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The stanford prostate cancer calculator: Development and external validation of online nomograms incorporating PIRADS scores to predict clinically significant prostate cancer. | LitMetric

AI Article Synopsis

  • Multiparametric MRI (mpMRI) is effective in detecting clinically significant prostate cancer (CSC), but can produce false positives and negatives; a new nomogram combining mpMRI and clinical info aims to improve risk assessment.* -
  • The study analyzed data from 2,125 men who underwent mpMRI and biopsies, incorporating factors like age, race, PSA density, and PIRADS scores to create a more accurate predictive model called the Stanford Prostate Cancer Calculator (SPCC).* -
  • The SPCC showed strong predictive power and generalizability after being validated with data from multiple institutions and is now available as a free tool for clinical decision-making regarding prostate cancer risk.*

Article Abstract

Background: While multiparametric MRI (mpMRI) has high sensitivity for detection of clinically significant prostate cancer (CSC), false positives and negatives remain common. Calculators that combine mpMRI with clinical variables can improve cancer risk assessment, while providing more accurate predictions for individual patients. We sought to create and externally validate nomograms incorporating Prostate Imaging Reporting and Data System (PIRADS) scores and clinical data to predict the presence of CSC in men of all biopsy backgrounds.

Methods: Data from 2125 men undergoing mpMRI and MR fusion biopsy from 2014 to 2018 at Stanford, Yale, and UAB were prospectively collected. Clinical data included age, race, PSA, biopsy status, PIRADS scores, and prostate volume. A nomogram predicting detection of CSC on targeted or systematic biopsy was created.

Results: Biopsy history, Prostate Specific Antigen (PSA) density, PIRADS score of 4 or 5, Caucasian race, and age were significant independent predictors. Our nomogram-the Stanford Prostate Cancer Calculator (SPCC)-combined these factors in a logistic regression to provide stronger predictive accuracy than PSA density or PIRADS alone. Validation of the SPCC using data from Yale and UAB yielded robust AUC values.

Conclusions: The SPCC combines pre-biopsy mpMRI with clinical data to more accurately predict the probability of CSC in men of all biopsy backgrounds. The SPCC demonstrates strong external generalizability with successful validation in two separate institutions. The calculator is available as a free web-based tool that can direct real-time clinical decision-making.

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Source
http://dx.doi.org/10.1016/j.urolonc.2021.06.004DOI Listing

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