RESUMEN
We investigated the possibility of using computer analysis of high-resolution CT images to radiologically classify the shape of pulmonary nodules. From a total of 107 HRCT images of solid, solitary pulmonary nodules with prior differentiation as benign (n=55) or malignant (n=52), we extracted the desired pulmonary nodules and calculated two quantitative parameters for characterizing nodules: circularity and second central moment. Using discriminant analysis for two thresholds in differentiating malignant from benign states resulted in a sensitivity of 76.9%, a specificity of 80%, a positive predictive value of 78.4%, and a negative predictive value of 78.6%.
Asunto(s)
Algoritmos , Inteligencia Artificial , Neoplasias Pulmonares/diagnóstico por imagen , Reconocimiento de Normas Patrones Automatizadas/métodos , Intensificación de Imagen Radiográfica , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Nódulo Pulmonar Solitario/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Diagnóstico Diferencial , Femenino , Humanos , Masculino , Persona de Mediana Edad , Intensificación de Imagen Radiográfica/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
We investigated the possibility of using computer analysis of high-resolution CT images to radiologically classify the shape of pulmonary nodules. Using a combination of circularity and second moment as quantitative measures we were able to classify pulmonary nodules in each shape group as effectively as could a radiologist. We found that pulmonary nodules with circularity < or =0.75 and second moment < or =0.18 were very likely to reveal lung cancer.