Context: MRI-targeted prostate biopsy (MRI-TB) may be an alternative to systematic biopsy for diagnosing prostate cancer.Objective: The primary aims of this systematic review and meta-analysis were to compare the detection rates of clinically significant and clinically insignificant cancer by MRI-TB to systematic biopsy in men undergoing prostate biopsy to identify prostate cancer. Evidence acquisition: A literature search was conducted using the PubMed, Embase, Web of Science, Cochrane library and Clinicaltrials.gov databases. We included prospective and retrospective paired studies where the index test was MRI-TB and the comparator test was systematic biopsy. We also included randomized controlled trials (RCTs) if one arm included MRI-TB and another arm included systematic biopsy. The risk of bias was assessed using a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. In addition, the Cochrane risk of bias 2.0 tool was used for RCTs.Evidence Synthesis: We included 68 studies with a paired design and 8 RCTs, comprising a total of 14709 men who received either both MRI-TB and systematic biopsy or were randomized to receive one of the tests. MRI-TB detected more men with clinically significant cancer than systematic biopsy (Detection ratio (DR) 1.16 [95% CI 1.09-1.24], p < 0.0001) and fewer men with clinically insignificant cancer than systematic biopsy (DR 0.66 [95% CI 0.57-0.76], p < 0.0001). The proportion of cores positive for cancer was greater for MRI-TB than systematic biopsy, relative risk 3.17 [95% CI 2.82-3.56], p<0.0001.Conclusions: MRI-TB is an attractive alternative diagnostic strategy to systematic biopsy. Patient summary:We evaluated the published literature, comparing two methods of diagnosing prostate cancer. We found that biopsies targeted to suspicious areas on an MRI (MRI-Targeted biopsy) were better at detecting prostate cancer that needs to be treated and at avoiding the diagnosis of disease that doesn't need treatment than the traditional systematic biopsy.

Background Accurate prognostication is crucial in treatment decisions made for men diagnosed with non-metastatic prostate cancer. Current models rely on prespecified variables, which limits their performance. We aimed to investigate a novel machine learning approach to develop an improved prognostic model for predicting 10-year prostate cancer-specific mortality and compare its performance with existing validated models. Methods We derived and tested a machine learning-based model using Survival Quilts, an algorithm that automatically selects and tunes ensembles of survival models using clinicopathological variables. Our study involved a US population-based cohort of 171 942 men diagnosed with non-metastatic prostate cancer between Jan 1, 2000, and Dec 31, 2016, from the prospectively maintained Surveillance, Epidemiology, and End Results (SEER) Program. The primary outcome was prediction of 10-year prostate cancer-specific mortality. Model discrimination was assessed using the concordance index (c-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with nine other prognostic models in clinical use, and decision curve analysis was done. Findings 647 151 men with prostate cancer were enrolled into the SEER database, of whom 171 942 were included in this study. Discrimination improved with greater granularity, and multivariable models outperformed tier-based models. The Survival Quilts model showed good discrimination (c-index 0•829, 95% CI 0•820-0•838) for 10-year prostate cancer-specific mortality, which was similar to the top-ranked multivariable models: PREDICT Prostate (0•820, 0•811-0•829) and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram (0•787, 0•776-0•798). All three multivariable models showed good calibration with low Brier scores (Survival Quilts 0•036, 95% CI 0•035-0•037; PREDICT Prostate 0•036, 0•035-0•037; MSKCC 0•037, 0•035-0•039). Of the tier-based systems, the Cancer of the Prostate Risk Assessment model (c-index 0•782, 95% CI 0•771-0•793) and Cambridge Prognostic Groups model (0•779, 0•767-0•791) showed higher discrimination for predicting 10-year prostate cancer-specific mortality. c-indices for models from the National Comprehensive Cancer Care Network, Genitourinary Radiation Oncologists of Canada, American Urological Association, European Association of Urology, and National Institute for Health and Care Excellence ranged from 0•711 (0•701-0•721) to 0•761 (0•750-0•772). Discrimination for the Survival Quilts model was maintained when stratified by age and ethnicity. Decision curve analysis showed an incremental net benefit from the Survival Quilts model compared with the MSKCC and PREDICT Prostate models currently used in practice. Interpretation A novel machine learning-based approach produced a prognostic model, Survival Quilts, with discrimination for 10-year prostate cancer-specific mortality similar to the top-ranked prognostic models, using only standard clinicopathological variables. Future integration of additional data will likely ...

BackgroundPrognostic stratification is the cornerstone of management in nonmetastatic prostate cancer (PCa). However, existing prognostic models are inadequate—often using treatment outcomes rather than survival, stratifying by broad heterogeneous groups and using heavily treated cohorts. To address this unmet need, we developed an individualised prognostic model that contextualises PCa-specific mortality (PCSM) against other cause mortality, and estimates the impact of treatment on survival.Methods and findingsUsing records from the United Kingdom National Cancer Registration and Analysis Service (NCRAS), data were collated for 10,089 men diagnosed with nonmetastatic PCa between 2000 and 2010 in Eastern England. Median follow-up was 9.8 years with 3,829 deaths (1,202 PCa specific). Totals of 19.8%, 14.1%, 34.6%, and 31.5% of men underwent conservative management, prostatectomy, radiotherapy (RT), and androgen deprivation monotherapy, respectively. A total of 2,546 men diagnosed in Singapore over a similar time period represented an external validation cohort. Data were randomly split 70:30 into model development and validation cohorts. Fifteen-year PCSM and non-PCa mortality (NPCM) were explored using separate multivariable Cox models within a competing risks framework. Fractional polynomials (FPs) were utilised to fit continuous variables and baseline hazards. Model accuracy was assessed by discrimination and calibration using the Harrell C-index and chi-squared goodness of fit, respectively, within both validation cohorts. A multivariable model estimating individualised 10- and 15-year survival outcomes was constructed combining age, prostate-specific antigen (PSA), histological grade, biopsy core involvement, stage, and primary treatment, which were each independent prognostic factors for PCSM, and age and comorbidity, which were prognostic for NPCM. The model demonstrated good discrimination, with a C-index of 0.84 (95% CI: 0.82–0.86) and 0.84 (95% CI: 0.80–0.87) for 15-year PCSM in the UK and Singapore validation cohorts, respectively, comparing favourably to international risk-stratification criteria. Discrimination was maintained for overall mortality, with C-index 0.77 (95% CI: 0.75–0.78) and 0.76 (95% CI: 0.73–0.78). The model was well calibrated with no significant difference between predicted and observed PCa-specific (p = 0.19) or overall deaths (p = 0.43) in the UK cohort. Key study limitations were a relatively small external validation cohort, an inability to account for delayed changes to treatment beyond 12 months, and an absence of tumour-stage subclassifications.Conclusions‘PREDICT Prostate’ is an individualised multivariable PCa prognostic model built from baseline diagnostic information and the first to our knowledge that models potential treatment benefits on overall survival. Prognostic power is high despite using only routinely collected clinicopathological information.