Improvement of prostate cancer detection combining a computer-aided diagnostic system with TRUS-MRI targeted biopsy.


Journal

Abdominal radiology (New York)
ISSN: 2366-0058
Titre abrégé: Abdom Radiol (NY)
Pays: United States
ID NLM: 101674571

Informations de publication

Date de publication:
01 2019
Historique:
pubmed: 29 7 2018
medline: 27 2 2020
entrez: 29 7 2018
Statut: ppublish

Résumé

To validate a novel consensus method, called target-in-target, combining human analysis of mpMRI with automated CAD system analysis, with the aim to increasing the prostate cancer detection rate of targeted biopsies. A cohort of 420 patients was enrolled and 253 patients were rolled out, due to exclusion criteria. 167 patients, underwent diagnostic 3T MpMRI. Two expert radiologists evaluated the exams adopting PI-RADSv2 and CAD system. When a CAD target overlapped with a radiologic one, we performed the biopsy in the overlapping area which we defined as target-in-target. Targeted TRUS-MRI fusion biopsy was performed in 63 patients with a total of 212 targets. The MRI data of all targets were quantitatively analyzed, and diagnostic findings were compared to pathologist's biopsy reports. CAD system diagnostic performance exhibited sensitivity and specificity scores of 55.2% and 74.1% [AUC = 0.63 (0.54 ÷ 0.71)] , respectively. Human readers achieved an AUC value, in ROC analysis, of 0.71 (0.63 ÷ 0.79). The target-in-target method provided a detection rate per targeted biopsy core of 81.8 % vs. a detection rate per targeted biopsy core of 68.6 % for pure PI-RADS based on target definitions. The higher per-core detection rate of the target-in-target approach was achieved irrespective of the presence of technical flaws and artifacts. A novel consensus method combining human reader evaluation with automated CAD system analysis of mpMRI to define prostate biopsy targets was shown to improve the detection rate per biopsy core of TRUS-MRI fusion biopsies. Results suggest that the combination of CAD system analysis and human reader evaluation is a winning strategy to improve targeted biopsy efficiency.

Identifiants

pubmed: 30054684
doi: 10.1007/s00261-018-1712-z
pii: 10.1007/s00261-018-1712-z
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

264-271

Auteurs

Riccardo Campa (R)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Maurizio Del Monte (M)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Giovanni Barchetti (G)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Martina Pecoraro (M)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Vincenzo Salvo (V)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Isabella Ceravolo (I)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Elena Lucia Indino (EL)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Antonio Ciardi (A)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Carlo Catalano (C)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy.

Valeria Panebianco (V)

Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, V.le Regina Elena, 324, 00161, Rome, Italy. valeria.panebianco@gmail.com.

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Classifications MeSH