Biparametric vs. Multiparametric MRI in the Detection of Cancer in Transperineal Targeted-Biopsy-Proven Peripheral Prostate Cancer Lesions Classified as PI-RADS Score 3 or 3+1: The Added Value of ADC Quantification.

ADC biparametric MRI multiparametric MRI prostate biopsy prostate cancer

Journal

Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402

Informations de publication

Date de publication:
25 Jul 2024
Historique:
received: 31 05 2024
revised: 21 07 2024
accepted: 25 07 2024
medline: 10 8 2024
pubmed: 10 8 2024
entrez: 10 8 2024
Statut: epublish

Résumé

Biparametric MRI (bpMRI) has an important role in the diagnosis of prostate cancer (PCa), by reducing the cost and duration of the procedure and adverse reactions. We assess the additional benefit of the ADC map in detecting prostate cancer (PCa). Additionally, we examine whether the ADC value correlates with the presence of clinically significant tumors (csPCa). 104 peripheral lesions classified as PI-RADS v2.1 score 3 or 3+1 at the mpMRI underwent transperineal MRI/US fusion-guided targeted biopsy. The lesions were classified as PI-RADS 3 or 3+1; at histopathology, 30 were adenocarcinomas, 21 of which were classified as csPCa. The ADC threshold that maximized the Youden index in order to predict the presence of a tumor was 1103 (95% CI (990, 1243)), with a sensitivity of 0.8 and a specificity of 0.59; both values were greater than those found using the contrast medium, which were 0.5 and 0.54, respectively. Similar results were also found with csPCa, where the optimal ADC threshold was 1096 (95% CI (988, 1096)), with a sensitivity of 0.86 and specificity of 0.59, compared to 0.49 and 0.59 observed in the mpMRI. Our study confirms the possible use of a quantitative parameter (ADC value) in the risk stratification of csPCa, by reducing the number of biopsies and, therefore, the number of unwarranted diagnoses of PCa and the risk of overtreatment.

Sections du résumé

BACKGROUND BACKGROUND
Biparametric MRI (bpMRI) has an important role in the diagnosis of prostate cancer (PCa), by reducing the cost and duration of the procedure and adverse reactions. We assess the additional benefit of the ADC map in detecting prostate cancer (PCa). Additionally, we examine whether the ADC value correlates with the presence of clinically significant tumors (csPCa).
METHODS METHODS
104 peripheral lesions classified as PI-RADS v2.1 score 3 or 3+1 at the mpMRI underwent transperineal MRI/US fusion-guided targeted biopsy.
RESULTS RESULTS
The lesions were classified as PI-RADS 3 or 3+1; at histopathology, 30 were adenocarcinomas, 21 of which were classified as csPCa. The ADC threshold that maximized the Youden index in order to predict the presence of a tumor was 1103 (95% CI (990, 1243)), with a sensitivity of 0.8 and a specificity of 0.59; both values were greater than those found using the contrast medium, which were 0.5 and 0.54, respectively. Similar results were also found with csPCa, where the optimal ADC threshold was 1096 (95% CI (988, 1096)), with a sensitivity of 0.86 and specificity of 0.59, compared to 0.49 and 0.59 observed in the mpMRI.
CONCLUSIONS CONCLUSIONS
Our study confirms the possible use of a quantitative parameter (ADC value) in the risk stratification of csPCa, by reducing the number of biopsies and, therefore, the number of unwarranted diagnoses of PCa and the risk of overtreatment.

Identifiants

pubmed: 39125483
pii: diagnostics14151608
doi: 10.3390/diagnostics14151608
pii:
doi:

Types de publication

Journal Article

Langues

eng

Auteurs

Elena Bertelli (E)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Michele Vizzi (M)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Chiara Marzi (C)

Department of Statistics, Informatics and Applications "G. Parenti" (DiSIA), University of Florence, 50134 Florence, Italy.

Sandro Pastacaldi (S)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Alberto Cinelli (A)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Martina Legato (M)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Ron Ruzga (R)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Federico Bardazzi (F)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Vittoria Valoriani (V)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Francesco Loverre (F)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Francesco Impagliazzo (F)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Diletta Cozzi (D)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Samuele Nardoni (S)

Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, 50134 Florence, Italy.

Davide Facchiano (D)

Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, 50134 Florence, Italy.

Sergio Serni (S)

Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, 50134 Florence, Italy.
Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy.

Lorenzo Masieri (L)

Unit of Urological Minimally Invasive, Robotic Surgery and Kidney Transplantation, Careggi Hospital, University of Florence, 50134 Florence, Italy.
Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy.

Andrea Minervini (A)

Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy.
Unit of Oncologic Minimally-Invasive Urology and Andrology, Careggi Hospital, 50134 Florence, Italy.

Simone Agostini (S)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Vittorio Miele (V)

Department of Radiology, Careggi University Hospital, 50134 Florence, Italy.

Classifications MeSH