51 research outputs found

    Dynamic 11C-Choline PET / CT for the primary diagnosis of prostate cancer

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    ABSTRACT Objectives: To test the ability of dynamic 11C-PET / CT to discriminate cancerous tissue from background tissue in patients with localized prostate cancer. Materials and Methods: Twenty-four consecutive patients with prostate cancer were prospectively evaluated with dynamic 11C-choline PET / CT prior to radical prostatectomy. The PET / CT scan was divided into 18 sequences of 5 seconds each, followed by 9 sequences of 60 seconds each. Whole-mount sections of harvested prostates served as reference standards. Volumes of interest were positioned on the dynamic PET / CT images and the following quantitative variables were calculated: perfusion coefficient (K1), washout constant (K2), area under the curve (AUC) at 175 and 630 seconds, and average and maximum standardized uptake values (SUVavg, and SUVmax). Wilcoxon signed-ranks test was used to compare benign and cancerous areas of the prostate. Results: Areas of cancerous tissue were characterized by higher SUVavg and SUVmax than areas of benign tissue (3.67 ± 2.7 vs. 2.08 ± 1.3 and 5.91 ± 4.4 vs. 3.71 ± 3.7, respectively, P < 0.001), in addition to a higher K1 (0.95 ± 0.58 vs. 0.43 ± 0.24, P < 0.001) and greater cumulative tracer uptake, represented by the AUC at 175 and 630 seconds (P <0.001). No associations were found between dynamic parameters and preoperative prostate specific antigen level or Gleason score. Conclusions: In this pilot study, 11C-choline PET / CT demonstrated increased tracer uptake with higher values of static and dynamic parameters in areas of prostate cancer compared to areas of benign tissue. Larger studies are warranted to validate these results and examine the potential applicability of 11C-choline dynamic PET / CT for the diagnosis of prostate cancer

    Influence on the Slope on the Character of Surface Soils and the Yield of Plants

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    textabstractIntroduction: In this study, we estimated the time from first detectable prostate-specific antigen (PSA) following radical prostatectomy (RP) to commonly used definitions of biochemical recurrence (BCR). We also identified the predictors of time to BCR. Methods: We identified subjects who underwent a RP and had an undetectable PSA after surgery followed by at least 1 detectable PSA between 2000 and 2011. The primary outcome was time to BCR (PSA.0.2 and successive PSA.0.2) and prediction of the rate of PSA rise. Outcomes were calculated using a competing risk analysis, with univariable and multivariable Fine and Grey models. We employed a mixed effect model to test clinical predictors that are associated with the rate of PSA rise. Results: The cohort included 376 patients. The median follow-up from surgery was 60.5 months (interquartile range [IQR] 40.8. 91.5) and from detectable PSA was 18 months (IQR 11.32). Only 45.74% (n = 172) had PSA values.0.2 ng/mL, while 15.16% (n = 57) reached the PSA level of.0.4 ng/mL and rising. On multivariable analysis, the values of the first detectable PSA and pathologic Gleason grade 8 or higher were consistently independent predictors of time to BCR. In the mixed effect model rate, the PSA rise was associated with time from surgery to first detectable PSA, Gleason score, and prostate volume. The main limitation of this study is the large proportion of patients that received treatment without reaching BCR. It is plausible that shorter estimated median times would occur at a centre that does not use salvage therapy at such an early state. Conclusion: The time from first detectable PSA to BCR may be lengthy. Our analyses of the predictors of the rate of PSA rise can help determine a personalized approach for patients with a detectable PSA after surgery

    Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

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    In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ1) was lower in both peripheral-zone (p &lt; 0.0001) and central-gland (p &lt; 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection
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