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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To investigate Haralick texture analysis of prostate MRI for cancer detection and differentiating Gleason scores (GS).
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.
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).
Several Haralick-based texture features appear useful for prostate cancer detection and GS assessment.
• 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.
KeywordsProstatic neoplasm Adenocarcinoma Magnetic resonance imaging Image processing Computer-assisted Gleason grading
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.
There were no sources of support or conflicts of interest which require disclosure.
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