Is Likert better than PI-RADS at predicting prostate cancer on MRI and can a mathematical algorithm achieve similar results?


Journal

Acta radiologica (Stockholm, Sweden : 1987)
ISSN: 1600-0455
Titre abrégé: Acta Radiol
Pays: England
ID NLM: 8706123

Informations de publication

Date de publication:
Sep 2023
Historique:
medline: 13 9 2023
pubmed: 13 7 2023
entrez: 13 7 2023
Statut: ppublish

Résumé

Prostate Imaging Reporting & Data System (PI-RADS) is an internationally recognized system to quantify risk of prostate cancer on magnetic resonance imaging (MRI). However, studies have suggested methods to improve predictive accuracy. To assess two different methods that aim to improve the accuracy of PI-RADS scores: a subjective Likert score given by experienced reporters, and an objective Calculated Adjustment of PI-RADS Equivocal Score (CAPES). Five experienced reporters in a quaternary referral unit used a standardized reporting template to prospectively collect PI-RADS and Likert scores for 1467 multiparametric MRI (mpMRI) scans between January 2021 and June 2022. Histology results were recorded for patients who underwent trans-perineal biopsy. The CAPES tool was retrospectively applied to the cases scoring PI-RADS 3. A theoretical standardized biopsy protocol (assuming all patients scoring ≥3 were referred for biopsy) was used to compare the three scoring systems for sensitivity, specificity, and positive predictive value (PPV). Across all reporters, significantly fewer equivocal "3" scores were given using Likert (15.7%) or CAPES (2.2%) compared to PI-RADS (24.1%). Assuming a protocol where all patients scoring ≥3 were biopsied, Likert had a higher specificity (69.0% vs. 54.4%), sensitivity (98.3% vs. 97.7%), and PPV (49.9% vs. 40.3%) than PI-RADS for identifying ISUP ≥2 cancer. The CAPES tool had an even higher specificity (81.4%) and PPV (61.2%) with only a slightly lower sensitivity (93.4%) resulting in 37.1% (n = 316) fewer biopsies than PI-RADS, and 22.4% (n = 155) fewer biopsies than Likert across 1467 patients. Compared to PI-RADS scoring, Likert scoring or CAPES can result in fewer equivocal scores, greater PPV, and fewer unnecessary biopsies.

Sections du résumé

BACKGROUND BACKGROUND
Prostate Imaging Reporting & Data System (PI-RADS) is an internationally recognized system to quantify risk of prostate cancer on magnetic resonance imaging (MRI). However, studies have suggested methods to improve predictive accuracy.
PURPOSE OBJECTIVE
To assess two different methods that aim to improve the accuracy of PI-RADS scores: a subjective Likert score given by experienced reporters, and an objective Calculated Adjustment of PI-RADS Equivocal Score (CAPES).
MATERIAL AND METHODS METHODS
Five experienced reporters in a quaternary referral unit used a standardized reporting template to prospectively collect PI-RADS and Likert scores for 1467 multiparametric MRI (mpMRI) scans between January 2021 and June 2022. Histology results were recorded for patients who underwent trans-perineal biopsy. The CAPES tool was retrospectively applied to the cases scoring PI-RADS 3. A theoretical standardized biopsy protocol (assuming all patients scoring ≥3 were referred for biopsy) was used to compare the three scoring systems for sensitivity, specificity, and positive predictive value (PPV).
RESULTS RESULTS
Across all reporters, significantly fewer equivocal "3" scores were given using Likert (15.7%) or CAPES (2.2%) compared to PI-RADS (24.1%). Assuming a protocol where all patients scoring ≥3 were biopsied, Likert had a higher specificity (69.0% vs. 54.4%), sensitivity (98.3% vs. 97.7%), and PPV (49.9% vs. 40.3%) than PI-RADS for identifying ISUP ≥2 cancer. The CAPES tool had an even higher specificity (81.4%) and PPV (61.2%) with only a slightly lower sensitivity (93.4%) resulting in 37.1% (n = 316) fewer biopsies than PI-RADS, and 22.4% (n = 155) fewer biopsies than Likert across 1467 patients.
CONCLUSIONS CONCLUSIONS
Compared to PI-RADS scoring, Likert scoring or CAPES can result in fewer equivocal scores, greater PPV, and fewer unnecessary biopsies.

Identifiants

pubmed: 37438925
doi: 10.1177/02841851231187135
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2659-2666

Auteurs

William Stevens (W)

Department of Radiology, Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Leeds, UK.

Catherine Parchment-Smith (C)

Mid Yorkshire Hospitals NHS Trust Wakefield, Wakefield, UK.

Ese Adiotomre (E)

Department of Radiology, Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Leeds, UK.

Oliver Hulson (O)

Department of Radiology, Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Leeds, UK.

Atif Khan (A)

Department of Radiology, Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Leeds, UK.

Philip Melling (P)

Department of Information and Insight, Digital Informatics Team, Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Leeds, UK.

Sacha Pierre (S)

Department of Radiology, Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Leeds, UK.

Jonathan Smith (J)

Department of Radiology, Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Leeds, UK.

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