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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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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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Acknowledgments

The scientific guarantor of this publication is Andreas Wibmer, MD. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. Junting Zheng, Debra Goldman and Chaya Moskowitz kindly provided statistical advice for this manuscript. All have significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. No study subjects or cohorts have been previously reported. Methodology: retrospective, experimental, performed at one institution.

Disclosure

There were no sources of support or conflicts of interest which require disclosure.

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Authors and Affiliations

  1. Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, 10065, New York City, NY, USA

    Andreas Wibmer, Hedvig Hricak, Evis Sala & Hebert Alberto Vargas

  2. Department of Urology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, 10065, New York City, NY, USA

    Tatsuo Gondo, Kazuhiro Matsumoto & James Eastham

  3. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, 10065, New York City, NY, USA

    Harini Veeraraghavan & Duc Fehr

  4. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, 10065, New York City, NY, USA

    Junting Zheng, Debra Goldman & Chaya Moskowitz

  5. Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, 10065, New York City, NY, USA

    Samson W. Fine & Victor E. Reuter

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  1. Andreas Wibmer
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Correspondence to Andreas Wibmer.

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Wibmer, A., Hricak, H., Gondo, T. et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 25, 2840–2850 (2015). https://doi.org/10.1007/s00330-015-3701-8

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