Journal of Urology最新文献

Purpose: Conventional prostate magnetic resonance imaging has limited accuracy for clinically significant prostate cancer (csPCa). We performed diffusion basis spectrum imaging (DBSI) prior to biopsy and applied artificial intelligence models to these DBSI metrics to predict csPCa.

Materials and methods: Between February 2020 and March 2024, 241 patients underwent prostate MRI that included conventional and DBSI-specific sequences prior to prostate biopsy. We used artificial intelligence models with DBSI-metrics as input classifiers and the biopsy pathology as the ground truth. The DBSI-based model was compared with available biomarkers (PSA, PSA density, and PI-RADS) for risk discrimination of csPCa defined as Gleason score >7.

Results: The DBSI-based model was an independent predictor of csPCa (OR 2.04, 95%CI 1.52-2.73, p<0.01), as were PSA density (OR 2.02, 95%CI 1.21-3.35, p=0.01) and PI-RADS classification (OR 4.00, 95%CI 1.37-11.6 for PI-RADS 3, p=0.01; OR 9.67, 95%CI 2.89-32.7, for PI-RADS 4-5, p<0.01), adjusting for age, family history, and race. Within our dataset, the DBSI-based model alone performed similarly to PSA density + PI-RADS (AUC 0.863 vs. 0.859, p=0.89), while the combination of the DBSI-based model + PI-RADS had the highest risk discrimination for csPCa (AUC 0.894, p<0.01). A clinical strategy using the DBSI-based model for patients with PI-RADS 1-3 could have reduced biopsies by 27% while missing 2% of csPCa (compared to biopsy for all).

Conclusions: Our DBSI-based artificial intelligence model accurately predicted csPCa on biopsy and can be combined with PI-RADS to potentially reduce unnecessary prostate biopsies.

Purpose: We aimed to determine whether implementation of clinical decision support (CDS) tool integrated into the electronic health record (EHR) of a multi-site academic medical center increased the proportion of patients with American Urological Association (AUA) "high risk" microscopic hematuria (MH) who receive guideline concordant evaluations.

Materials and methods: We conducted a two-arm cluster randomized quality improvement project in which 202 ambulatory sites from a large health system were randomized to either have their physicians receive at time of test results an automated CDS alert for patients with 'high-risk' MH with associated recommendations for imaging and cystoscopy (intervention) or usual care (control). Primary outcome was met if a patient underwent both imaging and cystoscopy within 180 days from MH result. Secondary outcomes assessed individual completion of imaging, cystoscopy or placement of imaging orders.

Results: There were 917 patients randomized to intervention (n=476) or control (n=441) arms between October-December 2021. The percentage of eligible patients for whom the alert correctly triggered in the intervention arm was 83%. Primary outcome was achieved in 0.6% vs. 0.9% (RR: 0.69; 95% CI 0.15, 3.10) of patients in the intervention and control arms, respectively. Patients in the intervention and control groups had similar rates of completed imaging (17.7% vs. 14.7%) and cystoscopy (1.5% vs. 0.9%). Those in the intervention arm had a higher likelihood of CT urogram order (5.5% vs. 1.1%, p=0.003) and a non-significant increase in urology evaluation (11.1% vs. 7.5%, p=0.09).

Conclusion: Implementing an EHR-integrated CDS tool to promote evaluation of patients with high-risk MH did not lead to improvements in patient completion of a full guideline-concordant evaluation. The development of an algorithm to trigger a CDS alert was demonstrated to be feasible and effective. Further multi-level assessment of barriers to evaluation are necessary to continue to improve the approach to evaluating high risk patients with MH.

Purpose: Clinical variables alone have limited ability to determine which patients will have recurrence after radical prostatectomy (RP). We evaluated the ability of locked multimodal artificial intelligence (MMAI) algorithms trained on prostate biopsy specimens to predict prostate cancer specific mortality (PCSM) and overall survival (OS) among patients undergoing radical prostatectomy with digitized RP specimens.

Materials and methods: The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Randomized Controlled Trial randomized subjects from 1993-2001 to cancer screening or control. A subset of patients who underwent RP with available digitized histopathological images and subsequent survival data were identified. Distant metastasis (DM) and PCSM MMAIs originally trained on biopsy slides for patients undergoing radiation were evaluated for prediction of PCSM and OS. Cox proportional hazards modeling and Kaplan Meier survival curve analysis were utilized.

Results: 1032 patients who underwent RP with median follow up of 17 years (IQR 14.3, 19.3 years) were identified. MMAI algorithms for PCSM and DM both predicted PCSM (HR 2.31, 95% confidence interval [CI] 1.6-3.35, p<0.001, and HR 1.96, 95% CI 1.35-2.85, p<0.001, respectively). Similarly, DM and PCSM MMAI predicted OS (HR 1.22, 95% CI 1.01-1.47, p=0.04 and HR 1.19, 95% CI 1.02-1.4, p=0.03).

Conclusions: Locked MMAI algorithms previously developed and validated on biopsy specimens from patients undergoing radiation for prostate cancer successfully predicted clinical outcomes when applied to RP specimens from patients treated with surgery. MMAI models and other biomarkers may help select patients who may benefit from post-operative treatment intensification with androgen deprivation therapy or radiation.

Purpose: The 18-gene MyProstateScore 2.0 (MPS2) test was previously validated for detection of grade group ≥ 2 (GG ≥ 2) prostate cancer using post-digital rectal examination (DRE) urine. To improve ease of testing, we validated MPS2 using first-catch, non-DRE urine.

Materials and methods: Patients provided first-catch urine before biopsy. MPS2 values were calculated using previously validated models differing only by extent of clinical data included biomarkers alone (BA; no clinical data), biomarkers and clinical factors (BA + CF), and biomarkers, clinical factors, and prostate volume (BA + CF + PV). The primary outcome was GG ≥ 2 cancer on biopsy. MPS2 performance and clinical consequences of testing were compared with PSA and the Prostate Cancer Prevention Trial risk calculator (PCPTrc).

Results: The cohort included 266 men with median PSA 6.6 ng/mL (IQR, 4.9-9.1) of whom 103 (39%) had GG ≥ 2 cancer on biopsy. The AUC for GG ≥ 2 cancer was 57% for PSA, 62% for PCPTrc, and 71%, 74%, and 77% for MPS2 models. Under a testing approach detecting > 90% of GG ≥ 2 cancers, MPS2 testing would have avoided 36% to 42% of unnecessary biopsies, as compared with 13% using the PCPTrc. In patients with a prior negative biopsy, MPS2 testing would have avoided 44% to 53% of repeat biopsies, as compared with only 2.6% using PCPTrc.

Conclusions: Using first-catch urine, MPS2 meaningfully improved the proportion of biopsies avoided relative to PCPTrc while maintaining highly sensitive detection of GG ≥ 2 cancer. Non-DRE testing provides a convenient, objective, and highly accurate testing option to reduce the need for imaging and biopsy in men with elevated PSA.