Cognitive versus Software-Assisted Registration: Development of a New Nomogram Predicting Prostate Cancer at MRI-Targeted Biopsies.

Abstract

Multiparametric magnetic resonance imaging (mpMRI) is gaining acceptance to guide targeted biopsy (TB) in prostate cancer (PC) diagnosis. We aimed to compare the detection rate of software-assisted fusion TB (SA-TB) versus cognitive fusion TB (COG-TB) for PC and to evaluate potential clinical features in detecting PC and clinically significant PC (csPC) at TB. This was a retrospective cohort study of patients with rising and/or persistently elevated prostate-specific antigen (PSA) undergoing mpMRI followed by either transperineal SA-TB or transrectal COG-TB. The analysis showed a matched-paired analysis between SA-TB versus COG-TB without differences in clinical or radiological characteristics. Differences among detection of PC/csPC among groups were analyzed. A multivariable logistic regression model predicting PC at TB was fitted. The model was evaluated using the receiver operating characteristic-derived area under the curve, goodness of fit test, and decision-curve analyses. One hundred ninety-one and 87 patients underwent SA-TB or COG-TB, respectively. The multivariate logistic analysis showed that SA-TB was associated with overall PC (odds ratio [OR], 5.70; P< .01) and PC at TB (OR, 3.00; P< .01) but not with overall csPC (P= .40) and csPC at TB (P= .40). A nomogram predicting PC at TB was constructed using the Prostate Imaging Reporting and Data System version 2.0, age, PSA density and biopsy technique, showing improved clinical risk prediction against a threshold probability of 10% with a c-index of 0.83. In patients with suspected PC, software-assisted biopsy detects most cancers and outperforms the cognitive approach in targeting magnetic resonance imaging-visible lesions. Furthermore, we introduced a prebiopsy nomogram for the probability of PC in TB.