ArticleCancer management and research2020
Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer.
Article in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.Insights into imaging · 2023Review
- Machine Learning-Based Prediction of Pathological Upgrade From Combined Transperineal Systematic and MRI-Targeted Prostate Biopsy to Final Pathology: A Multicenter Retrospective Study.Frontiers in oncology · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies. PATIENTS AND
methodsWe retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostate-specific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator.
resultsA total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit.
conclusionWe developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients.
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