Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI.

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;...

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Detalles Bibliográficos
Publicado en:European Radiology Vol. 25; no. 9; pp. 2727 - 2738
Autores principales: Dikaios, Nikolaos, Alkalbani, Jokha, Abd-Alazeez, Mohamed, Sidhu, Harbir Singh, Kirkham, Alex, Ahmed, Hashim U, Emberton, Mark, Freeman, Alex, Halligan, Steve, Taylor, Stuart, Atkinson, David, Punwani, Shonit
Formato: research Journal Article
Publicado: Springer Nature Sep2015
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario: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.