Cited by 392Radiomics and Machine Learning in Medical ImagingProstate Cancer Diagnosis and TreatmentImage Retrieval and Classification Techniques

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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

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Wibmer, Andreas, Hricak, Hedvig, Gondo, Tatsuo, Matsumoto, Kazuhiro, Veeraraghavan, Harini, Fehr, Duc, Zheng, Junting, Goldman, Debra, Moskowitz, Chaya, Fine, Samson W., Reuter, Victor E., Eastham, James, Sala, Evis, Vargas, Hebert Alberto·2015·European Radiology

DOI 10.1007/s00330-015-3701-8PMC PMC5026307

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Abstract

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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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: Wibmer, Andreas
Hricak, Hedvig
Matsumoto, Kazuhiro
Veeraraghavan, Harini
Fehr, Duc
Zheng, Junting
Moskowitz, Chaya
Fine, Samson W.
Reuter, Victor E.
Sala, Evis
Vargas, Hebert Alberto
Venue: European Radiology
Publisher: Springer Science and Business Media LLC
Published: 2015-05-21
Volume: 25
Issue: 10
Pages: 2840-2850
ISSN: 0938-7994 (ISSN-L)
1432-1084
DOI: 10.1007/s00330-015-3701-8
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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Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores

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