specific frequencies and wavelengths associated with them, and you can see that visible light makes up only a small portion of the electromagnetic spectrum. Because the technologies developed to work in various parts of the electromagnetic spectrum are different, for reasons of convenience and historical...
Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...
Isotopes and Radioisotopes01:28

Isotopes and Radioisotopes

In the early 1900s, English chemist Frederick Soddy realized that an element could have atoms with different masses that were chemically indistinguishable. These different types are called isotopes — atoms of the same element that differ in mass. Isotopes differ in mass because they have different numbers of neutrons but are chemically identical because they have the same number of protons. Soddy was awarded the Nobel Prize in Chemistry in 1921 for this discovery.
An isotope containing more...
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Atomic Absorption Spectroscopy: Radiation and Light Sources

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Atomic Emission Spectroscopy: Lab

AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Imaging Studies II: Positron Emission Tomography and Scintigraphy

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Video Experimental Relacionado

Updated: Jul 7, 2026

Isolation and Characterization of Tumor-initiating Cells from Sarcoma Patient-derived Xenografts
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Un modelo de clasificación basado en la radiómica explicable para el diagnóstico de sarcoma

Simona Correra1,2, Arnar Evgení Gunnarsson2, Marco Recenti2

  • 1Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy.

Diagnostics (Basel, Switzerland)
|August 28, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un marco de IA explicable para clasificar los tumores de sarcoma utilizando la radiómica de resonancia magnética. El modelo distingue con precisión los tumores, ayudando en el diagnóstico y tratamiento personalizado del cáncer.

Palabras clave:
ClasificaciónExplicabilidadAprendizaje automáticoRadiologíadiagnóstico de sarcoma

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Área de la Ciencia:

  • Radiómica y imágenes médicas
  • Inteligencia Artificial en Oncología
  • Aprendizaje automático para la clasificación de enfermedades

Sus antecedentes:

  • La clasificación de tumores de sarcoma se basa en gran medida en la interpretación subjetiva de las imágenes de resonancia magnética.
  • Necesidad de métodos objetivos y automatizados para mejorar la precisión y la eficiencia del diagnóstico.
  • La IA explicable (IAX) ofrece un potencial de apoyo a la toma de decisiones clínicas transparente y confiable.

Objetivo del estudio:

  • Desarrollar y validar un marco de aprendizaje automático explicable basado en la radiómica para la clasificación automatizada de tumores de sarcoma mediante RM.
  • Reducir la dependencia del médico de la interpretación subjetiva de las imágenes.
  • Mejorar la interpretabilidad de los modelos de IA en el diagnóstico médico.

Principales métodos:

  • Extracción de 851 características radiómicas, incluidos los descriptores wavelet, de 186 imágenes de resonancia magnética de pacientes con sarcoma.
  • Entrenamiento de un clasificador de Bosque Aleatorio con ajuste de hiperparámetros mediante validación cruzada anidada.
  • Utilizando la Importancia de las Características y las Explicaciones Agnóstico-Modelo Interpretables Locales (LIME) para la interpretabilidad del modelo.

Principales resultados:

  • El modelo radiómico logró una puntuación F1 de 0,742 y una precisión de 0,724 en el conjunto de pruebas.
  • El análisis LIME identificó la textura y las características radiómicas basadas en wavelets como predictores clave.
  • El marco demostró una clasificación eficaz de los tumores de sarcoma a partir de tejido sano.

Conclusiones:

  • El marco de IA explicable propuesto permite una clasificación precisa e interpretable de los sarcomas a partir de la resonancia magnética.
  • Este enfoque no invasivo apoya el diagnóstico precoz, personalizado y de precisión del cáncer.
  • El estudio subraya el valor de la IA explicable para mejorar la seguridad de la toma de decisiones clínicas.