Neural network prediction of prostate tissue composition based on magnetic resonance imaging analysis. A pilot study

OBJECTIVE: To develop a neural network model that estimates prostate histology using magnetic resonance imaging (MRI). STUDY DESIGN: Fifty-three men with lower urinary tract symptoms (average age = 63.8 +/- 8.9 years) underwent a prostate MRI (T2) and sextant biopsy of the prostate. Masson Trichome and immunohistochemical prostate-specific antigen staining of the biopsy material were used to calculate the amount of stroma and epithelium in the inner gland (central plus transition zone). MRIs were normalized to the mean intensity of the obturator internus muscle for comparative analyses. Gray scale and texture features were extracted from the inner gland in the midsection transverse MRI slice. Clinical and image variables were used in two neural networks predicting a high amount of stroma and a high amount of epithelium, respectively. RESULTS: The positive and negative predictive values of the stroma and epithelium neural networks were 95%, 69% and 65%, 92%, respectively. CONCLUSION: These data suggest that the combined use of these neural networks may predict patient response to medical therapy targeting prostatic stroma or epithelium.