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.
| Original language | English |
|---|---|
| Pages (from-to) | 2840-2850 |
| Number of pages | 11 |
| Journal | European Radiology |
| Volume | 25 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - 2015 Oct 13 |
| Externally published | Yes |
Fingerprint
Keywords
- Adenocarcinoma
- Computer-assisted
- Gleason grading
- Image processing
- Magnetic resonance imaging
- Prostatic neoplasm
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
Cite this
Haralick texture analysis of prostate MRI : utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. / 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.
In: European Radiology, Vol. 25, No. 10, 13.10.2015, p. 2840-2850.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Haralick texture analysis of prostate MRI
T2 - utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores
AU - Wibmer, Andreas
AU - Hricak, Hedvig
AU - Gondo, Tatsuo
AU - Matsumoto, Kazuhiro
AU - Veeraraghavan, Harini
AU - Fehr, Duc
AU - Zheng, Junting
AU - Goldman, Debra
AU - Moskowitz, Chaya
AU - Fine, Samson W.
AU - Reuter, Victor E.
AU - Eastham, James
AU - Sala, Evis
AU - Vargas, Hebert Alberto
PY - 2015/10/13
Y1 - 2015/10/13
N2 - 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.
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.
KW - Adenocarcinoma
KW - Computer-assisted
KW - Gleason grading
KW - Image processing
KW - Magnetic resonance imaging
KW - Prostatic neoplasm
UR - http://www.scopus.com/inward/record.url?scp=84941420890&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941420890&partnerID=8YFLogxK
U2 - 10.1007/s00330-015-3701-8
DO - 10.1007/s00330-015-3701-8
M3 - Article
C2 - 25991476
AN - SCOPUS:84941420890
VL - 25
SP - 2840
EP - 2850
JO - European Radiology
JF - European Radiology
SN - 0938-7994
IS - 10
ER -