OBJECTIVES: To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. METHODS: Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. RESULTS: The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. CONCLUSION: LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. KEY POINTS: • The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.
OBJECTIVES: To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. METHODS: Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. RESULTS: The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. CONCLUSION: LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. KEY POINTS: • The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.
Authors: Cornelis G van Niekerk; J Alfred Witjes; Jelle O Barentsz; Jeroen A W M van der Laak; Christina A Hulsbergen-van de Kaa Journal: Prostate Date: 2012-09-19 Impact factor: 4.104
Authors: Andreas Erbersdobler; Henning Fritz; Silvia Schnöger; Markus Graefen; Peter Hammerer; Hartwig Huland; R Peter Henke Journal: Eur Urol Date: 2002-01 Impact factor: 20.096
Authors: A V Taira; G S Merrick; R W Galbreath; H Andreini; W Taubenslag; R Curtis; W M Butler; E Adamovich; K E Wallner Journal: Prostate Cancer Prostatic Dis Date: 2009-09-29 Impact factor: 5.554
Authors: Mrishta Brizmohun Appayya; Harbir S Sidhu; Nikolaos Dikaios; Edward W Johnston; Lucy Am Simmons; Alex Freeman; Alexander Ps Kirkham; Hashim U Ahmed; Shonit Punwani Journal: Br J Radiol Date: 2017-12-15 Impact factor: 3.039
Authors: Justin M Ream; Ankur M Doshi; Diane Dunst; Nainesh Parikh; Max X Kong; James S Babb; Samir S Taneja; Andrew B Rosenkrantz Journal: J Magn Reson Imaging Date: 2016-09-20 Impact factor: 4.813
Authors: Michela Antonelli; Edward W Johnston; Nikolaos Dikaios; King K Cheung; Harbir S Sidhu; Mrishta B Appayya; Francesco Giganti; Lucy A M Simmons; Alex Freeman; Clare Allen; Hashim U Ahmed; David Atkinson; Sebastien Ourselin; Shonit Punwani Journal: Eur Radiol Date: 2019-06-11 Impact factor: 5.315
Authors: Elena Bertelli; Laura Mercatelli; Chiara Marzi; Eva Pachetti; Michela Baccini; Andrea Barucci; Sara Colantonio; Luca Gherardini; Lorenzo Lattavo; Maria Antonietta Pascali; Simone Agostini; Vittorio Miele Journal: Front Oncol Date: 2022-01-13 Impact factor: 6.244
Authors: Nikolaos Dikaios; Francesco Giganti; Harbir S Sidhu; Edward W Johnston; Mrishta B Appayya; Lucy Simmons; Alex Freeman; Hashim U Ahmed; David Atkinson; Shonit Punwani Journal: Eur Radiol Date: 2018-11-19 Impact factor: 5.315