In World journal of urology ; h5-index 40.0
PURPOSE : Computer-aided diagnosis (CAD) may improve prostate cancer (PCa) detection and support multiparametric magnetic resonance imaging (mpMRI) readers for better characterization. We evaluated Watson Elementary® (WE®) CAD system results referring to definitive pathological examination in patients treated with robot-assisted radical prostatectomy (RARP) in a tertiary referral center.
METHODS : Patients treated with RARP between 2020 and 2021 were selected. WE® calculates the Malignancy Attention Index (MAI), starting from the information contained in the mpMRI images. Outcome measures were the capability to predict the presence of PCa, to correctly locate the dominant lesion, to delimit the largest diameter of the dominant lesion, and to predict the extraprostatic extension (EPE).
RESULTS : Overall, tumor presence was confirmed in 46 (92%) WE® highly suspicious areas, while it was confirmed in 43 (86%) mpMRI PI-RADS ≥ 4 lesions. The WE® showed a positive agreement with mpMRI of 92%. In 98% of cases, visible tumor at WE® showed that the highly suspicious areas were within the same prostate sector of the dominant tumor nodule at pathology. WE® showed a 2.5 mm median difference of diameter with pathology, compared with a 3.8 mm of mpMRI versus pathology (p = 0.019). In prediction of EPE, WE® and mpMRI showed sensitivity, specificity, positive and negative predictive value of 0.81 vs 0.71, 0.56 vs 0.60, 0.88 vs 0.85 and 0.42 vs 0.40, respectively.
CONCLUSION : The WE® system resulted accurate in the PCa dominant lesion detection, localization and delimitation providing additional information concerning EPE prediction.
Vittori Gianni, Bacchiani Mara, Grosso Antonio Andrea, Raspollini Maria Rosaria, Giovannozzi Neri, Righi Lorenzo, Di Maida Fabrizio, Agostini Simone, De Nisco Fausto, Mari Andrea, Minervini Andrea
2023-Jan-03
Artificial intelligence, Index lesion, Magnetic resonance imaging, Prostate cancer
