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Updated: Jul 16, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study
Anika Thon1,2, Ulf Teichgräber1, Cornelia Tennstedt-Schenk3
1Institute of Diagnostic and Interventional Radiology, Department of Experimental Radiology, Jena University Hospital, Friedrich-Schiller University, Jena, Germany.
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™) 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™ 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™ 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, χ2 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.
