Glioma: Application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity

PLoS ONE

Volumn 9, Issue 9, 2014, Pages

  Ryu, Young Jin ^a   Choi, Seung Hong ^a,b   Park, Sang Joon ^a,c   Yun, Tae Jin ^a   Kim, Ji Hoon ^a   Sohn, Chul Ho ^a  

^a Seoul National University College of Medicine   (South Korea)

^b Seoul National University   (South Korea)

^c Seoul National University Hospital   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

GADOBUTROL; GADOLINIUM PENTETATE MEGLUMINE; KI 67 ANTIGEN;

ADULT; ANALYSIS OF VARIANCE; APPARENT DIFFUSION COEFFICIENT; ARTICLE; CAMERA; CANCER GRADING; CLINICAL ARTICLE; DIAGNOSTIC ACCURACY; DIFFUSION COEFFICIENT; DIFFUSION WEIGHTED IMAGING; ENTROPY; GLIOMA; HISTOGRAM; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; MALE; POST HOC ANALYSIS; RETROSPECTIVE STUDY; SINGLE DRUG DOSE; STATISTICAL ANALYSIS; STUDENT T TEST; TEXTURE ANALYSIS; BRAIN NEOPLASMS; COMPUTER ASSISTED DIAGNOSIS; GENE EXPRESSION; GENETICS; IMAGE ENHANCEMENT; MIDDLE AGED; PATHOLOGY; SENSITIVITY AND SPECIFICITY; STATISTICS AND NUMERICAL DATA; TUMOR VOLUME;

ADULT; BRAIN NEOPLASMS; DIFFUSION MAGNETIC RESONANCE IMAGING; ENTROPY; GENE EXPRESSION; GLIOMA; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; KI-67 ANTIGEN; MALE; MIDDLE AGED; NEOPLASM GRADING; SENSITIVITY AND SPECIFICITY; TUMOR BURDEN;

EID: 84907486211     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0108335     Document Type: Article

References (46)

1. Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch M (2006) Epidemiology and molecular pathology of glioma. Nat Clin Pract Neurol 2: 494-503; quiz 491 p following 516.
2. Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, et al. (1999) Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9: 53-60.
3. Daumas-Duport C, Scheithauer B, O'Fallon J, Kelly P (1988) Grading of astrocytomas. A simple and reproducible method. Cancer 62: 2152-2165.
4. Hilario A, Ramos A, Perez-Nunez A, Salvador E, Millan JM, et al. (2012) The added value of apparent diffusion coefficient to cerebral blood volume in the preoperative grading of diffuse gliomas. AJNR Am J Neuroradiol 33: 701-707.
5. Kang Y, Choi SH, Kim YJ, Kim KG, Sohn CH, et al. (2011) Gliomas: Histogram Analysis of Apparent Diffusion Coefficient Maps with Standard- or High-b-Value Diffusion-weighted MR Imaging-Correlation with Tumor Grade. Radiology 261: 882-890.
6. Lee EJ, Lee SK, Agid R, Bae JM, Keller A, et al. (2008) Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. AJNR Am J Neuroradiol 29: 1872-1877.
7. Murakami R, Hirai T, Kitajima M, Fukuoka H, Toya R, et al. (2008) Magnetic resonance imaging of pilocytic astrocytomas: usefulness of the minimum apparent diffusion coefficient (ADC) value for differentiation from high-grade gliomas. Acta Radiol 49: 462-467.
8. Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, et al. (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3: 573-589.
9. Materka A, Strzelecki M (1998) Texture analysis methods-a review. Technical university of lodz, institute of electronics, COST B11 report, Brussels: 9-11.
10. Rosenfeld A, Kak AC (2014) Digital picture processing: Elsevier.
11. Levine MD, Levine MD (1985) Vision in man and machine: McGraw-Hill New York.
12. Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67: 157-164.
13. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrastenhanced CT texture as a biomarker of 5-year survival. Radiology 266: 177-184.
14. Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA (2010) Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 10: 137-143.
15. Win T, Miles KA, Janes SM, Ganeshan B, Shastry M, et al. (2013) Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/ CT Predict Survival in Patients with Non-Small Cell Lung Cancer. Clinical Cancer Research 19: 3591-3599.
16. Chen W, Giger ML, Li H, Bick U, Newstead GM (2007) Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 58: 562-571.
17. Eliat PA, Olivie D, Saikali S, Carsin B, Saint-Jalmes H, et al. (2012) Can dynamic contrast-enhanced magnetic resonance imaging combined with texture analysis differentiate malignant glioneuronal tumors from other glioblastoma? Neurol Res Int 2012: 195176.
18. Zacharaki EI, Wang SM, Chawla S, Yoo DS, Wolf R, et al. (2009) Classification of Brain Tumor Type and Grade Using MRI Texture and Shape in a Machine Learning Scheme. Magnetic Resonance in Medicine 62: 1609-1618.
19. Schad LR, Bluml S, Zuna I (1993) MR tissue characterization of intracranial tumors by means of texture analysis. Magn Reson Imaging 11: 889-896.
20. Skogen K, Ganeshan B, Good C, Critchley G, Miles K (2013) Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade. J Neurooncol 111: 213-219.
21. S AR, Thomas T (2010) Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs. Int J of Recent Trends in Engineering and Technology 4: 27-33.
22. Abràmoff MD, Magalhães PJ, Ram SJ (2004) Image processing with ImageJ. Biophotonics international 11: 36-42.
23. Emblem KE, Nedregaard B, Nome T, Due-Tonnessen P, Hald JK, et al. (2008) Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 247: 808-817.
24. Fan GG, Deng QL, Wu ZH, Guo QY (2006) Usefulness of diffusion/perfusionweighted MRI in patients with non-enhancing supratentorial brain gliomas: a valuable tool to predict tumour grading? Br J Radiol 79: 652-658.
25. Maia AC Jr, Malheiros SM, da Rocha AJ, da Silva CJ, Gabbai AA, et al. (2005) MR cerebral blood volume maps correlated with vascular endothelial growth factor expression and tumor grade in nonenhancing gliomas. AJNR Am J Neuroradiol 26: 777-783.
26. Tozer DJ, Jager HR, Danchaivijitr N, Benton CE, Tofts PS, et al. (2007) Apparent diffusion coefficient histograms may predict low-grade glioma subtype. Nmr in Biomedicine 20: 49-57.
27. Ganeshan B, Miles KA, Young RC, Chatwin CR (2007) Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. Clin Radiol 62: 761-768.
28. Haralick RM, Shanmuga K, Dinstein I (1973) Textural Features for Image Classification. Ieee Transactions on Systems Man and Cybernetics Smc 3: 610-621.
29. Gupta RK, Cloughesy TF, Sinha U, Garakian J, Lazareff J, et al. (2000) Relationships between choline magnetic resonance spectroscopy, apparent diffusion coefficient and quantitative histopathology in human glioma. Journal of neuro-oncology 50: 215-226.
30. Guo AC, Cummings TJ, Dash RC, Provenzale JM (2002) Lymphomas and High-Grade Astrocytomas: Comparison of Water Diffusibility and Histologic Characteristics1. Radiology 224: 177-183.
31. Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, et al. (2001) The role of diffusion-weighted imaging in patients with brain tumors. American Journal of Neuroradiology 22: 1081-1088.
32. Wilke M, Eidenschink A, Müller-Weihrich S, Auer D (2001) MR diffusion imaging and 1H spectroscopy in a child with medulloblastoma. Acta Radiologica 42: 39-42.
33. Pauleit D, Langen KJ, Floeth F, Hautzel H, Riemenschneider MJ, et al. (2004) Can the apparent diffusion coefficient be used as a noninvasive parameter to distinguish tumor tissue from peritumoral tissue in cerebral gliomas? Journal of Magnetic Resonance Imaging 20: 758-764.
34. Castillo M, Smith JK, Kwock L, Wilber K (2001) Apparent diffusion coefficients in the evaluation of high-grade cerebral gliomas. American journal of neuroradiology 22: 60-64.
35. Baehring JM, Bi WL, Bannykh S, Piepmeier JM, Fulbright RK (2007) Diffusion MRI in the early diagnosis of malignant glioma. Journal of neuro-oncology 82: 221-225.
36. Scholzen T, Gerdes J (2000) The Ki-67 protein: from the known and the unknown. J Cell Physiol 182: 311-322.
37. Kiss R, Dewitte O, Decaestecker C, Camby I, Gordower L, et al. (1997) The combined determination of proliferative activity and cell density in the prognosis of adult patients with supratentorial high-grade astrocytic tumors. Am J Clin Pathol 107: 321-331.
38. Higano S, Yun X, Kumabe T, Watanabe M, Mugikura S, et al. (2006) Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 241: 839-846.
39. Sunwoo L, Choi SH, Park CK, Kim JW, Yi KS, et al. (2013) Correlation of apparent diffusion coefficient values measured by diffusion MRI and MGMT promoter methylation semiquantitatively analyzed with MS-MLPA in patients with glioblastoma multiforme. J Magn Reson Imaging 37: 351-358.
40. Saito T, Yamasaki F, Kajiwara Y, Abe N, Akiyama Y, et al. (2012) Role of perfusion-weighted imaging at 3T in the histopathological differentiation between astrocytic and oligodendroglial tumors. European journal of radiology 81: 1863-1869.
41. Cha S, Tihan T, Crawford F, Fischbein NJ, Chang S, et al. (2005) Differentiation of low-grade oligodendrogliomas from low-grade astrocytomas by using quantitative blood-volume measurements derived from dynamic susceptibility contrast-enhanced MR imaging. American journal of neuroradiology 26: 266-273.
42. Kanno H, Nishihara H, Narita T, Yamaguchi S, Kobayashi H, et al. (2012) Prognostic implication of histological oligodendroglial tumor component: clinicopathological analysis of 111 cases of malignant gliomas. PloS one 7: e41669.
43. van den Bent M, Chinot O-L, Cairncross JG (2003) Recent developments in the molecular characterization and treatment of oligodendroglial tumors. Neurooncology 5: 128-138.
44. Alvarez-Linera J (2008) 3T MRI: advances in brain imaging. European journal of radiology 67: 415-426.
45. Kim H, Choi SH, Kim J-H, Ryoo I, Kim SC, et al. (2013) Gliomas: Application of Cumulative Histogram Analysis of Normalized Cerebral Blood Volume on 3 T MRI to Tumor Grading. PloS one 8: e63462.
46. Law M, Young R, Babb J, Pollack E, Johnson G (2007) Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol 28: 761-766.