Sonohistology - ultrasonic tissue characterization for prostate cancer diagnostics.

A computer-aided diagnostic system for imaging prostate cancer has been developed in order to supplement today's conventional methods for the early detection of prostate carcinoma. The system is based on analysis of the spectral content of radiofrequency ultrasonic echo data in combination with evaluations of textural, contextual, morphological and clinical features in a multiparameter approach. A state-of-the-art, non-linear classifier, the so-called adaptive network-based fuzzy inference system, is used for higher-order classification of the underlying tissue-describing parameters. The system has been evaluated on radio-frequency ultrasound data originating from 100 patients using histological specimens obtained after prostatectomy as the gold standard. Leave-one-out cross-validation over patient data sets results in areas under the ROC curve of 0.86 +/- 0.01 for hypoechoic and hyperechoic tumors and of 0.84 +/- 0.02 for isoechoic tumors, respectively.

[Prostate cancer diagnosis using ultrasound elastography. Introduction of a novel technique and first clinical results]. / Prostatakarzinomdiagnostik durch UltraschallelastographieVorstellung eines neuartigen Verfahrens und erste klinische Ergebnisse.

During the last decade screening has improved prostate cancer detection. The main reason for this development is a better understanding of the margins of prostate-specific antigen (PSA) serum levels and the classification of PSA subtypes. In contrast, the introduction of transrectal ultrasound has not led to a measurable change in the prostate cancer detection rate. Our aim was to develop a novel ultrasound system for the acquisition of elastographic images of the prostate and evaluate the system regarding its clinical applicability. We used a technically modified conventional ultrasound system and analyzed the high-frequency ultrasonic data with a computer program. The first patient-based results suggest that elastography allows an accurate measurement of tumor size and localization in contrast to conventional transrectal ultrasound. Elastography visualizes different tissue elasticities to distinguish benign and cancerous tissue. Thus, we were able to even correctly classify prostate cancer lesions which are iso- or hyperechoic in B-mode sonography.

Ultrasonic tissue characterization for prostate diagnostics: spectral parameters vs. texture parameters.

An ultrasonic multi-feature tissue characterizing system for the detection of prostate cancer is presented. The system is based on the processing of radio frequency (RF) ultrasonic echo data. Data from 100 patients was acquired in a clinical study. Parameters are extracted from the RF echo data and classified using two adaptive network-based fuzzy inference systems (FIS) working in parallel as a nonlinear classifier. Next to spectral parameters, conventional texture parameters are calculated using demodulated and log-compressed echo data. In the first approach, the classifier is trained on both, spectral and texture parameters. In the second approach, the classifier is only trained on texture parameters. Classification results of both approaches are compared and it is demonstrated, that only the use of spectral parameters yields satisfying classification results. Results of a minimum distance classifier (MDC) are presented for comparison with the fuzzy inference system. For the final fuzzy inference systems used in this approach, the area under the ROC curve is between 84% and 86% for the combined approach and between 70% and 74% for the approach based on texture parameters only.