ArticleEuropean radiology2015
Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.
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 199 papers, 1 of them a synthesis that pooled it.
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Who cites it
199 citing papers in PubMed, 1 synthesis or guideline pooled it.
- MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review.Abdominal radiology (New York) · 2023Pooled it
- MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218).European radiology · 2021Trial
- Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation.Abdominal radiology (New York) · 2019Trial
- Trial
- Texture analysis of magnetic resonance images of the human placenta throughout gestation: A feasibility study.PloS one · 2019Trial
- 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
- Biparametric MRI in prostate cancer: utility of whole-prostate and whole-lesion histogram and texture analysis for clinically significant prostate cancer.Japanese journal of radiology · 2026Article
- 3D organotypic skin models recapitulate autoantibody-driven pemphigus pathomechanisms and targeted therapeutic response.Science advances · 2026Article
- Enhancing deep learning interpretability for hand-crafted feature-guided histologic image classification via weak-to-strong generalization.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- A New Radiotranscriptomic Approach to Analyze Combined Sets of T3b Stage-Specific Genes and Radiomic Features in Prostate Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- Article
- Early prediction of neoadjuvant chemotherapy efficacy among patients with triple-negative breast cancer using an ultrasound-based radiomics nomogram.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning.Scientific reports · 2025Article
- Quantitative Prostate MRI, From theAJR. American journal of roentgenology · 2025Review
- Combining MRI radiomics, hypoxia gene signature score and clinical variables for prediction of biochemical recurrence-free survival after radiotherapy in prostate cancer.La Radiologia medica · 2025Article
- Data-driven image analysis to determine antibody-induced dissociation of cell-cell adhesion and antibody pathogenicity in pemphigus.PNAS nexus · 2025Article
- Radiomics for differential diagnosis of ischemic and dilated cardiomyopathy using non-contrast-enhanced cine cardiac magnetic resonance imaging.La Radiologia medica · 2025Article
- Retrospectively Quantified T2 Improves Detection of Clinically Significant Peripheral Zone Prostate Cancer.Cancers · 2025Article
- Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study.Insights into imaging · 2025Article
- Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI.Bioengineering (Basel, Switzerland) · 2025Article
139 more citing papers are in PubMed but not listed here.
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Abstract
objectivesTo investigate Haralick texture analysis of prostate MRI for cancer detection and differentiating Gleason scores (GS).
methodsOne hundred and forty-seven patients underwent T2- weighted (T2WI) and diffusion-weighted prostate MRI. Cancers ≥0.5 ml and non-cancerous peripheral (PZ) and transition (TZ) zone tissue were identified on T2WI and apparent diffusion coefficient (ADC) maps, using whole-mount pathology as reference. Texture features (Energy, Entropy, Correlation, Homogeneity, Inertia) were extracted and analysed using generalized estimating equations.
resultsPZ cancers (n = 143) showed higher Entropy and Inertia and lower Energy, Correlation and Homogeneity compared to non-cancerous tissue on T2WI and ADC maps (p-values: <.0001-0.008). In TZ cancers (n = 43) we observed significant differences for all five texture features on the ADC map (all p-values: <.0001) and for Correlation (p = 0.041) and Inertia (p = 0.001) on T2WI. On ADC maps, GS was associated with higher Entropy (GS 6 vs. 7: p = 0.0225; 6 vs. >7: p = 0.0069) and lower Energy (GS 6 vs. 7: p = 0.0116, 6 vs. >7: p = 0.0039). ADC map Energy (p = 0.0102) and Entropy (p = 0.0019) were significantly different in GS ≤3 + 4 versus ≥4 + 3 cancers; ADC map Entropy remained significant after controlling for the median ADC (p = 0.0291).
conclusionSeveral Haralick-based texture features appear useful for prostate cancer detection and GS assessment. KEY POINTS: • Several Haralick texture features may differentiate non-cancerous and cancerous prostate tissue. • Tumour Energy and Entropy on ADC maps correlate with Gleason score. • T2w-image-derived texture features are not associated with the Gleason score.
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