Preoperative prostate biopsy and multiparametric magnetic resonance imaging: Reliability in detecting prostate cancer

Purpose The aim of the study was to analyse and compare the ability of multiparametric magnetic resonance imaging (mp–MRI) and prostate biopsy (PB) to correctly identify tumor foci in patients undergoing radical prostatectomy (RP) for prostate cancer (PCa). Materials and Methods 157 patients with clinically localised PCa with a PSA <10 ng/mL and a negative DRE diagnosed on the first (12 samples, Group A) or second (18 samples, Group B) PB were enrolled at our institution. All patients underwent mp-MRI with T2-weighted images, diffusion-weighted imaging, dynamic contrast enhanced-MRI prior to RP. A map of comparison describing each positive biopsy sample was created for each patient, with each tumor focus shown on the MRI and each lesion present on the definitive histological examination in order to compare tumor detection and location. The sensitivity of mp-MRI and PB for diagnosis was compared using Student’s t-test. The ability of the two exams to detect the prevalence of Gleason pattern 4 in the identified lesions was compared using a chi-square test. Results Overall sensitivity of PB and mp-MRI to identify tumor lesion was 59.4% and 78.9%, respectively (p<0.0001). PB missed 144/355 lesions, 59 of which (16.6%) were significant. mp-MRI missed 75/355 lesions, 12 of which (3.4%) were significant. No lesions with a GS≥8 were missed. Sensitivity of PB and mp-MRI to detect the prevalence of Gleason pattern 4 was 88.2% and 97.4%, respectively. Conclusions mp-MRI seems to identify more tumor lesions than PB and to provide more information concerning tumor characteristics

Untargeted metabolomic profile for the detection of prostate carcinoma-preliminary results from PARAFAC2 and PLS-DA Models

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares&ndash;discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach

[-2]proPSA versus ultrasensitive PSA fluctuations over time in the first year from radical prostatectomy, in an high-risk prostate cancer population: A first report

BACKGROUND: [−2]proPSA and its derivatives have an higher diagnostic accuracy than PSA in predicting prostate cancer (PCa). In alternative to PSA, ultrasensitive PSA (uPSA) and [−2]proPSA could be potentially useful in recurrent disease detection. This research focused on [−2]proPSA and uPSA fluctuations over time and their possible clinical and pathological determinants, in the first year after RP. METHODS: A cohort of 106 consecutive patients, undergoing RP for high-risk prostate cancer (pT3/pT4 and/or positive margins), was enrolled. No patient received either preoperative/postoperative androgen deprivation therapy or immediate adjuvant RT, this latter for patient choice. [−2]proPSA and uPSA were measured at 1, 3, 6, 9, 12 months after RP; their trends over time were estimated by the mixed-effects linear model. The uPSA relapse was defined either as 3 rising uPSA values after nadir or 2 consecutive uPSA >0.2 ng/ml after RP. RESULTS: The biochemical recurrence (BCR) rate at 1 year after RP was either 38.6 % (in case of 3 rising uPSA values) or 34.9 % (in case of PSA >0.2 ng/ml after nadir), respectively. The main risk factors for uPSA fluctuations over time were PSA at diagnosis >8 ng/ml (p = 0.014), pT (p = 0.038) and pN staging (p = 0.001). In turn, PSA at diagnosis >8 ng/ml (p = 0.012) and pN (p < 0.001) were the main determinants for [−2]proPSA trend over time. In a 39 patients subgroup, uPSA decreased from month 1 to 3, while [−2]proPSA increased in 90 % of them; subsequently, both uPSA and [−2]proPSA increased in almost all cases. The [−2]proPSA trend over time was independent from BCR status either in the whole cohort as well in the 39 men subgroup. CONCLUSIONS: Both uPSA and [−2]proPSA had independent significant fluctuations over time. PSA at diagnosis >8 ng/ml and pathological staging significantly modified both these trends over time. Since BCR was not confirmed as determinant of [−2]proPSA fluctuations, its use as marker of early biochemical relapse may not be actually recommended, in an high-risk prostate cancer patients population