Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features.

PURPOSE To investigate relationships between magnetic resonance (MR) imaging measurements and the underlying composition of normal and malignant prostate tissue. MATERIALS AND METHODS Twenty-four patients (median age, 63 years; age range, 44-72 years) gave informed consent to be examined for this research ethics board-approved study. Before undergoing prostatectomy, patients were examined with T2-weighted, diffusion-weighted, T2 mapping, and dynamic contrast material-enhanced MR imaging at 1.5 T. Maps of apparent diffusion coefficient (ADC), T2, volume transfer constant (K(trans)), and extravascular extracellular space (v(e)) were calculated. Whole-mount hematoxylin-eosin-stained sections were generated and digitized at histologic resolution. Percentage areas of tissue components (nuclei, cytoplasm, stroma, luminal space) were measured by using image segmentation. Corresponding regions on MR images and histologic specimens were defined by using anatomically defined segments in peripheral zone (PZ) and central gland tissue. Cancer and normal PZ regions were identified at histopathologic analysis. Each MR parameter-histologic tissue component pair was assessed by using linear mixed-effects models, and cancer versus normal PZ values were compared by using nonparametric tests. RESULTS ADC and T2 were inversely related to percentage area of nuclei and percentage area of cytoplasm and positively related to percentage area of luminal space (P < or = .01). These trends were reversed for K(trans) (P < .001). K(trans) had a significantly negative (P = .01) slope versus percentage area of stroma, and v(e) had a positive (P = .008) slope versus percentage area of stroma. The v(e) was inversely proportional to the percentage area of nuclei (P = .05). All MR imaging parameters (P < or = .05) and the percentage areas of all tissue components (P < or = .001) except stroma (P > .48) were significantly different between cancer and normal PZ tissue. CONCLUSION MR imaging-derived parameters measured in the prostate were significantly related to the proportion of specific histologic components that differ between normal and malignant PZ tissue. These relationships may help define imaging-related histologic prognostic parameters for prostate cancer.

[1]  Bin Wang,et al.  Diffusion‐weighted imaging of prostate cancer: Correlation between apparent diffusion coefficient values and tumor proliferation , 2009, Journal of magnetic resonance imaging : JMRI.

[2]  Gary Liney,et al.  Correlation of diffusion‐weighted magnetic resonance data with cellularity in prostate cancer , 2009, BJU international.

[3]  H. Hricak,et al.  Correlation of MR imaging and MR spectroscopic imaging findings with Ki-67, phospho-Akt, and androgen receptor expression in prostate cancer. , 2009, Radiology.

[4]  Georg Heinze,et al.  Morphometric signature differences in nuclei of Gleason pattern 4 areas in Gleason 7 prostate cancer with differing primary grades on needle biopsy. , 2009, The Journal of urology.

[5]  Olivier Rouvière,et al.  Evaluation of T2-weighted and dynamic contrast-enhanced MRI in localizing prostate cancer before repeat biopsy , 2009, European Radiology.

[6]  Katsuyoshi Ito,et al.  Apparent diffusion coefficient values in peripheral and transition zones of the prostate: Comparison between normal and malignant prostatic tissues and correlation with histologic grade , 2008, Journal of magnetic resonance imaging : JMRI.

[7]  Gerardo Fernandez,et al.  Systems pathology approach for the prediction of prostate cancer progression after radical prostatectomy. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  N M deSouza,et al.  Diffusion-weighted magnetic resonance imaging: a potential non-invasive marker of tumour aggressiveness in localized prostate cancer. , 2008, Clinical radiology.

[9]  Jason A Koutcher,et al.  Prostate cancer: identification with combined diffusion-weighted MR imaging and 3D 1H MR spectroscopic imaging--correlation with pathologic findings. , 2008, Radiology.

[10]  Y. Liu,et al.  Dynamic contrast-enhanced MRI of benign prostatic hyperplasia and prostatic carcinoma: correlation with angiogenesis. , 2008, Clinical radiology.

[11]  Masoom A Haider,et al.  Combined T2-weighted and diffusion-weighted MRI for localization of prostate cancer. , 2007, AJR. American journal of roentgenology.

[12]  R. Lenkinski,et al.  3T MR of the prostate: Reducing susceptibility gradients by inflating the endorectal coil with a barium sulfate suspension , 2007, Magnetic resonance in medicine.

[13]  M. Kattan,et al.  The utility of magnetic resonance imaging and spectroscopy for predicting insignificant prostate cancer: an initial analysis , 2007, BJU international.

[14]  N. deSouza,et al.  MAGNETIC RESONANCE IMAGING IN PROSTATE CANCER : VALUE OF APPARENT DIFFUSION COEFFICIENTS FOR IDENTIFYING MALIGNANT NODULES , 2010 .

[15]  M. Reiser,et al.  Per-sextant localization and staging of prostate cancer: correlation of imaging findings with whole-mount step section histopathology. , 2007, AJR. American journal of roentgenology.

[16]  D J Collins,et al.  Evaluation of response to treatment using DCE-MRI: the relationship between initial area under the gadolinium curve (IAUGC) and quantitative pharmacokinetic analysis , 2006, Physics in medicine and biology.

[17]  Silvia D. Chang,et al.  Combined diffusion‐weighted and dynamic contrast‐enhanced MRI for prostate cancer diagnosis—Correlation with biopsy and histopathology , 2006, Journal of magnetic resonance imaging : JMRI.

[18]  M. Kattan,et al.  Correlation of proton MR spectroscopic imaging with gleason score based on step-section pathologic analysis after radical prostatectomy. , 2005, Radiology.

[19]  Takeo Ishigaki,et al.  Differentiation of noncancerous tissue and cancer lesions by apparent diffusion coefficient values in transition and peripheral zones of the prostate , 2005, Journal of magnetic resonance imaging : JMRI.

[20]  Vassilis Poulakis,et al.  Preoperative neural network using combined magnetic resonance imaging variables, prostate-specific antigen, and gleason score for predicting prostate cancer biochemical recurrence after radical prostatectomy. , 2004, Urology.

[21]  Michael J. Wilson,et al.  Microvessel density as a molecular marker for identifying high-grade prostatic intraepithelial neoplasia precursors to prostate cancer. , 2004, Experimental and molecular pathology.

[22]  K. Hosseinzadeh,et al.  Endorectal diffusion‐weighted imaging in prostate cancer to differentiate malignant and benign peripheral zone tissue , 2004, Journal of magnetic resonance imaging : JMRI.

[23]  Mark Rijpkema,et al.  Combined quantitative dynamic contrast‐enhanced MR imaging and 1H MR spectroscopic imaging of human prostate cancer , 2004, Journal of magnetic resonance imaging : JMRI.

[24]  H. Hricak,et al.  Chronic prostatitis: MR imaging and 1H MR spectroscopic imaging findings--initial observations. , 2004, Radiology.

[25]  H. Schlemmer,et al.  Can pre-operative contrast-enhanced dynamic MR imaging for prostate cancer predict microvessel density in prostatectomy specimens? , 2004, European Radiology.

[26]  Henkjan J Huisman,et al.  Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. , 2003, Radiology.

[27]  B. Rutt,et al.  Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state , 2003, Magnetic resonance in medicine.

[28]  B. Issa,et al.  In vivo measurement of the apparent diffusion coefficient in normal and malignant prostatic tissues using echo‐planar imaging , 2002, Journal of magnetic resonance imaging : JMRI.

[29]  Peter Gibbs,et al.  Comparison of quantitative T2 mapping and diffusion‐weighted imaging in the normal and pathologic prostate , 2001, Magnetic resonance in medicine.

[30]  J Kurhanewicz,et al.  Sextant localization of prostate cancer: comparison of sextant biopsy, magnetic resonance imaging and magnetic resonance spectroscopic imaging with step section histology. , 2000, The Journal of urology.

[31]  D P Dearnaley,et al.  Dynamic contrast enhanced MRI of prostate cancer: correlation with morphology and tumour stage, histological grade and PSA. , 2000, Clinical radiology.

[32]  P. Carroll,et al.  Prostate cancer: localization with three-dimensional proton MR spectroscopic imaging--clinicopathologic study. , 1999, Radiology.

[33]  M. Kattan,et al.  A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. , 1998, Journal of the National Cancer Institute.

[34]  S. Piantadosi,et al.  Correlation of prostate needle biopsy and radical prostatectomy Gleason grade in academic and community settings. , 1997, The American journal of surgical pathology.

[35]  P. Tofts Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.

[36]  J R Thornbury,et al.  Local staging of prostate cancer with endorectal MR imaging: correlation with histopathology. , 1996, AJR. American journal of roentgenology.

[37]  R. Herfkens,et al.  Determining the volume of prostatic carcinoma: value of MR imaging with an external-array coil. , 1993, AJR. American journal of roentgenology.

[38]  P H Bland,et al.  Prostate cancer: correlation of MR images with tissue optical density at pathologic examination. , 1991, Radiology.

[39]  L Axel,et al.  Prostatic carcinoma and benign prostatic hyperplasia: correlation of high-resolution MR and histopathologic findings. , 1989, Radiology.

[40]  D G Mitchell,et al.  The biophysical basis of tissue contrast in extracranial MR imaging. , 1987, AJR. American journal of roentgenology.