A positive correlation was found between contamination of the culture medium and microbial colonization of the conjunctival swabs, Nevertheless, microbial colonization of the conjunctiva was high and contamination of the culture medium was relatively low. For the microbial contamination rate of the donated corneas in the medium, conjunctival disinfection time with iodine solution before explantation of the corneoscleral disc and the addition of antibiotics to the culture medium seem to play a protective role.

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CSPC was defined as either Gleason grade group 2 or greater, or grade group 1 with more than 1/3 of total cores positive. Univariate Chi square, logistic regression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the association between PSAD values and CSPC. RESULTS: For all mp-MRI in our patient cohort PPV was 54%, NPV 85%, Sn 95% and Sp 27%. Of the 1857 men with PI-RADS scores of 1 or 2 on mp-MRI, 200 men were followed by our practice and had undergone subsequent biopsy. Average age was 67 years, average PSA 7.3 ng/mL, average prostate size 71 mL, and average PSAD .13. Overall cancer detection rate on biopsy was 22.5% (45 patients) and for CSPC 8% (16 patients). On logistic regression analysis, PSAD was found to be a statistically significant predictor of CSPC (p[<.001), with the area under the ROC being .68 (Fig 1). Using a cutoff of 0.1 NPV is 96%, PPV 11%, Sn 75% and Sp 51%. Using a cutoff of 0.2 NPV is 94%, PPV 22.2%, Sn 37%, and Sp 88% and 36% PPV for any cancer on biopsy. CONCLUSIONS: Similar to prior reports, a negative MRI will miss <10% of CSPC. PSAD appears to have statistically significant prognostic value for patients with a negative MRI. Based on our data cohort, patients with negative mp-MRI and a PSAD score above 0.2 still have a 22% risk of CSPC and 36% of any cancer. Our results strongly confirm the utility of PSAD to stratify which patients with a negative prostate mp-MRI should undergo a prostate biopsy.

Prediction of the presence of extracapsular extension (ECE) of prostate cancer (PCa) before surgery is of paramount importance to tailor the amount of nervesparing during radical prostatectomy (RP). A novel nomogram to predict ECE has been recently developed with the integration of a multiparametric magnetic resonance imaging (mpMRI) derived variable (Martini et al, BJUI 2018, 1-9). Authors defined the "presence of ECE" as the loss or irregularity of the capsule, whereas contact, bulge or abutment are considered as negative for ECE.We aimed to externally validate this nomogram on 137 prostatic lobes from 106 patients undergoing mpMRI-targeted biopsy plus saturation sampling.METHODS: We applied the model from Martini to the most recent cases of PCa patients (n[106) with a positive mpMRI submitted to RP. PCa was diagnosed in all cases by mpMRI-targeted plus systematic saturation biopsy. According to Martini 0 s model, we considered only lobes with a positive biopsy (137). The primary endpoint was to perform an EV; the secondary endpoint was to explore the incremental role of the mpMRI-variable added to conventional clinical-pathological ones.AUC was used to assess the nomogram 0 s discriminative performance. The comparison between AUCs of two-nested models was performed using the test of Heller.RESULTS: The AUC at EV was 67.6% (95%CI:57.4%-77.8%). Sensitivity and specificity at the 20% cutoff suggested by Authors were 53.6% (95%CI:33.9%-72.5%) and 77.1% (95%CI:68%-84.6%), respectively. The model showed a poor calibration with tendency towards underestimation. As far as the secondary endpoint, the tool without mpMRI-variable showed a discrimination of 66.5% (95% CI:56.5%-76.7%) and the difference between the two AUCs was not statistically significant (p[0.113).CONCLUSIONS: On External validation, the predictive performance of Martini 0 s model seems to be suboptimal. A possible explanation could be the subjective approach of ECE depiction at mpMRI used by Authors; actually, the ideal variable predicting ECE from imaging is far to be defined.Further EV studies on larger sample size are required to definitely assess the generalizability of this nomogram.