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MP82-10 DEVELOPMENT AND EXTERNAL VALIDATION OF MRI-BASED NOMOGRAM TO PREDICT THE PROBABILITY OF PROSTATE CANCER DIAGNOSIS WITH MRI-US FUSION BIOPSY

2018; Lippincott Williams & Wilkins; Volume: 199; Issue: 4S Linguagem: Inglês

10.1016/j.juro.2018.02.2738

1527-3792

Giuseppe Simone, Gabriele Tuderti, Mariaconsiglia Ferriero, Valeria Panebianco, Rocco Papalia, Emanuela Altobelli, Alessandro Giacobbe, Lucio Benecchi, Leonardo Misuraca, Salvatore Guaglianone, Muto Giovanni, Masakatsu Oishi, Manju Aron, Suzanne L. Palmer, Osamu Ukimura, Inderbir S. Gill, Michele Gallucci, Andre Luis Abreu,

AI in cancer detection

You have accessJournal of UrologyProstate Cancer: Detection & Screening VII1 Apr 2018MP82-10 DEVELOPMENT AND EXTERNAL VALIDATION OF MRI-BASED NOMOGRAM TO PREDICT THE PROBABILITY OF PROSTATE CANCER DIAGNOSIS WITH MRI-US FUSION BIOPSY Giuseppe Simone, Gabriele Tuderti, Mariaconsiglia Ferriero, Valeria Panebianco, Rocco Papalia, Emanuela Altobelli, Alessandro Giacobbe, Lucio Benecchi, Leonardo Misuraca, Salvatore Guaglianone, Muto Giovanni, Masakatsu Oishi, Manju Aron, Suzanne Palmer, Osamu Ukimura, Inderbir S. Gill, Michele Gallucci, and Andre Luis De Castro Abreu Giuseppe SimoneGiuseppe Simone More articles by this author , Gabriele TudertiGabriele Tuderti More articles by this author , Mariaconsiglia FerrieroMariaconsiglia Ferriero More articles by this author , Valeria PanebiancoValeria Panebianco More articles by this author , Rocco PapaliaRocco Papalia More articles by this author , Emanuela AltobelliEmanuela Altobelli More articles by this author , Alessandro GiacobbeAlessandro Giacobbe More articles by this author , Lucio BenecchiLucio Benecchi More articles by this author , Leonardo MisuracaLeonardo Misuraca More articles by this author , Salvatore GuaglianoneSalvatore Guaglianone More articles by this author , Muto GiovanniMuto Giovanni More articles by this author , Masakatsu OishiMasakatsu Oishi More articles by this author , Manju AronManju Aron More articles by this author , Suzanne PalmerSuzanne Palmer More articles by this author , Osamu UkimuraOsamu Ukimura More articles by this author , Inderbir S. GillInderbir S. Gill More articles by this author , Michele GallucciMichele Gallucci More articles by this author , and Andre Luis De Castro AbreuAndre Luis De Castro Abreu More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.2738AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The wide diffusion of multiparametric magnetic resonance imaging (MRI) has dramatically modified the scenario of prostate cancer (PCa) diagnosis. The detection rate of MRI-ultrasound (US) fusion biopsy increased as well as the need for an extended prostate biopsy sampling with saturation biopsy decreased. The aim of this study was to develop, to calibrate and to externally validate a nomogram to predict the probability of detecting a prostate cancer METHODS Prospectively collected data from 3 european tertiary referral center series of 478 consecutive patients who underwent MRI-US fusion biopsy using the UroStation™ (Koelis, France) were used to build the nomogram. External validation was performed in 406 patients from a US tertiary referral center. The Mann–Whitney U test and the Chi-square tests were used to evaluate differences in continuous and categorical variables, respectively. A logistic regression model is created to identify predictors of PCa diagnosis with MRI-US fusion biopsy. Predictive accuracy was quantified using the concordance index (CI). Internal validation with 200 bootstrap resampling and calibration plots were generated to explore nomogram performance. RESULTS The development and validation cohorts were homogeneous for age (66.3 vs 66 yrs, p=0.57]), PSA levels (9.4 vs 8.8 ng/Ml, p=0.71] and PCa detection rates (57.4 vs 56.7%, p=0.81). Age, PSA serum levels, PIRADS score at MRI report, number of targeted and number of systematic cores taken were included in the model (Figure 1a). The nomogram showed high predictive accuracy (CI 0.82) and was well calibrated (Figure 1b). In the validation cohort the predictive accuracy was 0.77. Limitations include the need for a pre-biopsy mp-MRI and consequent fusion biopsy to reproduce findings. CONCLUSIONS This nomogram provides a high accuracy in predicting the probability of PCa diagnosis with MRI-US fusion biopsy. This is an easy to use clinical tool that physicians may use for patients counseling purposes. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e1109-e1110 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Giuseppe Simone More articles by this author Gabriele Tuderti More articles by this author Mariaconsiglia Ferriero More articles by this author Valeria Panebianco More articles by this author Rocco Papalia More articles by this author Emanuela Altobelli More articles by this author Alessandro Giacobbe More articles by this author Lucio Benecchi More articles by this author Leonardo Misuraca More articles by this author Salvatore Guaglianone More articles by this author Muto Giovanni More articles by this author Masakatsu Oishi More articles by this author Manju Aron More articles by this author Suzanne Palmer More articles by this author Osamu Ukimura More articles by this author Inderbir S. Gill More articles by this author Michele Gallucci More articles by this author Andre Luis De Castro Abreu More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...