Análisis radiómico basado en el volumen de escala de grises de la tomografía computarizada de haz cónico para la diferenciación de quistes y tumores odontogénicos
DOI:
https://doi.org/10.15517/7es0h673Palabras clave:
Tomografía computarizada de haz cónico; Valor de escala de grises; Quistes maxilares; Tumores maxilares; Radiómica.Resumen
Caracterizar cuantitativamente el volumen de escala de grises (GSV) en la tomografía computarizada de haz cónico (CBCT) y evaluar su capacidad para diferenciar quistes odontogénicos de tumores odontogénicos mediante características radiómicas de primer orden. En este estudio retrospectivo se analizaron 100 lesiones confirmadas histopatológicamente (50 quistes y 50 tumores). Los estudios de CBCT fueron estandarizados mediante remuestreo y discretización de niveles de gris antes de la segmentación tridimensional manual. La extracción radiómica, realizada conforme a la Iniciativa para la Estandarización de Biomarcadores por Imagen (IBSI), produjo inicialmente 107 características. Posteriormente, el análisis se limitó a las características de intensidad de primer orden y, tras la reducción por correlación, se seleccionaron diez descriptores no redundantes del GSV. Las diferencias entre grupos se evaluaron mediante la prueba U de Mann-Whitney y la estructura del GSV se investigó mediante análisis de componentes principales (ACP). Los tumores presentaron mayor varianza (p=0.008), intensidad máxima más elevada (p=0.017) y menor curtosis (p=0.027) que los quistes, indicando mayor heterogeneidad interna y una distribución más amplia de intensidades. Asimismo, mostraron mayor entropía y menor uniformidad, reflejando una distribución de niveles de gris más aleatoria. Las medidas de tendencia central demostraron un valor discriminatorio limitado. El ACP mostró que los tres primeros componentes principales explicaban el 89.45 % de la varianza total. El análisis del volumen de escala de grises constituye un método cuantitativo objetivo para la evaluación volumétrica en CBCT. Los patrones diferenciales observados entre quistes y tumores respaldan al GSV como un biomarcador de imagen prometedor para la diferenciación de lesiones odontogénicas.
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Derechos de autor 2026 Sivan Sathish, Haritma Nigam, Rupal Gupta.
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