RESUMEN
A statistical approach was implemented in the study of histologic characteristics from ex vivo basal cell carcinomas, based on the properties of backscattered acoustic waves, for the purpose of evaluating the method as a diagnostic tool. The study was developed using an ultrasound biomicroscope working at a frequency of 45 MHz. The parameters examined were signal-to-noise ratio (SNR), and shape parameters from the Weibull (b(W)) and generalized gamma (c(GG) and υ(GG)) probability density functions. Twenty-seven carcinomatous skin samples were obtained from volunteer patients and classified into two groups (BCC1 and BCC2) based on the distribution patterns of their tumor nests; also, seven non-tumoral samples were used for comparative purposes. Significant differences between groups were obtained for all studied parameters. The successful differentiation between some tissue groups suggests its potential use for carcinoma characterization.
Asunto(s)
Carcinoma Basocelular/diagnóstico por imagen , Carcinoma Basocelular/patología , Interpretación de Imagen Asistida por Computador/métodos , Microscopía Acústica/métodos , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Interpretación Estadística de Datos , Humanos , Dosis de Radiación , Reproducibilidad de los Resultados , Dispersión de Radiación , Sensibilidad y Especificidad , SonidoRESUMEN
Ultrasound breast images have been used to improve diagnostics and decrease the number of unneeded biopsies. Malignant breast tumors tend to present irregular and blurred contours while benign ones are usually round, smooth and well-defined. Accordingly, investigating the tumor contour may help in establishing diagnosis. Herein, Mutual Information and Linear Discriminant Analysis were implemented to rank morphometric features in discriminating breast tumors in ultrasound images. Seven features were extracted from Convex Polygon and the Normalized Radial Length techniques. By applying a Mutual Information based approach, it was possible to identity features with possibly non-linear contributions to the outcome.
Asunto(s)
Neoplasias de la Mama/ultraestructura , Aumento de la Imagen/métodos , Ultrasonografía Mamaria/métodos , Bases de Datos Factuales , Femenino , HumanosRESUMEN
This work aims at investigating seven morphological parameters in distinguishing malignant and benign breast tumors on ultrasound images. Linear discriminant analysis was applied to sets of up to five parameters and then the performances were assessed using the area Az (+/- standard error) under the ROC curve, accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value and negative predictive value. The most relevant individual parameters were the normalized residual value (nrv) and overlap ratio (RS), both calculated from the convex polygon technique, and the circularity (C). When nrv and C were taken together with roughness (R), calculated from normalized radial length (NRL), a performance slightly over 83% in distinguishing malignant and benign breast tumors was achieved.
Asunto(s)
Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/diagnóstico , Ultrasonografía Mamaria/métodos , Algoritmos , Inteligencia Artificial , Automatización , Teorema de Bayes , Femenino , Humanos , Modelos Estadísticos , Reconocimiento de Normas Patrones Automatizadas/métodos , Valor Predictivo de las Pruebas , Curva ROC , Reproducibilidad de los Resultados , Estudios Retrospectivos , Sensibilidad y EspecificidadRESUMEN
OBJECTIVE: This work develops a discrete-event computer simulation model for the analysis of a mammography clinic performance. MATERIAL AND METHODS: Two mammography clinic computer simulation models were developed, based on an existing public sector clinic of the Brazilian Cancer Institute, located in Rio de Janeiro city, Brazil. Two clinics in a total of seven configurations (number of equipment units and working personnel) were studied. Models tried to simulate changes in patient arrival rates, number of equipment units, available personnel (technicians and physicians), equipment maintenance scheduling schemes and exam repeat rates. Model parameters were obtained by direct measurements and literature reviews. A commercially-available simulation software was used for model building. RESULTS: The best patient scheduling (patient arrival rate) for the studied configurations had an average of 29 min for Clinic 1 (consisting of one mammography equipment, one to three technicians and one physician) and 21 min for Clinic 2 (two mammography equipment units, one to four technicians and one physician). The exam repeat rates and equipment maintenance scheduling simulations indicated that a large impact over patient waiting time would appear in the smaller capacity configurations. CONCLUSIONS: Discrete-event simulation was a useful tool for defining optimal operating conditions for the studied clinics, indicating the most adequate capacity configurations and equipment maintenance schedules.