Diagnostics of prostate cancer based on ultrasonic multifeature tissue characterization

Ultrasonic multifeature tissue characterization can be used for the computerized detection of prostate cancer tumors. Malignant areas within the prostate can be located with a high degree of accuracy, independent of the diagnostic skills of the operator. Radiofrequency ultrasonic echo data of the prostate are captured using standard ultrasound equipment. Several features describing the histological characteristics of the underlying tissue are estimated after dividing each ultrasound data frame into up to 1000 regions of interest and compensating the echo data for diffraction and system dependent effects. Spectral features, textural features of first and second order, clinical variables and morphological descriptors are applied. Two parallel network-based fuzzy inference systems classify and separate the regions of interest. Subsequent morphological analysis combines clusters within malignancy maps, which consist of conventional grey-scaled B-mode images with areas of high cancer probability marked in red. In a clinical study, RF ultrasonic echo data of 100 patients have been recorded. Prostate slices with histological diagnosis following radical prostatectomies are used as standard. The mean area under the ROC curve is between 0.84, for isoechoic tumors, and 0.86, for hypo- and hyperechoic tumors. Standard deviations are as low as 0.02, for isoechoic tumors, and 0.01, for hyper- and hypoechoic tumors. All three spectral approaches, evaluated conventional Fourier spectrum parameters, generalized spectrum parameters and AR parameters, yield comparable classification rates for the underlying prostate data sets.