ArticleEuropean radiology2015
Logistic regression model for diagnosis of transition zone 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 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive factors of incidental prostate cancer in patients undergoing surgery for presumed benign prostatic hyperplasia: an updated systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Meta-research: How many diagnostic or prognostic models published in radiological journals are evaluated externally?European radiology · 2024Pooled it
- Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.European radiology · 2020Pooled it
- Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.Journal of magnetic resonance imaging : JMRI · 2017Trial
- A multi-center study: development and validation of a BpMRI focused model in transition zone PI-RADS 3 and 4 lesions to detect clinically significant prostate cancer.Abdominal radiology (New York) · 2025Article
- Article
- High-grade prostate cancer demonstrates preferential growth in the cranio-caudal axis and provides discrimination of disease grade in an MRI parametric model.The British journal of radiology · 2024Article
- Integrating CT-based radiomic model with clinical features improves long-term prognostication in high-risk prostate cancer.Frontiers in oncology · 2023Article
- Multiple analyses suggests texture features can indicate the presence of tumor in the prostate tissue.Physical and engineering sciences in medicine · 2022Article
- Combining Magnetic Resonance Diffusion-Weighted Imaging with Prostate-Specific Antigen to Differentiate Between Malignant and Benign Prostate Lesions.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole mount histopathology.Magnetic resonance imaging · 2022Article
- Review
- A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features.Brain sciences · 2021Article
- Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition.Magma (New York, N.Y.) · 2021Article
- Article
- MR Fingerprinting and ADC Mapping for Characterization of Lesions in the Transition Zone of the Prostate Gland.Radiology · 2019Article
- Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer.European radiology · 2019Article
- The clinical utility of prostate cancer heterogeneity using texture analysis of multiparametric MRI.International urology and nephrology · 2019Article
- Characterization and stratification of prostate lesions based on comprehensive multiparametric MRI using detailed whole-mount histopathology as a reference standard.NMR in biomedicine · 2017Article
- Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI.European radiology · 2017Article
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14 authors.
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Abstract
objectivesWe aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI).
methodsOne hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance.
resultsTwenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively.
conclusionsLR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. KEY POINTS: • MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.
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