Objective: The aim of this study was to assess the value of sonoelastography (SE) for prostate cancer detection in comparison with systematic biopsy findings. Material and methods: Four hundred and ninety two PSA screening volunteers (mean age: 61.9 +/- 8.6) with an total PSA > 1.25 ng/mL and a free to total PSA ration of <18% underwent SE of the prostate before 10 core systematic prostate biopsy. Tissue elasticity of the peripheral zone was investigated only. Tissue elasticity was displayed from red (soft) to green (intermediate) and to blue (hard). Only hard lesions (blue) were considered to be suspicious for prostate cancer. The peripheral zone of the prostate was divided in 3 regions on each side: base, mid-gland, apex. A different investigator performed systematic biopsy, and the biopsy findings were compared with the SE findings. Results: In 125 of 492 patients (25.4%) systematic biopsy demonstrated prostate cancer. Cancer was detected in 321 of 2952 (11%) outer gland areas (74 in the base, 106 in the mid-gland, 141 in the apex). The Gleason score ranged from 3 to 10 (mean: 6.5). In SE 533 of 2952 (18.1%) suspicious areas were detected and 258 of these areas (48.4%) showed cancer. Most of the false-positive findings (275/533 areas; 51.6%) were associated with chronic inflammation and atrophy especially at the basal prostate areas. The sensitivity by entire organ was calculated with 86% and the specificity 72%. The analysis by outer gland areas showed the highest sensitivity in the apex (79%). The specificity by outer gland areas ranged between 85% and 93%. The correlation between SE findings and biopsy results was high (p < 0.001). Conclusion: Sonoelastography findings showed a good correlation with the systematic biopsy results. The best sensitivity and specificity was found in the apex region. Sonoelastography seems to offer a new approach for differentiation of tissue stiffness of the prostate and may therefore improve prostate cancer detection. (C) 2007 Elsevier Ireland Ltd. All rights reserved.
Useful keywords (using NLM MeSH Indexing)
Predictive Value of Tests
Sensitivity and Specificity
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