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01-10-2017 | Computer Applications

Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study

Authors: Valentina Giannini, Simone Mazzetti, Enrico Armando, Silvia Carabalona, Filippo Russo, Alessandro Giacobbe, Giovanni Muto, Daniele Regge

Published in: European Radiology | Issue 10/2017

Abstract

Objectives

To compare the performance of experienced readers in detecting prostate cancer (PCa) using likelihood maps generated by a CAD system with that of unassisted interpretation of multiparametric magnetic resonance imaging (mp-MRI).

Methods

Three experienced radiologists reviewed mp-MRI prostate cases twice. First, readers observed CAD marks on a likelihood map and classified as positive those suspicious for cancer. After 6 weeks, radiologists interpreted mp-MRI examinations unassisted, using their favourite protocol. Sensitivity, specificity, reading time and interobserver variability were compared for the two reading paradigms.

Results

The dataset comprised 89 subjects of whom 35 with at least one significant PCa. Sensitivity was 80.9% (95% CI 72.1–88.0%) and 87.6% (95% CI 79.8–93.2; p = 0.105) for unassisted and CAD paradigm respectively. Sensitivity was higher with CAD for lesions with GS > 6 (91.3% vs 81.2%; p = 0.046) or diameter ≥10 mm (95.0% vs 80.0%; p = 0.006). Specificity was not affected by CAD. The average reading time with CAD was significantly lower (220 s vs 60 s; p < 0.001).

Conclusions

Experienced readers using likelihood maps generated by a CAD scheme can detect more patients with ≥10 mm PCa lesions than unassisted MRI interpretation; overall reporting time is shorter. To gain more insight into CAD–human interaction, different reading paradigms should be investigated.

Key points

• With CAD, sensitivity increases in patients with prostate tumours ≥10 mm and/or GS > 6.

• CAD significantly reduces reporting time of multiparametric MRI.

• When using CAD, a marginal increase of inter-reader agreement was observed.

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Russo F, Regge D, Armando E et al (2016) Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard. BJU Int 118:84–94CrossRefPubMed

Arumainayagam N, Ahmed HU, Moore CM et al (2013) Multiparametric MR imaging for detection of clinically significant prostate cancer: a validation cohort study with transperineal template prostate mapping as the reference standard. Radiology 268:761–769CrossRefPubMed

Bratan F, Niaf E, Melodelima C et al (2013) Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: a prospective study. Eur Radiol 23:2019–2029CrossRefPubMed

Litjens GJ, Barentsz JO, Karssemeijer N, Huisman HJ (2015) Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI. Eur Radiol 25:3187–3199CrossRefPubMedPubMedCentral

Vos PC, Barentsz JO, Karssemeijer N, Huisman HJ (2012) Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis. Phys Med Biol 57:1527–1542CrossRefPubMed

Ruprecht O, Weisser P, Bodelle B, Ackermann H, Vogl TJ (2012) MRI of the prostate: interobserver agreement compared with histopathologic outcome after radical prostatectomy. Eur J Radiol 81:456–460CrossRefPubMed

Thompson JE, Moses D, Shnier R et al (2014) Multiparametric magnetic resonance imaging guided diagnostic biopsy detects significant prostate cancer and could reduce unnecessary biopsies and over detection: a prospective study. J Urol 192:67–74CrossRefPubMed

Giannarini G, Zazzara M, Rossanese M et al (2014) Will multi-parametric magnetic resonance imaging be the future tool to detect clinically significant prostate cancer? Front Oncol 4:294CrossRefPubMedPubMedCentral

Regge D, Della Monica P, Galatola G et al (2013) Efficacy of computer-aided detection as a second reader for 6-9-mm lesions at CT colonography: multicenter prospective trial. Radiology 266:168–176CrossRefPubMed

10.

Iussich G, Correale L, Senore C et al (2013) CT colonography: preliminary assessment of a double-read paradigm that uses computer-aided detection as the first reader. Radiology 268:743–751CrossRefPubMed

11.

Iussich G, Correale L, Senore C et al (2014) Computer-aided detection for computed tomographic colonography screening: a prospective comparison of a double-reading paradigm with first-reader computer-aided detection against second-reader computer-aided detection. Invest Radiol 49:173–182CrossRefPubMed

12.

Valente IR, Cortez PC, Neto EC, Soares JM, de Albuquerque VH, Tavares JM (2016) Automatic 3D pulmonary nodule detection in CT images: a survey. Comput Methods Programs Biomed 124:91–107CrossRefPubMed

13.

Felker ER, Margolis DJ, Nassiri N, Marks LS (2016) Prostate cancer risk stratification with magnetic resonance imaging. Urol Oncol 34:311–319CrossRefPubMed

14.

Lemaître G, Martí R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F (2015) Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput Biol Med 60:8–31CrossRefPubMed

15.

Niaf E, Lartizien C, Bratan F et al (2014) Prostate focal peripheral zone lesions: characterization at multiparametric MR imaging–influence of a computer-aided diagnosis system. Radiology 271:761–769CrossRefPubMed

16.

Hambrock T, Vos PC, Hulsbergen-van de Kaa CA, Barentsz JO, Huisman HJ (2013) Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging–effect on observer performance. Radiology 266:521–530CrossRefPubMed

17.

Barentsz JO, Weinreb JC, Verma S et al (2016) Synopsis of the PI-RADS v2 guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol 69:41–49CrossRefPubMed

18.

Giannini V, Mazzetti S, Vignati A et al (2015) A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging. Comput Med Imaging Graph 46:219–226CrossRefPubMed

19.

Giannini V, Vignati A, De Luca M et al (2015) A novel and fully automated registration method for prostate cancer detection using multiparametric magnetic resonance imaging. J Med Imag Health Inform 5:1171–1182CrossRef

20.

Mazzetti S, Gliozzi AS, Bracco C, Russo F, Regge D, Stasi M (2012) Comparison between PUN and Tofts models in the quantification of dynamic contrast-enhanced MR imaging. Phys Med Biol 57:8443–8453CrossRefPubMed

21.

Connor RJ (1987) Sample size for testing differences in proportions for the paired-sample design. Biometrics 43:207–211CrossRefPubMed

22.

Petrick N, Haider M, Summers RM et al (2008) CT colonography with computer-aided detection as a second reader: observer performance study. Radiology 246:148–156CrossRefPubMed

23.

Turkbey B, Mani H, Aras O et al (2013) Prostate cancer: can multiparametric MR imaging help identify patients who are candidates for active surveillance? Radiology 268:144–152CrossRefPubMedPubMedCentral

24.

Chamie K, Sonn GA, Finley DS et al (2014) The role of magnetic resonance imaging in delineating clinically significant prostate cancer. Urology 83:369–375CrossRefPubMedPubMedCentral

Title: Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study
Authors: Valentina Giannini
Simone Mazzetti
Enrico Armando
Silvia Carabalona
Filippo Russo
Alessandro Giacobbe
Giovanni Muto
Daniele Regge
Publication date: 01-10-2017
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 10/2017
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-4805-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study

Abstract

Objectives

Methods

Results

Conclusions

Key points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key points

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