Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.
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 tra...
| Published in: | European Radiology Vol. 25; no. 10; pp. 2840 - 2851 |
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| Main Authors: | , , , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Oct2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109645568&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109645568 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Oct2015 vid: 25 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109645568 NLM25991476 2013163940 10.1007/s00330-015-3701-8 NLM25991476 PMC5026307 [Available on 10/01/16] 109645568 ppf: 2840 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. aug: au: 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 sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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