Next Article in Journal
Colonic Volume Changes in Paediatric Constipation Compared to Normal Values Measured Using MRI
Next Article in Special Issue
MRI and Targeted Biopsy Essential Tools for an Accurate Diagnosis and Treatment Decision Making in Prostate Cancer
Previous Article in Journal
Value Attribution in the Decision to Use of Whole Body MRI for Early Cancer Diagnosis
Previous Article in Special Issue
Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection
Article

Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

1
Department of Surgical Sciences, University of Turin, 10126 Turin, Italy
2
Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy
3
Department of Urology, Humanitas Gradenigo, 10153 Turin, Italy
4
Department of Urology, Humanitas University, 10153 Turin, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Felix G. Meinel
Diagnostics 2021, 11(6), 973; https://doi.org/10.3390/diagnostics11060973
Received: 29 April 2021 / Revised: 17 May 2021 / Accepted: 26 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Role of Imaging and Artificial Intelligence in Prostate Cancer)
Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time. View Full-Text
Keywords: computer aided diagnosis; prostate cancer; artificial intelligence; assisted reading computer aided diagnosis; prostate cancer; artificial intelligence; assisted reading
Show Figures

Figure 1

MDPI and ACS Style

Giannini, V.; Mazzetti, S.; Cappello, G.; Doronzio, V.M.; Vassallo, L.; Russo, F.; Giacobbe, A.; Muto, G.; Regge, D. Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers. Diagnostics 2021, 11, 973. https://doi.org/10.3390/diagnostics11060973

AMA Style

Giannini V, Mazzetti S, Cappello G, Doronzio VM, Vassallo L, Russo F, Giacobbe A, Muto G, Regge D. Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers. Diagnostics. 2021; 11(6):973. https://doi.org/10.3390/diagnostics11060973

Chicago/Turabian Style

Giannini, Valentina, Simone Mazzetti, Giovanni Cappello, Valeria M. Doronzio, Lorenzo Vassallo, Filippo Russo, Alessandro Giacobbe, Giovanni Muto, and Daniele Regge. 2021. "Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers" Diagnostics 11, no. 6: 973. https://doi.org/10.3390/diagnostics11060973

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop