MRI-targeted biopsy cores from prostate index lesions: assessment and prediction of the number needed.
Journal
Prostate cancer and prostatic diseases
ISSN: 1476-5608
Titre abrégé: Prostate Cancer Prostatic Dis
Pays: England
ID NLM: 9815755
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
20
05
2022
accepted:
23
09
2022
revised:
08
09
2022
medline:
28
8
2023
pubmed:
9
10
2022
entrez:
8
10
2022
Statut:
ppublish
Résumé
Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model. We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates. The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model. The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates.
Sections du résumé
BACKGROUND
Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model.
METHODS
We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates.
RESULTS
The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model.
CONCLUSION
The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates.
Identifiants
pubmed: 36209237
doi: 10.1038/s41391-022-00599-2
pii: 10.1038/s41391-022-00599-2
pmc: PMC10449625
doi:
Substances chimiques
Prostate-Specific Antigen
EC 3.4.21.77
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
543-551Informations de copyright
© 2022. The Author(s).
Références
BJU Int. 2016 Jul;118(1):84-94
pubmed: 26198404
BMC Urol. 2021 Apr 23;21(1):68
pubmed: 33892696
Urology. 2017 Apr;102:178-182
pubmed: 27871829
CA Cancer J Clin. 2018 Jan;68(1):7-30
pubmed: 29313949
Lancet Oncol. 2019 Jan;20(1):100-109
pubmed: 30470502
Eur Urol. 2019 Mar;75(3):385-396
pubmed: 29908876
Eur Urol. 2019 May;75(5):712-720
pubmed: 30509763
J Urol. 2018 Oct;200(4):767-773
pubmed: 29733838
Cancer. 2016 Mar 15;122(6):884-92
pubmed: 26749141
Eur Urol Oncol. 2018 Oct;1(5):418-425
pubmed: 31158081
Urology. 2007 Mar;69(3):520-5
pubmed: 17382157
Can Urol Assoc J. 2013 May-Jun;7(5-6):E293-8
pubmed: 22398204
Diagn Interv Radiol. 2014 Jul-Aug;20(4):293-8
pubmed: 24808435
Eur Urol. 2019 Apr;75(4):570-578
pubmed: 30477981
J Urol. 2010 Mar;183(3):963-8
pubmed: 20089283
Sci Rep. 2020 Sep 29;10(1):15982
pubmed: 32994502
Urol Clin North Am. 2014 May;41(2):299-313
pubmed: 24725491
BJU Int. 2021 Dec;128 Suppl 3:36-44
pubmed: 34374190
Prostate Cancer Prostatic Dis. 2011 Mar;14(1):46-52
pubmed: 20498680
Urol Int. 2012;89(2):126-35
pubmed: 22814003
Circulation. 2015 Nov 17;132(20):1920-30
pubmed: 26572668
AJR Am J Roentgenol. 2020 Nov;215(5):1098-1103
pubmed: 32877244
Eur Urol. 2019 Jul;76(1):14-17
pubmed: 31047733
Eur Urol. 2017 Mar;71(3):353-365
pubmed: 27543165
JBR-BTR. 2010 Mar-Apr;93(2):62-70
pubmed: 20524513
Ther Adv Urol. 2019 Apr 28;11:1756287219842485
pubmed: 31065294
J Urol. 2006 May;175(5):1605-12
pubmed: 16600713
Eur J Cancer. 2015 Jun;51(9):1164-87
pubmed: 24120180
AJR Am J Roentgenol. 2019 Apr;212(4):847-854
pubmed: 30807218
JAMA Surg. 2019 Sep 1;154(9):811-818
pubmed: 31188412
Eur Urol. 2019 Jun;75(6):889-890
pubmed: 30930061
J Urol. 2018 Apr;199(4):976-982
pubmed: 29154904
JAMA. 2017 Jun 27;317(24):2532-2542
pubmed: 28655021
J Urol. 2016 Dec;196(6):1613-1618
pubmed: 27320841
Clin Radiol. 2019 Nov;74(11):853-864
pubmed: 31079953
J Urol. 2018 Nov;200(5):1030-1034
pubmed: 29733837
Eur Urol. 2008 Dec;54(6):1270-86
pubmed: 18423974
Eur Urol. 2021 Feb;79(2):243-262
pubmed: 33172724
Prostate Cancer Prostatic Dis. 2022 Apr;25(4):727-734
pubmed: 35067674
World J Urol. 2019 Oct;37(10):2109-2117
pubmed: 30652213
Rofo. 2022 Aug;194(8):852-861
pubmed: 35545106
Eur Radiol. 2020 Oct;30(10):5404-5416
pubmed: 32424596
Rev Urol. 2007 Summer;9(3):93-8
pubmed: 17934565
Eur Urol. 2019 Sep;76(3):284-303
pubmed: 31130434
BJU Int. 2020 Jul;126(1):83-90
pubmed: 31260602
J Clin Oncol. 2000 Mar;18(6):1161-3
pubmed: 10715283
Eur Urol Oncol. 2021 Oct;4(5):697-713
pubmed: 33358543
Radiology. 2019 Apr;291(1):83-89
pubmed: 30694165
J Clin Med. 2020 Jan 15;9(1):
pubmed: 31952120
J Urol. 2011 Nov;186(5):1830-4
pubmed: 21944136
Urol Oncol. 2021 Mar;39(3):193.e1-193.e6
pubmed: 33127298