Utilización de la citomorfología junto con la citometría de flujo en material obtenido por PAAF en lesiones linfoproliferativas. Comparación de coste frente a la biopsia / Use of cytomorphology in combination with flow cytometry in material obtained through FNA in lymphoproliferative lesions. Costs compared with biopsy

Introducción: El uso de la punción-aspiración con aguja fina (PAAF) junto con la citometría de flujo (CMF) constituye una alternativa diagnóstica frente a la biopsia. Objetivos: Se compara la eficacia diagnóstica de la citología más CMF en enfermedades linfoproliferativas y se calcula el coste de la PAAF frente a la biopsia. Resultados: La sensibilidad de la CCF es del 96 por ciento en procesos reactivos y la especificidad del 97 por ciento. En el diagnóstico de los linfomas no Hodgkin, teniendo en cuenta el tipo, la sensibilidad es del 89 por ciento, la especificidad del 100 por ciento y la eficacia del 95 por ciento. La citología unida a la CMF resulta cuatro veces más barata que la biopsia. Conclusiones: Las técnicas de CMF e inmunocitoquímica para aplicar a las PAAF son aconsejables en determinados casos por su rapidez, economía, menor morbilidad y menores molestias para el paciente. Otra perspectiva a considerar es la aplicación de técnicas de biología molecular (AU)

Automated segmentation of cell nuclei in fine needle aspirates of the breast.

OBJECTIVE: To achieve a classifier of breast lesions to discriminate between benign and malignant cases of cytologic smears with automated segmentation image analysis techniques. STUDY DESIGN: The techniques were applied to images of epithelial cell nuclei from cytologic smears obtained by fine needle aspiration. The images of the nuclei were taken from 95 cases of malignant lesions and 47 benign (approximately 25 nuclei per case), and 28 nuclear variables were measured. The data were analyzed by a double methodology, discriminant analysis and classification and regression trees (CART), to determine which provided the best results. RESULTS: CART selected the SD of the nuclear area with correct classification of 85.1% of benign and 94.7% malignant aspirates. Discriminant analysis selected the group of variables formed by axis lengths, SD of the longest axis, sphericity and variance of gray levels, with results similar to those of CART. CONCLUSION: Automated segmentation image analysis techniques were effective, and the classifier was quick, simple and efficacious in malignant-benign discrimination.

Diagnosis of breast cancer by measuring nuclear disorder using planar graphs.

OBJECTIVE: To achieve a classifier of breast lesions to distinguish benign from malignant mammary lesions by quantifying nuclear disorder in epithelial cell groups from smears obtained by fine needle aspiration. STUDY DESIGN: The study included 95 cases of breast cancer (289 groups) and 47 of benign breast lesions (150 groups), diagnosed by cytology. Information from planar graphs (mean of nuclear distances, standard deviation, maximum and minimum distance between nuclei) was used, and an algorithm constructed for this purpose was applied. The data were classified by double methodology--discriminant analysis, and classification and regression trees (CART)--to determine which achieved the best results. RESULTS: CART selected the standard deviation of nuclear distances with accurate classification in 95.7% of benign lesions and 97.9% of malignant. Discriminant analysis constructed the discriminant function using the mean of nuclear distances and its standard deviation, with results similar to those of CART. CONCLUSION: The classifier based on nuclear disorder that we constructed proved to be rapid, simple and effective for malignant-benign discrimination in breast lesions and should be of diagnostic assistance.