Texture features on T2-weighted magnetic resonance imaging: New potential biomarkers for prostate cancer aggressiveness

Physics in Medicine and Biology

Volumn 60, Issue 7, 2015, Pages 2685-2701

  Vignati, A ^a   Mazzetti, S ^a   Giannini, V ^a   Russo, F ^a   Bollito, E ^b   Porpiglia, F ^b   Stasi, M ^a   Regge, D ^a  

^a CANDIOLO CANCER INSTITUTE   (Italy)

^b UNIVERSITY OF TURIN   (Italy)

Author keywords

ADC maps; GLCM texture feature; pathologic Gleason score correlation; prostate cancer aggressiveness; T2 weighted MR imaging

Indexed keywords

DIFFUSION; MAGNETIC RESONANCE IMAGING; SURFACE DIFFUSION; TEXTURES; UROLOGY;

APPARENT DIFFUSION COEFFICIENT; GLEASON SCORES; GRAY LEVEL CO-OCCURRENCE MATRIX; MR IMAGING; PROSTATE CANCERS; RECEIVER OPERATING CHARACTERISTIC CURVES; SPEARMAN CORRELATION COEFFICIENTS; TEXTURE FEATURES;

DISEASES;

AGED; CANCER GRADING; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; EVALUATION STUDY; HUMAN; MALE; MIDDLE AGED; PROCEDURES; PROSTATIC NEOPLASMS;

AGED; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MIDDLE AGED; NEOPLASM GRADING; PROSTATIC NEOPLASMS;

EID: 84925609486     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/60/7/2685     Document Type: Article

References (46)

1. Antel S B, Collins D L, Bernasconi N, Andermann F, Shinghal R, Kearney R E, Arnold D L and Bernasconi A 2003 Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis Neuroimage 19 1748-59
2. Bae H et al 2014 Apparent diffusion coefficient value as a biomarker reflecting morphological and biological features of prostate cancer Int. Urol. Nephrol. 46 555-61
3. Bland J M and Altman D G 1995 Calculating correlation coefficients with repeated observations. II. Correlation between subjects BMJ 310 633
4. Castellano G, Bonilha L, Li L M and Cendes F 2004 Texture analysis of medical images Clin. Radiol. 59 1061-9
5. Chan H P, Wei D, Helvie M A, Sahiner B, Adler D D, Goodsitt M M and Petrick N 1995 Computer-aided classification of mammographic masses and normal issue: linear discriminant analysis in texture feature space Phys. Med. Biol. 40 857-76
6. Chan I, Wells W 3rd, Mulkern R V, Haker S, Zhang J, Zou K H, Maier S E and Tempany C M 2003 Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier Med Phys. 30 2390-8
7. Chen W, Giger M L, Li H, Bick U and Newstead G M 2007 Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images Magn. Reson. Med. 58 562-71
8. Donati O F, Mazaheri Y, Afaq A, Vargas H A, Zheng J, Moskowitz C S, Hricak H and Akin O 2014 Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient Radiology 271 143-52
9. Fleming I D, Cooper J S, Henson D E, Hutter R V P, Kennedy B J, Murphy G P, O'Sullivan B, Sobin L H and Yarbro J W (ed) 1997 AJCC Cancer Staging Manual 5th edn (Philadelphia, PA: Lippincott-Raven)
10. Freeborough PA and Fox NC 1998 MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease IEEE Trans. Med. Imaging 17 475-9
11. Giannini V, Vignati A, Mirasole S, Mazzetti S, Russo F, Stasi M and Regge D 2014 MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness Comput. Methods Biomech. Biomed. Eng.: Imag. Vis. at press
12. Gibbs P and Turnbull L W 2003 Textural analysis of contrast-enhanced MR images of the breast Magn. Reson. Med. 50 92-8
13. GNU Octave 2014 version 3.8.2. Available at (www.gnu.org/software/octave/)
14. Gupta R T, Kauffman C R, Polascik T J, Taneja S S and Rosenkrantz A B 2013 The state of prostate MRI in 2013 Oncology 27 262-70
15. Hambrock T, Somford D M, Huisman H J, van Oort I M, Witjes J A, Hulsbergen-van de Kaa C A, Scheenen T and Barentsz J O 2011 Relationship between apparent diffusion coefficients at 3.0 T MR imaging and Gleason grade in peripheral zone prostate cancer Radiology 259 453-61
16. Haralick R M, Sharmugam K and Dinstein I 1973 Textural features for image classification IEEE Trans. Syst. Man Cybernet. 3 610-21
17. Helpap B and Egevad L 2006 The significance of modified Gleason grading of prostatic carcinoma in biopsy and radical prostatectomy specimens Virchows Arch 449 622-7
18. Herlidou-Même S, Constans J M, Carsin B, Olivie D, Eliat PA, Nadal-Desbarats L, Gondry C, Le Rumeur E, Idy-Peretti I and de Certaines J D 2003 MRI texture analysis on texture test objects, normal brain and intracranial tumors Magn. Reson. Imag. 21 989-93
19. Johnson H J, McCormick M, Ibañez L and The Insight Software Consortium 2013 The ITK Software Guide 3rd edn (Clifton Park, NY: Kitware, Inc) www.itk.org/ItkSoftwareGuide.pdf
20. Jung S I, Donati O F, Vargas H A, Goldman D, Hricak H and Akin O 2013 Transition zone prostate cancer: incremental value of diffusion-weighted endorectal MR imaging in tumor detection and assessment of aggressiveness Radiology 269 493-503
21. Kobus T, Vos P C, Hambrock T, De Rooij M, Hulsbergen-Van de Kaa C A, Barentsz J O, Heerschap A and Scheenen T W 2012 Prostate cancer aggressiveness: in vivo assessment of MR spectroscopy and diffusion-weighted imaging at 3 T Radiology 265 457-67
22. Li H, Giger M L, Olopade O I, Margolis A, Lan L and Chinander M R 2005 Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms Acad. Radiol. 12 863-73
23. Madabhushi A, Feldman M D, Metaxas D N, Tomaszeweski J and Chute D 2005 Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI IEEE Trans. Med. Imaging 24 1611-25
24. Mahmoud-Ghoneim D, Toussaint G, Constans J M and de Certaines J D 2003 3D texture analysis in MRI: a preliminary evaluation in gliomas Magn. Reson. Imag. 21 983-7
25. Montironi R, Lopez-Beltran A, Mazzucchelli R, Scarpelli M and Bollito E 2001 Assessment of radical prostatectomy specimens and diagnostic reporting of pathological findings Pathologica 93 226-32
26. Mostafizur Rahman M and Davis D N 2013 Addressing the class imbalance problem in medical datasets Int. J. Mach. Learn. Comput. 3 224-8
27. Hall-Beyer M 2007 The GLCM tutorial home page www.fp.ucalgary.ca/mhallbey/tutorial.htm
28. Nam R K et al 2014 Incidence of complications other than urinary incontinence or erectile dysfunction after radical prostatectomy or radiotherapy for prostate cancer: a population-based cohort study Lancet. Oncol. 15 223-31
29. Niaf E, Rouvière O, Mège-Lechevallier F, Bratan F and Lartizien C 2012 Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI Phys. Med. Biol. 57 3833-51
30. Obuchowski N A 1997 Nonparametric analysis of clustered ROC curve data Biometrics 53 567-78
31. Oto A, Yang C, Kayhan A, Tretiakova M, Antic T, Schmid-Tannwald C, Eggener S, Karczmar G S and Stadler W M 2011 Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis AJR Am. J. Roentgenol. 197 1382-90
32. Peng Y, Jiang Y, Antic T, Giger M L, Eggener S E and Oto A 2014 Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a cross-imager study Radiology 271 461-71
33. Peng Y, Jiang Y, Yang C, Brown J B, Antic T, Sethi I, Schmid-Tannwald C, Giger M L, Eggener S E and Oto A 2013 Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score - a computer-aided diagnosis development study Radiology 267 787-96
34. Potosky A L, Davis W W, Hoffman R M, Stanford J L, Stephenson R A, Penson D F and Harlan L C 2004 Five-year outcomes after prostatectomy or radiotherapy for prostate cancer: the prostate cancer outcomes study J. Natl Cancer Inst. 96 1358-67
35. Quint L E, Van Erp J S, Bland P H, Del Buono E A, Mandell S H, Grossman H B and Gikas P W 1991 Prostate cancer: correlation of MR images with tissue optical density at pathologic examination Radiology 179 837-42
36. Rahman M M and Davis D N 2013 Addressing the class imbalance problem in medical datasets J. Mach. Learning Comput. 3 224-8
37. Rosenkrantz A B et al 2012 Prostate cancer: multiparametric MRI for index lesion localization - a multiple-reader study AJR Am. J. Roentgenol. 199 830-7
38. Rosenkrantz A B, Kopec M, Kong X, Melamed J, Dakwar G, Babb J S and Taouli B 2010 Prostate cancer versus post-biopsy hemorrhage: diagnosis with T2- and diffusion-weighted imaging J. Magn. Reson. Imag. 31 1387-94
39. Schiebler M L, McSherry S, Keefe B, Mittelstaedt C A, Mohler J L, Dent G A and McCartney W H 1991 Comparison of the digital rectal examination, endorectal ultrasound, and body coil magnetic resonance imaging in the staging of adenocarcinoma of the prostate Urol. Radiol. 13 110-18
40. Stamey T A, Freiha F S, McNeal J E, Redwine E A, Whittemore A S and Schmid H P 1993 Localized prostate cancer. Relationship of volume to clinical significance for treatment of prostate cancer Cancer 71 933-8 (suppl. 3)
41. Turkbey B et al 2011 Is apparent diffusion coefficient associated with clinical risk scores for prostate cancers that are visible on 3 T MR images? Radiology 258 488-95
42. Vargas H A, Akin O, Franiel T, Mazaheri Y, Zheng J, Moskowitz C, Udo K, Eastham J and Hricak H 2011 Diffusion-weighted endorectal MR imaging at 3 T for prostate cancer: tumor detection and assessment of aggressiveness Radiology 259 775-84
43. Viswanath S E, Bloch N B, Chappelow J C, Toth R, Rofsky N M, Genega E M, Lenkinski R E and Madabhushi A 2012 Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 T endorectal, in vivo T2-weighted MR imagery J. Magn. Reson. Imag. 36 213-24
44. Vos E K, Litjens G J, Kobus T, Hambrock T, Hulsbergen-van de Kaa C A, Barentsz J O, Huisman H J and Scheenen T W 2013 Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 T Eur. Urol. 64 448-55
45. Wang L, Mazaheri Y, Zhang J, Ishill N M, Kuroiwa K and Hricak H 2008 Assessment of biologic aggressiveness of prostate cancer: correlation of MR signal intensity with Gleason grade after radical prostatectomy Radiology 246 168-76
46. Yun B L, Cho N, Li M, Jang M H, Park S Y, Kang H C, Kim B, Song I C and Moon W K 2014 Intratumoral heterogeneity of breast cancer Xenograft models: texture analysis of diffusion-weighted MR imaging Korean J. Radiol 15 591-604