Radiomics: The process and the challenges

Magnetic Resonance Imaging

Volumn 30, Issue 9, 2012, Pages 1234-1248

  Kumar, Virendra ^a   Gu, Yuhua ^a   Basu, Satrajit ^b   Berglund, Anders ^a   Eschrich, Steven A ^a   Schabath, Matthew B ^a   Forster, Kenneth ^a   Aerts, Hugo J W L ^c,d   Dekker, Andre ^c   Fenstermacher, David ^a   Goldgof, Dmitry B ^b   Hall, Lawrence O ^b   Lambin, Philippe ^c   Balagurunathan, Yoganand ^a   Gatenby, Robert A ^a   Gillies, Robert J ^a  

^a H LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE   (United States)

^b University of South Florida   (United States)

^c MAASTRICHT UNIVERSITY   (Netherlands)

^d HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Image features; Imaging; Radiomics; Segmentation; Tumor

Indexed keywords

FLUORODEOXYGLUCOSE F 18;

ARTICLE; COMPUTER ASSISTED EMISSION TOMOGRAPHY; COMPUTER ASSISTED TOMOGRAPHY; DATA ANALYSIS; DATA BASE; DATA EXTRACTION; DATA MINING; DIAGNOSTIC IMAGING; DYNAMIC CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING; HUMAN; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; IMAGING; LUNG NON SMALL CELL CANCER; NUCLEAR MAGNETIC RESONANCE IMAGING; PHENOTYPE; POSITRON EMISSION TOMOGRAPHY; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; QUANTITATIVE IMAGING; RADIOMICS; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; CARCINOMA, NON-SMALL-CELL LUNG; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG NEOPLASMS; MAGNETIC RESONANCE IMAGING; MEDICAL INFORMATICS; MULTIVARIATE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; PHANTOMS, IMAGING; POSITRON-EMISSION TOMOGRAPHY; RADIATION ONCOLOGY; REPRODUCIBILITY OF RESULTS; RISK FACTORS; SOFTWARE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84867139157     PISSN: 0730725X     EISSN: 18735894     Source Type: Journal    
DOI: 10.1016/j.mri.2012.06.010     Document Type: Article

References (97)

1. Lambin P., Rios-Velazquez E., Leijenaar R., Carvalho S., Stiphout R.V., Granton P., et al. Radiomics: extracting more information from medical images using advance feature analysis. Eur J Cancer 2012, 48(4):441-446.
2. Jaffe C.C. Measures of response: RECIST, WHO, and new alternatives. J Clin Oncol 2006, 24(20):3245-3251.
3. Burton A. RECIST: right time to renovate?. Lancet Oncol 2007, 8(6):464-465.
4. Rubin D.L. Creating and curating a terminology for radiology: ontology modeling and analysis. J Digit Imaging 2008, 21(4):355-362.
5. Opulencia P., Channin D.S., Raicu D.S., Furst J.D. Mapping LIDC, RadLex™, and lung nodule image features. J Digit Imaging 2011, 24(2):256-270.
6. Channin D.S., Mongkolwat P., Kleper V., Rubin D.L. The annotation and image mark-up project 1. Radiology 2009, 253(3):590-592.
7. Rubin DL, Mongkolwat P, Kleper V, Supekar K, Channin DS. Medical imaging on the semantic web: annotation and image markup. 2008. AAAI Spring Symposium Series, Semantic Scientific Knowledge Integration, Stanford University, 2008.
8. Jackson A., O'Connor J.P.B., Parker G.J.M., Jayson G.C. Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging. Clin Cancer Res 2007, 13(12):3449-3459.
9. Rose C.J., Mills S.J., O'Connor J.P.B., Buonaccorsi G.A., Roberts C., Watson Y., et al. Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps. Magn Reson Med 2009, 62(2):488-499.
10. Gibbs P., Turnbull L.W. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003, 50(1):92-98.
11. Canuto H.C., McLachlan C., Kettunen M.I., Velic M., Krishnan A.S., Neves A.A., et al. Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent. Magn Reson Med 2009, 61(5):1218-1224.
12. Segal E., Sirlin C.B., Ooi C., Adler A.S., Gollub J., Chen X., et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007, 25(6):675-680.
13. Diehn M., Nardini C., Wang D.S., McGovern S., Jayaraman M., Liang Y., et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci 2008, 105(13):5213.
14. Boellaard R., O'Doherty M.J., Weber W.A., Mottaghy F.M., Lonsdale M.N., Stroobants S.G., et al. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. Eur J Nucl Med Mol Imaging 2010, 37(1):181-200.
15. Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med 2009, 50(Suppl. 1):11S-20S.
16. Ollers M., Bosmans G., van Baardwijk A., Dekker A., Lambin P., Teule J., et al. The integration of PET-CT scans from different hospitals into radiotherapy treatment planning. Radiother Oncol 2008, 87(1):142-146.
17. Janssen M.H., Ollers M.C., van Stiphout R.G., Riedl R.G., van den Bogaard J., Buijsen J., et al. Blood glucose level normalization and accurate timing improves the accuracy of PET-based treatment response predictions in rectal cancer. Radiother Oncol 2010, 95(2):203-208.
18. Padhani A.R., Liu G., Koh D.M., Chenevert T.L., Thoeny H.C., Takahara T., et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009, 11(2):102-125.
19. Delakis I., Moore E.M., Leach M.O., De Wilde J.P. Developing a quality control protocol for diffusion imaging on a clinical MRI system. Phys Med Biol 2004, 49(8):1409-1422.
20. Yang X., Knopp M.V. Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol 2011, 2011:732848.
21. Galbraith S.M., Lodge M.A., Taylor N.J., Rustin G.J., Bentzen S., Stirling J.J., et al. Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis. NMR Biomed 2002, 15(2):132-142.
22. Makkat S., Luypaert R., Sourbron S., Stadnik T., De Mey J. Quantification of perfusion and permeability in breast tumors with a deconvolution-based analysis of second-bolus T1-DCE data. J Magn Reson Imaging 2007, 25(6):1159-1167.
23. Yang C., Stadler W.M., Karczmar G.S., Milosevic M., Yeung I., Haider M.A. Comparison of quantitative parameters in cervix cancer measured by dynamic contrast-enhanced MRI and CT. Magn Reson Med 2010, 63(6):1601-1609.
24. Priest A.N., Gill A.B., Kataoka M., McLean M.A., Joubert I., Graves M.J., et al. Dynamic contrast-enhanced MRI in ovarian cancer: initial experience at 3 tesla in primary and metastatic disease. Magn Reson Med 2010, 63(4):1044-1049.
25. McGrath D.M., Bradley D.P., Tessier J.L., Lacey T., Taylor C.J., Parker G.J. Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats. Magn Reson Med 2009, 61(5):1173-1184.
26. Jackson E, Ashton E, Evelhoch J, Buonocore M, Karczmar G, Rosen M, et al. Multivendor, multisite DCE-MRI phantom validation study. 2009. In: Proceedings of the 95th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA '10); December 2009; Chicago, IL, USA.
27. Armato S.G., McLennan G., Hawkins D., Bidaut L., McNitt-Gray M.F., Meyer C.R., et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 2011, 38(2):915-931.
28. Armato S.G., Meyer C.R., McNitt-Gray M.F., McLennan G., Reeves A.P., Croft B.Y., et al. The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software. Clin Pharmacol Ther 2008, 84(4):448-456.
29. Basu S, Hall L, Goldgof D, Gu Y, Kumar V, Choi J, et-al. Developing a classifier model for lung tumors in CT-scan images. IEEE International Conference on Systems, Man and Cybernetics (SMC 2011), Anchorage, Alaska, 10/2011.
30. Stroom J., Blaauwgeers H., van Baardwijk A., Boersma L., Lebesque J., Theuws J., et al. Feasibility of pathology-correlated lung imaging for accurate target definition of lung tumors. Int J Radiat Oncol Biol Phys 2007, 69(1):267-275.
31. Hojjatoleslami S., Kittler J. Region growing: a new approach. IEEE Trans Image Process 1998, 7(7):1079-1084.
32. Dehmeshki J., Amin H., Valdivieso M., Ye X. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans Med Imaging 2008, 27(4):467-480.
33. Sethian J.A. Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science 1999, Cambridge University Press, Cambridge. 2nd ed.
34. Malladi R., Sethian J.A., Vemuri B.C. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 1995, 17(2):158-175.
35. Gao H., Chae O. Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recognition 2010, 43(7):2406-2417.
36. Chen Y.T. A level set method based on the Bayesian risk for medical image segmentation. Pattern Recognition 2010, 43(11):3699-3711.
37. Krishnan K., Ibanez L., Turner W.D., Jomier J., Avila R.S. An open-source toolkit for the volumetric measurement of CT lung lesions. Opt Express 2010, 18(14):15256-15266.
38. Osher S., Sethian J.A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 1988, 79(1):12-49.
39. Boykov Y., Veksler O., Zabih R. Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 2001, 23(11):1222-1239.
40. So R.W.K., Tang T.W.H., Chung A. Non-rigid image registration of brain magnetic resonance images using graph-cuts. Pattern Recognition 2011, 2450-2467.
41. Xu N., Bansal R., Ahuja N. Object segmentation using graph cuts based active contours. IEEE 2003, 42:II-46-II-53.
42. Slabaugh G., Unal G. Graph cuts segmentation using an elliptical shape prior. IEEE 2005, II-1222-II-1225.
43. Liu X., Veksler O., Samarabandu J. Graph cut with ordering constraints on labels and its applications. IEEE 2008, 1-8.
44. Ye X., Beddoe G., Slabaugh G. Automatic graph cut segmentation of lesions in CT using mean shift superpixels. J Biomed Imaging 2010, 2010:19.
45. Liu W., Zagzebski J.A., Varghese T., Dyer C.R., Techavipoo U., Hall T.J. Segmentation of elastographic images using a coarse-to-fine active contour model. Ultrasound Med Biol 2006, 32(3):397-408.
46. He Q., Duan Y., Miles J., Takahashi N. A context-sensitive active contour for 2D corpus callosum segmentation. Int J Biomed Imaging 2007, 2007(3):24826.
47. Chen C., Li H., Zhou X., Wong S. Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening. J Microsc 2008, 230(2):177-191.
48. Suzuki K., Kohlbrenner R., Epstein M.L., Obajuluwa A.M., Xu J., Hori M. Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. Med Phys 2010, 37:2159.
49. Wang L., Li C., Sun Q., Xia D., Kao C.Y. Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imaging Graph 2009, 33(7):520-531.
50. Mortensen E.N., Barrett W.A. Interactive segmentation with intelligent scissors. Graph Models Image Process 1998, 60(5):349-384.
51. Souza A, Udupa JK, Grevera G, Sun Y, Odhner D, Suri N, Schnall MD. Iterative live wire and live snake: new user-steered 3D image segmentation paradigms. In: Medical Imaging 2006: Image Processing. Reinhardt JM, Pluim JP, editors. Proceedings of the SPIE, 2006;6144:1159-1165.
52. Lu K., Higgins W.E. Interactive segmentation based on the live wire for 3D CT chest image analysis. Int J Comput Assist Radiol Surg 2007, 2(3):151-167.
53. Lu K., Higgins W.E. Segmentation of the central-chest lymph nodes in 3D MDCT images. Comput Biol Med 2011, 41(9):780-789. Epub 2011 Jul 12.
54. Rexilius J., Hahn H.K., Schluter M., Bourquain H., Peitgen H.O. Evaluation of accuracy in MS lesion volumetry using realistic lesion phantoms. Acad Radiol 2005, 12(1):17-24.
55. Tai P., Van Dyk J., Yu E., Battista J., Stitt L., Coad T. Variability of target volume delineation in cervical esophageal cancer. Int J Radiat Oncol Biol Phys 1998, 42(2):277-288.
56. Cooper J.S., Mukherji S.K., Toledano A.Y., Beldon C., Schmalfuss I.M., Amdur R., et al. An evaluation of the variability of tumor-shape definition derived by experienced observers from CT images of supraglottic carcinomas (ACRIN protocol 6658). Int J Radiat Oncol Biol Phys 2007, 67(4):972-975.
57. Jameson M.G., Holloway L.C., Vial P.J., Vinod S.K., Metcalfe P.E. A review of methods of analysis in contouring studies for radiation oncology. J Med Imaging Radiat Oncol 2010, 54(5):401-410.
58. Warfield S.K., Zou K.H., Wells W.M. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 2004, 23(7):903-921.
59. Zijdenbos A.P., Dawant B.M., Margolin R.A., Palmer A.C. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging 1994, 13(4):716-724.
60. Holub O., Ferreira S.T. Quantitative histogram analysis of images. Comput Phys Commun 2006, 175(9):620-623.
61. El Naqa I., Grigsby P., Apte A., Kidd E., Donnelly E., Khullar D., et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognition 2009, 42(6):1162-1171.
62. O'Sullivan F., Roy S., O'Sullivan J., Vernon C., Eary J. Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics 2005, 6(2):293-301.
63. Jain A.K. Fundamentals of digital image processing 1988, Prentice-Hall, Inc., New Jersey.
64. Lam S.W.C. Texture feature extraction using gray level gradient based co-occurence matrices. IEEE 1996, 261:267-271.
65. Haralick R.M., Shanmugam K., Dinstein I.H. Textural features for image classification. IEEE Trans Syst Man Cybern 1973, 3(6):610-621.
66. Galloway M.M. Texture analysis using gray level run lengths. Comput Graph Image Process 1975, 4(2):172-179.
67. Castellano G., Bonilha L., Li L.M., Cendes F. Texture analysis of medical images. Clin Radiol 2004, 59(12):1061-1069.
68. Zinovev D., Raicu D., Furst J., Armato S.G. Predicting radiological panel opinions using a panel of machine learning classifiers. Algorithms 2009, 2(4):1473-1502.
69. Soh L.K., Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 1999, 37(2):780-795.
70. Suárez J., Gancedo E., Álvarez J.M., Morán A. Optimum compactness structures derived from the regular octahedron. Engineering Structures 2008, 30(11):3396-3398.
71. Tang X. Texture information in run-length matrices. IEEE Trans Image Process 1998, 7(11):1602-1609.
72. Kramer K., Goldgof D.B., Hall L.O., Remsen A. Increased classification accuracy and speedup through pair-wise feature selection for support vector machines. IEEE 2011, 318-324.
73. Song F., Guo Z., Mei D. Feature selection using principal component analysis. Yichang. IEEE 2010, 27-30.
74. Heshmati A., Amjadifard R., Shanbehzadeh J. ReliefF-based feature selection for automatic tumor classification of mammogram images. Tehran. IEEE 2011, 1-5.
75. Jain A., Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 1997, 19(2):153-158.
76. Fu J., Lee S.K., Wong S.T.C., Yeh J.Y., Wang A.H., Wu H. Image segmentation feature selection and pattern classification for mammographic microcalcifications. Comput Med Imaging Graph 2005, 29(6):419-429.
77. Liu J., Erdal S., Silvey S.A., Ding J., Riedel J.D., Marsh C.B., et al. Toward a fully de-identified biomedical information warehouse. AMIA Annu Symp Proc 2009, 2009:370-374.
78. Freymann J.B., Kirby J.S., Perry J.H., Clunie D.A., Jaffe C.C. Image data sharing for biomedical research - meeting HIPAA requirements for de-identification. J Digit Imaging 2012, 25(1):14-24.
79. Fenstermacher D.A., Wenham R.M., Rollison D.E., Dalton W.S. Implementing personalized medicine in a cancer center. Cancer J 2011, 17(6):528-536.
80. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531-537.
81. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995, 289-300.
82. Tusher V.G., Tibshirani R., Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001, 98(9):5116-5121.
83. Storey J.D. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat 2003, 2013-2035.
84. Reiner A., Yekutieli D., Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003, 19(3):368-375.
85. Jain A., Dubes R. Algorithms that cluster data 1988, Prentice Hall, Englewood Cliffs, NJ.
86. Wilkinson L., Friendly M. The history of the cluster heat map. Am Stat 2009, 63(2):179-184.
87. Eschrich S., Yang I., Bloom G., Kwong K.Y., Boulware D., Cantor A., et al. Molecular staging for survival prediction of colorectal cancer patients. J Clin Oncol 2005, 23(15):3526-3535.
88. Shedden K., Taylor J.M., Enkemann S.A., Tsao M.S., Yeatman T.J., Gerald W.L., et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 2008, 14(8):822-827.
89. Jolliffe I.T. Principal component analysis 2002, Springer, New York. 2nd ed.
90. Wold S., Ruhe A., Wold H., Dunn W. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Statist Comput 1984, 5:735.
91. Dupuy A., Simon R.M. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 2007, 99(2):147-157.
92. Simon R.M., Paik S., Hayes D.F. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst 2009, 101(21):1446-1452.
93. Efron B. Bootstrap methods: another look at the jackknife. Ann Stat 1979, 7(1):1-26.
94. Efron B. Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc 1983, 316-331.
95. Dougherty E.R. Small sample issues for microarray-based classification. Compar Funct Genom 2001, 2(1):28-34.
96. Kim S., Dougherty E.R., Barrera J., Chen Y., Bittner M.L., Trent J.M. Strong feature sets from small samples. J Comput Biol 2002, 9(1):127-146.
97. Braga-Neto U., Hashimoto R., Dougherty E.R., Nguyen D.V., Carroll R.J. Is cross-validation better than resubstitution for ranking genes?. Bioinformatics 2004, 20(2):253-258.