ArticleEuropean radiology2015
Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI.
Article in European radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.European radiology · 2020Pooled it
- A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset.Journal of imaging · 2021Article
- Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition.Magma (New York, N.Y.) · 2021Article
- Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI.Frontiers in oncology · 2021Article
- Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature.Contrast media & molecular imaging · 2021Article
- Predicting Lung Cancer Patients' Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework.Journal of biomedical physics & engineering · 2020Article
- Article
- Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.European radiology · 2019Article
- Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer.European radiology · 2019Article
- Characterizing indeterminate (Likert-score 3/5) peripheral zone prostate lesions with PSA density, PI-RADS scoring and qualitative descriptors on multiparametric MRI.The British journal of radiology · 2018Article
- Dynamic contrast-enhanced MRI of the prostate: An intraindividual assessment of the effect of temporal resolution on qualitative detection and quantitative analysis of histopathologically proven prostate cancer.Journal of magnetic resonance imaging : JMRI · 2017Article
- Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI.European radiology · 2017Article
- Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.Academic radiology · 2016Review
- Evolution of multi-parametric MRI quantitative parameters following transrectal ultrasound-guided biopsy of the prostate.Prostate cancer and prostatic diseases · 2015Article
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Authors and funding
12 authors.
Funding
Abstract
objectivesTo 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.
methodsTwo 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.
resultsThe 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.
conclusionLR-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.
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