Diagnostic Utility of Artificial Intelligence-assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study.

Artificial intelligence Biopsy Computer-assisted decision-making Prostate cancer

Journal

European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661

Informations de publication

Date de publication:
29 Apr 2024
Historique:
received: 30 01 2024
revised: 22 03 2024
accepted: 12 04 2024
medline: 1 5 2024
pubmed: 1 5 2024
entrez: 30 4 2024
Statut: aheadofprint

Résumé

Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone. A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test. Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy. The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences. The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

Sections du résumé

BACKGROUND AND OBJECTIVE OBJECTIVE
Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone.
METHODS METHODS
A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test.
KEY FINDINGS AND LIMITATIONS UNASSIGNED
Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy.
CONCLUSIONS AND CLINICAL IMPLICATIONS CONCLUSIONS
The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.
PATIENT SUMMARY RESULTS
The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

Identifiants

pubmed: 38688825
pii: S2405-4569(24)00059-2
doi: 10.1016/j.euf.2024.04.007
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Karsten Guenzel (K)

Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany; Prostate-Diagnostic-Centre Berlin, PDZB, Berlin, Germany; Department of Urology, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany. Electronic address: Karsten.guenzel@PDZB.de.

Georg Lukas Baumgaertner (G)

Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Anwar R Padhani (AR)

Paul Strickland Scanner Centre, Mount Vernon Hospital, Middlesex, UK.

Johannes Luckau (J)

Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany.

Uwe Carsten Lock (U)

Prostate-Diagnostic-Centre Berlin, PDZB, Berlin, Germany.

Tomasz Ozimek (T)

Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany.

Stefan Heinrich (S)

Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany.

Jakob Schlegel (J)

Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany.

Jonas Busch (J)

Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany.

Ahmed Magheli (A)

Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany.

Julian Struck (J)

Department of Urology, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany.

Hendrik Borgmann (H)

Department of Urology, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany.

Tobias Penzkofer (T)

Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany.

Bernd Hamm (B)

Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Stefan Hinz (S)

Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany; Department of Urology, Magdeburg University Medical Center, Otto von Guericke University, Magdeburg, Germany.

Charlie Alexander Hamm (C)

Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany.

Classifications MeSH