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Radiology. 2014 Jun;271(3):761-9. doi: 10.1148/radiol.14130448. Epub 2014 Mar 5.

Prostate focal peripheral zone lesions: characterization at multiparametric MR imaging--influence of a computer-aided diagnosis system.

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  • 1From Inserm, U1032, LabTau, Lyon, F-69003, France; Université de Lyon, Lyon, F-69003, France; Université Lyon 1, Lyon, F-69003, France (E.N., F.B., O.R.); Université de Lyon, CREATIS; CNRS UMR5220; Inserm U1044; INSA-Lyon; Université Lyon 1, France (E.N., C.L.); Hospices Civils de Lyon, Department of Urinary and Vascular Radiology (F.B., O.R.) and Department of Pathology (F.M.), Hôpital Edouard Herriot, Lyon, F-69437, France; Hospices Civils de Lyon, Department of Biostatistics, F-69003, Lyon, France; Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biotatistique-Santé, F-69622, Villeurbanne, France (L.R., M.R.); and Université de Lyon, Lyon, F-69003, France; Université Lyon 1, Faculté de Médecine Lyon Est, Lyon, F-69003, France (O.R.).

Abstract

PURPOSE:

To assess the impact of a computer-aided diagnosis (CAD) system in the characterization of focal prostate lesions at multiparametric magnetic resonance (MR) imaging.

MATERIALS AND METHODS:

Formal institutional review board approval was not required. Thirty consecutive 1.5-T multiparametric MR imaging studies (with T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging) obtained before radical prostatectomy in patients between September 2008 and February 2010 were reviewed. Twelve readers assessed the likelihood of malignancy of 88 predefined peripheral zone lesions by using a five-level (level, 0-4) subjective score (SS) in reading session 1. This was repeated 5 weeks later in reading session 2. The CAD results were then disclosed, and in reading session 3, the readers could amend the scores assigned during reading session 2. Diagnostic accuracy was assessed by using a receiver operating characteristic (ROC) regression model and was quantified with the area under the ROC curve (AUC).

RESULTS:

Mean AUCs were significantly lower for less experienced (<1 year) readers (P < .02 for all sessions). Seven readers improved their performance between reading sessions 1 and 2, and 12 readers improved their performance between sessions 2 and 3. The mean AUCs for reading session 1 (83.0%; 95% confidence interval [CI]: 77.9%, 88.0%) and reading session 2 (84.1%; 95% CI: 78.1%, 88.7%) were not significantly different (P = .76). Although the mean AUC for reading session 3 (87.2%; 95% CI: 81.0%, 92.0%) was higher than that for session 2, the difference was not significant (P = .08). For an SS positivity threshold of 3, the specificity of reading session 2 (79.0%; 95% CI: 71.1%, 86.4%) was not significantly different from that of session 1 (78.7%; 95% CI: 70.5%, 86.8%) but was significantly lower than that of session 3 (86.2%; 95% CI: 77.1%, 93.1%; P < .03). The sensitivity of reading session 2 (68.4%; 95% CI: 57.5%, 77.7%) was significantly higher than that of session 1 (64.0%; 95% CI: 52.9%, 73.9%; P = .003) but was not significantly different from that of session 3 (71.4%; 95% CI: 58.3%, 82.7%).

CONCLUSION:

A CAD system may improve the characterization of prostate lesions at multiparametric MR imaging by increasing reading specificity.

PMID:
24592959
[PubMed - indexed for MEDLINE]
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