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Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores
Open accessAbstract
Objectives To investigate Haralick texture analysis of prostate MRI for cancer detection and differentiating Gleason scores (GS).Methods One 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.Results PZ 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).Conclusion Several 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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“Of note, other investigators have also found entropy determined from ADCapparent diffusion coefficient maps to be significantly associated with the pathologic Gleason score, even after controlling for the median ADCapparent diffusion coefficient (6,53).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [6]
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“In a study of 147 men with biopsy-proven prostate cancer, Wibmer et al (6) showed that Haralick texture analysis has the potential to enable differentiation of cancerous from noncancerous prostate tissue on both T2-weighted MR images and apparent diffusion coefficient (ADCapparent diffusion coefficient) maps derived from diffusion-weighted MR images (Fig 4).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [6]
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Metadata
- Authors
- Wibmer, A., Hricak, H., Gondo, T., Matsumoto, K., Veeraraghavan, H., Fehr, D., Zheng, J., Goldman, D., Moskowitz, C., Fine, S. W., Reuter, V. E., Eastham, J., Sala, E., & Vargas, H. A.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Wibmer, Andreas
- ORCID
- Venue
- European Radiology
- Published
- 2015-05-21
- Volume
- 25
- Issue
- 10
- Pages
- 2840-2850
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- PMC
- PMC5026307
- OpenAlex
- W313065440
- Cited by
- 391
- References
- 33
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 30 · 2023: 19 · 2022: 37 · 2021: 55 · 2020: 58 · 2019: 56 · 2018: 48 · 2017: 33 · 2016: 26 · 2015: 4
- FWCI
- 29.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- European Radiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (P30CA008748)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Urology › Prostate Cancer Diagnosis and Treatment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- prostate cancer, Gleason score, apparent diffusion coefficient, diffusion-weighted imaging, T2-weighted MRI, peripheral zone, transition zone, texture features, cancer detection
- MeSH terms
- Adult, Aged, Diagnosis, Differential, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Prostatectomy, Prostatic Neoplasms, Retrospective Studies, Diffusion Magnetic Resonance Imaging, Tumor Burden, Neoplasm Grading
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