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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Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Statistical and structural approaches to texture
Haralick, R. M. (1979) · Proceedings of the IEEE · doi:10.1109/proc.1979.11328 · Cited by 5,769
EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent
Mottet, N., Joaquim Bellmunt, B., Bolla, M., Briers, E., Cumberbatch, M. G. K., Santis, M. D., Fossati, N., Gross, T., Henry, A. M., Joniau, S., et al. (2016) · European Urology · doi:10.1016/j.eururo.2016.08.003 · Cited by 3,223
EAU Guidelines on Prostate Cancer
Heidenreich, A., Aus, G., Bolla, M., Joniau, S., Matveev, V. B., Schmid, H. P., & Zattoni, F. (2007) · European Urology · doi:10.1016/j.eururo.2007.09.002 · Cited by 3,134
ESUR prostate MR guidelines 2012
Barentsz, J. O., Richenberg, J., Clements, R., Choyke, P. L., Verma, S., Villeirs, G. M., Rouvière, O., Løgager, V., & Fütterer, J. J. (2012) · European Radiology · doi:10.1007/s00330-011-2377-y · Cited by 2,444
SAFETY AND MORBIDITY OF FIRST AND REPEAT TRANSRECTAL ULTRASOUND GUIDED PROSTATE NEEDLE BIOPSIES: RESULTS OF A PROSPECTIVE EUROPEAN PROSTATE CANCER DETECTION STUDY
Djavan, B., Waldert, M., Zlotta, A. R., Dobroński, P., Seitz, C., Mesut Remzi, A., BORKOWSKI, A., Schulman, C., & Marberger, M. (2001) · The Journal of Urology · doi:10.1016/s0022-5347(05)65851-x · Cited by 2,048
EAU Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent—Update 2013
Heidenreich, A., Bastian, P. J., Joaquim Bellmunt, B., Bolla, M., Joniau, S., van der Kwast, T., Mason, M. D., Matveev, V. B., Wiegel, T., Zattoni, F., et al. (2013) · European Urology · doi:10.1016/j.eururo.2013.09.046 · Cited by 1,935
Clinical Results of Long-Term Follow-Up of a Large, Active Surveillance Cohort With Localized Prostate Cancer
Klotz, L. H., Zhang, L., Lam, A., Nam, R. K., Mamedov, A., & Loblaw, A. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2009.24.2180 · Cited by 1,074
Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer
Tixier, F., Rest, C. C. L., Hatt, M., Albarghach, N. M., Pradier, O., Metges, J., Corcos, L., & Visvikis, D. (2011) · Journal of Nuclear Medicine · doi:10.2967/jnumed.110.082404 · Cited by 695
Engineering and Algorithm Design for an Image Processing API: A Technical Report on ITK - the Insight Toolkit
Yoo, T. S., Ackerman, M., Lorensen, W. E., Schroeder, W., Chalana, V., Aylward, S., Metaxas, D., & Whitaker, R. (2002) · Studies in health technology and informatics · doi:10.3233/978-1-60750-929-5-586 · Cited by 621

Show all 33 references

Research works printed but not linked: 1

Donati OF, Mazaheri Y, Afaq A et al (2013) Prostate Cancer Aggressiveness: Assessment with Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient. Radiology. doi: 10.1148/radiol.13130973:130973 · DOI

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Additional file 1 of Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm
Zhong, X., Guan, T., Tang, D. R., Li, J., Lu, B., Cui, S., & Tang, H. (2021) · Figshare · doi:10.6084/m9.figshare.14385612
Additional file 1 of A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis
Yu, Z., Ding, J., Pang, H., Fang, H., He, F., Xu, C., Li, X., & Ren, K. (2022) · Figshare · doi:10.6084/m9.figshare.21087527
Additional file 4 of Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study
Li, C., Hu, J., Zhang, Z., Wei, C., Chen, T., Wang, X., Dai, Y., & Shen, J. (2024) · Figshare · doi:10.6084/m9.figshare.26753788
Additional file 2 of Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study
Li, C., Hu, J., Zhang, Z., Wei, C., Chen, T., Wang, X., Dai, Y., & Shen, J. (2024) · Figshare · doi:10.6084/m9.figshare.26753782
Additional file 2 of Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm
Zhong, X., Guan, T., Tang, D. R., Li, J., Lu, B., Cui, S., & Tang, H. (2021) · Figshare · doi:10.6084/m9.figshare.14385615
Additional file 1 of MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins
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Additional file 3 of Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study
Li, C., Hu, J., Zhang, Z., Wei, C., Chen, T., Wang, X., Dai, Y., & Shen, J. (2024) · Figshare · doi:10.6084/m9.figshare.26753785
Additional file 5 of Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study
Li, C., Hu, J., Zhang, Z., Wei, C., Chen, T., Wang, X., Dai, Y., & Shen, J. (2024) · Figshare · doi:10.6084/m9.figshare.26753791
Additional file 1 of Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study
Li, C., Hu, J., Zhang, Z., Wei, C., Chen, T., Wang, X., Dai, Y., & Shen, J. (2024) · Figshare · doi:10.6084/m9.figshare.26753779
Additional file 1 of Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients
Wang, J., Chen, J., Zhou, R., Gao, Y., & Li, J. (2022) · Figshare · doi:10.6084/m9.figshare.19619367
Additional file 1 of A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis
Yu, Z., Xu, C., Zhang, Y., & Ji, F. (2022) · Figshare · doi:10.6084/m9.figshare.20388404
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FERREIRA, A. D. M., Nather, J. C., Barreto, L. F., Chahud, F., da Cunha, D. L., Ribeiro, L. G. S., Lacchini, F. C., Colli, L. M., & Muglia, V. F. (2026) · Research Square · doi:10.21203/rs.3.rs-10594837/v1
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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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