Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/10506
Title: Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth
Authors: Thon, Anika 
Teichgräber, Ulf 
Tennstedt-Schenk, Cornelia 
Hadjidemetriou, Stathis 
Winzler, Sven 
Malich, Ansgar 
Papageorgiou, Ismini 
Keywords: Multiparametric magnetic resonance imaging (mpMRI);Radiotherapy;Localization;B-Value
Category: Other Engineering and Technologies
Field: Engineering and Technology
Issue Date: 1-Oct-2017
Publisher: PUBLIC LIBRARY SCIENCE
Source: PLOS ONE, Volume: 12, Issue: 10,2017
metadata.dc.doi: 10.1371/journal.pone.0185995
Journal: PLoS ONE
Abstract: Background Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary (TM)) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. Aim/Objective To assess the performance of Watson Elementary (TM) in automated PCa diagnosis in our hospital's database of MRI-guided prostate biopsies. Methods The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61 +/- 6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary (TM) utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. Results The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06,chi(2) test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson's correlation). Conclusion The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis.
URI: http://ktisis.cut.ac.cy/handle/10488/10506
ISSN: 1932-6203
Rights: Copyright: © 2017 Thon et al.
Type: Article
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