Journal
DIAGNOSTICS
Volume 11, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/diagnostics11060973
Keywords
computer aided diagnosis; prostate cancer; artificial intelligence; assisted reading
Categories
Funding
- Fondazione AIRC [20398]
- European Union [952159]
Ask authors/readers for more resources
This study evaluated the diagnostic performance of CAD system for non-experienced readers in detecting and characterizing Prostate Cancer (PCa). The results showed that CAD assistance significantly increased sensitivity without negatively affecting specificity, while also reducing overall reporting time significantly.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available