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Optimal size threshold for PIRADSv2 category 5 upgrade and its positive predictive value: is it predictive of “very high” likelihood of clinically-significant cancer?

  • V. Batheja
  • , M. Osman
  • , M. Wynne
  • , D. Nemirovsky
  • , G. Morcos
  • , J. Riess
  • , B. Shin
  • , M. Whalen
  • , S. Haji-Momenian

Research output: Contribution to journalArticlepeer-review

Abstract

AIM: To identify the optimal size metric and threshold for Prostate Imaging Reporting and Data System (PIRADS) 5 upgrade, calculate its positive predictive value (PPV) for clinically-significant prostate cancer (csPCA), and determine if it is indicative of a “very high” likelihood of csPCA. MATERIALS AND METHODS: One hundred and forty-three PIRADS 4 or 5 lesions were evaluated. Lesion diameters were used to calculate lesion volume (LV). Pearson correlation between maximum lesion diameter (MLD) and LV was calculated. Area under the curve (AUC) for discriminating csPCA (Gleason grade ≥ 3 + 4) was calculated using MLD and LV. Optimal size thresholds (using Youden index) and highly predictive size thresholds were identified for the whole prostate (WP), peripheral zone (PZ), and transitional zone (TZ). RESULTS: There was high correlation between MLD and LV (r=0.77–0.81), with comparable AUCs for MLD and LV in the identification of csPCA in the WP (0.73, 0.72), PZ (0.73, 0.73), and TZ (0.79, 0.75). Optimal MLD thresholds were 1.4, 1.4, and 1.6 cm in the WP, PZ, and TZ respectively, with PPVs of 76%, 81%, and 69%, respectively. An MLD threshold of 2.7 cm would be needed in the WP to achieve a PPV approaching 90%, with sensitivity decreasing to 10%. CONCLUSIONS: There is high correlation between MLD and LV with comparable discrimination of csPCA using each. PIRADSv2's 1.5 cm MLD threshold is near the optimal threshold for PIRADS 5 upgrade but has moderate PPV. A much higher threshold would be needed to increase its PPV, with significant sacrifice in sensitivity.

Original languageEnglish (US)
Pages (from-to)e94-e101
JournalClinical Radiology
Volume79
Issue number1
DOIs
StatePublished - Jan 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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