Prostate MRI percentage tumor involvement or "PI-RADS percent" as a predictor of adverse surgical pathology.

BACKGROUND: This study assesses magnetic resonance imaging (MRI) prostate % tumor involvement or "PI-RADs percent" as a predictor of adverse pathology (AP) after surgery for localized prostate cancer (PCa). Two separate variables, "All PI-RADS percent" (APP) and "Highest PI-RADS percent" (HPP), are defined as the volume of All PI-RADS 3-5 score lesions on MRI and the volume of the Highest PI-RADS 3-5 score lesion each divided by TPV, respectively. METHOD: An analysis was done of an IRB approved prospective cohort of 557 patients with localized PCa who had targeted biopsy of MRI PIRADs 3-5 lesions followed by RARP from April 2015 to May 2020 performed by a single surgeon at a single center. AP was defined as ISUP GGG ≥3, pT stage ≥T3 and/or LNI. Univariate and multivariable analyses were used to evaluate APP and HPP at predicting AP with other clinical variables such as Age, PSA at surgery, Race, Biopsy GGG, mpMRI ECE and mpMRI SVI. Internal and External Validation demonstrated predicted probabilities versus observed probabilities. RESULTS: AP was reported in 44.5% (n = 248) of patients. Multivariable regression showed both APP (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04-1.14, p = 0.0007) and HPP (OR: 1.10; 95% CI: 1.04-1.16; p = 0.0007) were significantly associated with AP with individual area under the operating curves (AUCs) of 0.6142 and 0.6229, respectively, and AUCs of 0.8129 and 0.8124 when incorporated in models including preoperative PSA and highest biopsy GGG. CONCLUSIONS: Increasing PI-RADS Percent was associated with a higher risk of AP, and both APP and HPP may have clinical utility as predictors of AP in GGG 1 and 2 patients being considered for AS. PATIENT SUMMARY: Using PIRADs percent to predict AP for presurgical patients may help risk stratification, and for low and low volume intermediate risk patients, may influence treatment decisions.

Unified model involving genomics, magnetic resonance imaging and prostate-specific antigen density outperforms individual co-variables at predicting biopsy upgrading in patients on active surveillance for low risk prostate cancer.

BACKGROUND: Active surveillance (AS) is the reference standard treatment for the management of low risk prostate cancer (PCa). Accurate assessment of tumor aggressiveness guides recruitment to AS programs to avoid conservative treatment of intermediate and higher risk patients. Nevertheless, underestimating the disease risk may occur in some patients recruited, with biopsy upgrading and the concomitant potential for delayed treatment. AIM: To evaluate the accuracy of mpMRI and GPS for the prediction of biopsy upgrading during active surveillance (AS) management of prostate cancer (PCa). METHOD: A retrospective analysis was performed on 144 patients recruited to AS from October 2013 to December 2020. Median follow was 4.8 (IQR 3.6, 6.3) years. Upgrading was defined as upgrading to biopsy grade group ≥2 on follow up biopsies. Cox proportional hazard regression was used to investigate the effect of PSA density (PSAD), baseline Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 score and GPS on upgrading. Time-to-event outcome, defined as upgrading, was estimated using the Kaplan-Meier method with log-rank test. RESULTS: Overall rate of upgrading was 31.9% (n = 46). PSAD was higher in the patients who were upgraded (0.12 vs. 0.08 ng/ml2 , p = .005), while no significant difference was present for median GPS in the overall cohort (overall median GPS 21; 22 upgrading vs. 20 no upgrading, p = .2044). On univariable cox proportional hazard regression analysis, the factors associated with increased risk of biopsy upgrading were PSA (HR = 1.30, CI 1.16-1.47, p = <.0001), PSAD (HR = 1.08, CI 1.05-1.12, p = <.0001) and higher PI-RADS score (HR = 3.51, CI 1.56-7.91, p = .0024). On multivariable cox proportional hazard regression analysis, only PSAD (HR = 1.10, CI 1.06-1.14, p = <.001) and high PI-RADS score (HR = 4.11, CI 1.79-9.44, p = .0009) were associated with upgrading. A cox regression model combining these three clinical features (PSAD ≥0.15 ng/ml2 at baseline, PI-RADS Score and GPS) yielded a concordance index of 0.71 for the prediction of upgrading. CONCLUSION: In this study PSAD has higher accuracy over baseline PI-RADS score and GPS score for the prediction of PCa upgrading during AS. However, combined use of PSAD, GPS and PI-RADS Score yielded the highest predictive ability with a concordance index of 0.71.

Multiphoton Microscopy for Identifying Collagen Signatures Associated with Biochemical Recurrence in Prostate Cancer Patients.

Prostate cancer is a heterogeneous disease that remains dormant for long periods or acts aggressively with poor clinical outcomes. Identifying aggressive prostate tumor behavior using current glandular-focused histopathological criteria is challenging. Recent evidence has implicated the stroma in modulating prostate tumor behavior and in predicting post-surgical outcomes. However, the emergence of stromal signatures has been limited, due in part to the lack of adoption of imaging modalities for stromal-specific profiling. Herein, label-free multiphoton microscopy (MPM), with its ability to image tissue with stromal-specific contrast, is used to identify prostate stromal features associated with aggressive tumor behavior and clinical outcome. MPM was performed on unstained prostatectomy specimens from 59 patients and on biopsy specimens from 17 patients with known post-surgery recurrence status. MPM-identified collagen content, organization, and morphological tumor signatures were extracted for each patient and screened for association with recurrent disease. Compared to tumors from patients whose disease did not recur, tumors from patients with recurrent disease exhibited higher MPM-identified collagen amount and collagen fiber intensity signal and width. Our study shows an association between MPM-identified stromal collagen features of prostate tumors and post-surgical disease recurrence, suggesting their potential for prostate cancer risk assessment.