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;...
| Publicado en: | European Radiology Vol. 25; no. 9; pp. 2727 - 2738 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109616207&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109616207 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2015 vid: 25 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109616207 NLM25680730 2013116086 10.1007/s00330-015-3636-0 NLM25680730 109616207 ppf: 2727 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI. aug: au: 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 sug: subj: Magnetic Resonance Imaging Statistics and Numerical Data Prostatic Neoplasms Diagnosis Image Processing, Computer Assisted Methods Middle Age Biopsy Human Prostatic Neoplasms Pathology Prostate Pathology Pharmacokinetics ROC Curve Retrospective Design Male Adult Reproducibility of Results Aged Logistic Regression Sensitivity and Specificity Validation Studies Comparative Studies Evaluation Research Multicenter Studies Barthel Index Middle Aged: 45-64 years Adult: 19-44 years Aged: 65+ years Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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