Using biomarkers in patients with positive multiparametric magnetic resonance imaging: 4Kscore predicts the presence of cancer outside the index lesion

Objectives To evaluate if the blood biomarker, 4Kscore, in addition to multiparametric magnetic resonance imaging information could identify patients who would benefit from undergoing only a targeted biopsy. Methods We retrospectively analyzed a population of 256 men with positive multiparametric ma...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of urology 2021-01, Vol.28 (1), p.47-52
Hauptverfasser: Falagario, Ugo Giovanni, Lantz, Anna, Jambor, Ivan, Martini, Alberto, Ratnani, Parita, Wagaskar, Vinayak, Treacy, Patrick‐Julien, Veccia, Alessandro, Bravi, Carlo Andrea, Bashorun, Hafis O, Phillip, Deron, Lewis, Sara, Haines, Kenneth, Cormio, Luigi, Carrieri, Giuseppe, Tewari, Ash
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Objectives To evaluate if the blood biomarker, 4Kscore, in addition to multiparametric magnetic resonance imaging information could identify patients who would benefit from undergoing only a targeted biopsy. Methods We retrospectively analyzed a population of 256 men with positive multiparametric magnetic resonance imaging who underwent standard + targeted biopsy at Mount Sinai Hospital, New York, NY, USA. 4Kscore (OPKO Health, Miami, FL, USA) was sampled from all patients before biopsy. Uni‐ and multivariable binary logistic regression analyses were carried out to predict clinically significant prostate cancer, defined as International Society of Urological Pathology grade group ≥2, in standard biopsy cores. The model with the best area under the curve was selected and internal validation was carried out using the leave‐one‐out cross‐validation. Results The developed model showed an area under the curve of 0.86. Carrying out only targeted biopsy in patients with a model‐derived probability
ISSN:0919-8172
1442-2042
1442-2042
DOI:10.1111/iju.14385