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Published on: December 15, 2014
Quantitative 3-T Multiparametric MRI Parameters as Predictors of Aggressive Prostate Cancer.
Daniel Hyeong Seok Kim1, Ida Sonni1, Tristan Grogan1
1From the Departments of Radiological Sciences (D.H.S.K., I.S., V.M., W.H., K.H.S., D.S.L., S.S.R.), Medicine Statistics Core (T.G.), Pathology (A.S.), and Urology (R.E.R., S.S.R.), David Geffen School of Medicine at UCLA, 885 Tiverton Dr, Los Angeles, CA 90095.
Quantitative 3-T multiparametric MRI (mpMRI) parameters can predict aggressive prostate cancer (PCa) with large cribriform pattern (LCP) and intraductal carcinoma (IDC). These MRI findings correlate with histopathology, aiding in the diagnosis of aggressive PCa.
Area of Science:
- Radiology
- Oncology
- Pathology
Background:
- Accurate prediction of aggressive prostate cancer (PCa) is crucial for treatment planning.
- Large cribriform pattern (LCP) and intraductal carcinoma (IDC) are indicators of aggressive PCa.
- Whole-mount histopathology (WMHP) is the gold standard for PCa assessment.
Purpose of the Study:
- To identify quantitative 3-Tesla multiparametric MRI (mpMRI) parameters that correlate with and predict aggressive PCa.
- To assess the presence of LCP and IDC using mpMRI.
- To correlate mpMRI findings with WMHP in patients with PCa.
Main Methods:
- Retrospective analysis of 130 patients with 141 PCa lesions who underwent preoperative 3-T mpMRI and radical prostatectomy.
- WMHP was used to categorize lesions into three subcohorts based on aggressiveness (LCP and IDC presence).
- Quantitative mpMRI parameters including apparent diffusion coefficient (ADC) and initial area under the curve (iAUC) were derived and analyzed.
Main Results:
- Mean ADC values decreased with increasing PCa aggressiveness (P = .007).
- Mean iAUC values increased with increasing PCa aggressiveness (P = .04).
- ADC showed negative correlation (P = .004), while rate constant and iAUC showed positive correlation (P = .048 and P = .04) with PCa aggressiveness.
Conclusions:
- Quantitative 3-T mpMRI parameters significantly correlate with PCa aggressiveness.
- mpMRI parameters can help predict the presence of LCP and IDC.
- These findings support the use of quantitative mpMRI for non-invasive assessment of aggressive PCa.
