Comparativa de diferentes modelos radiómicos para la clasificación de lesiones adrenales indeterminadas diagnosticadas de forma incidental en TC con contraste
DOI:
https://doi.org/10.37004/sefm/2024.25.2.001Palabras clave:
radiómica, Inteligencia Artificial, LifeX, adenoma, incidentalomas, suprarrenalResumen
Objetivo: Se realiza una comparativa de diferentes modelos de aprendizaje automático para discriminar incidentalomas suprarrenales mediante estudios de TC.
Métodos: Se obtuvieron 62 características radiómicas diferentes a partir de una muestra de 61 incidentalomas indeterminados utilizando el software de licencia libre LIFEx. Se realizaron 19 modelos predictivos empleando además diversos métodos de selección de características para optimizar la detección de lesiones malignas. Para todos ellos se evaluaron cuatro métodos de validación cruzada. El contorneado de los adenomas fue realizado por duplicado por 4 radiólogos.
Resultados: Se obtienen los valores del área bajo la curva ROC entre 0,42 (0,09-0,81) y 0,92 (0,63-1,00), y exactitud de los modelos entre 0,63 (0,43-0,79) y 0,94 (0,82-1,00). El modelo de mejor rendimiento fue la regresión logística balanceada entrenado con 14 características con un coeficiente intraclase superior a 0,9, con el que se obtuvo una exactitud de 0,94 (0,74-1,00), un AUC ROC de 0,917 (0,63-1,00), una sensibilidad de 0,92 (0,65-1,00) y especificidad de 1,00 (0,71-1,00)
Conclusiones: La evaluación, comparación y validación de diferentes modelos predictivos basados en características radiómicas nos ha permitido obtener un modelo optimizado para la detección de tumores adrenales malignos entre los incidentalomas diagnosticados de forma incidental en TC con contraste.
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