A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI

Computerized Medical Imaging and Graphics

Volumn 41, Issue , 2015, Pages 37-45

  Haq, Nandinee Fariah ^a   Kozlowski, Piotr ^a   Jones, Edward C ^a   Chang, Silvia D ^a   Goldenberg, S Larry ^a   Moradi, Mehdi ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Dynamic contrast enhanced MRI; LASSO; PCA; Prostate cancer; SVM

Indexed keywords

DIAGNOSIS; DISEASES; FEATURE EXTRACTION; PHARMACOKINETICS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; TISSUE; UROLOGY;

DYNAMIC CONTRAST ENHANCED; DYNAMIC CONTRAST ENHANCED MRI; LASSO; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; PHARMACOKINETIC PARAMETERS; PROSTATE CANCER DETECTION; PROSTATE CANCERS; SUPPORT VECTOR MACHINE CLASSIFIERS;

MAGNETIC RESONANCE IMAGING;

ADULT; AGED; ARTICLE; CANCER DIAGNOSIS; CANCER TISSUE; CONTRAST ENHANCEMENT; HUMAN; IMAGE ANALYSIS; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROSTATE CANCER; PROSTATECTOMY; QUANTITATIVE ANALYSIS; SUPPORT VECTOR MACHINE; TREATMENT OUTCOME; ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; MACHINE LEARNING; MULTIMODAL IMAGING; PATHOLOGY; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS;

AGED; ALGORITHMS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; MULTIMODAL IMAGING; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84923366740     PISSN: 08956111     EISSN: 18790771     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2014.06.017     Document Type: Article

References (40)

1. Jemal A., Bray F., Center M.M., Ferlay J., Ward E., Forman D. Global cancer statistics. CA: Cancer J Clin 2011, 61(2):69-90.
2. Hegde J.V., Mulkern R.V., Panych L.P., Fennessy F.M., Fedorov A., Maier S.E., et al. Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging 2013, 37(5):1035-1054.
3. O'dowd G.J., Miller M.C., Orozco R., Veltri R.W. Analysis of repeated biopsy results within 1 year after a noncancer diagnosis. Urology 2000, 55(4):553-558.
4. Steyerberg E., Roobol M., Kattan M., Van der Kwast T., De Koning H., Schröder F. Prediction of indolent prostate cancer: validation and updating of a prognostic nomogram. J Urol 2007, 177(1):107-112.
5. Poon P., McCallum R., Henkelman M., Bronskill M., Sutcliffe S., Jewett M., et al. Magnetic resonance imaging of the prostate. Radiology 1985, 154(1):143-149.
6. Hricak H., Dooms G., Jeffrey R.B., Avallone A., Jacobs D., Benton W., et al. Prostatic carcinoma: staging by clinical assessment, CT, and MR imaging. Radiology 1987, 162(2):331-336.
7. Bloch B.N., Rofsky N.M., Baroni R.H., Marquis R.P., Pedrosa I., Lenkinski R.E. 3Tesla magnetic resonance imaging of the prostate with combined pelvic phased-array and endorectal coils: initial experience. Academic Radiology 2004, 11(8):863-867.
8. Augustin H., Fritz G., Ehammer T., Auprich M., Pummer K. Accuracy of 3-Tesla magnetic resonance imaging for the staging of prostate cancer in comparison to the Partin tables. Acta Radiologica 2009, 50(5):562-569.
9. Fütterer J.J., Heijmink S.W., Scheenen T.W., Jager G.J., Hulsbergen-Van de Kaa C.A., Witjes J.A., et al. Prostate cancer: local staging at 3-T endorectal MR imaging - early experience. Radiology 2006, 238(1):184-191.
10. Lv D., Guo X., Wang X., Zhang J., Fang J. Computerized characterization of prostate cancer by fractal analysis in MR images. J Magn Reson Imaging 2009, 30(1):161-168.
11. Kurhanewicz J., Vigneron D., Carroll P., Coakley F. Multiparametric magnetic resonance imaging in prostate cancer: present and future. Curr Opin Urol 2008, 18(1):71-77.
12. Barentsz J.O., Engelbrecht M., Jager G.J., Witjes J.A., de La Rosette J., van der Sanden B.P., et al. Fast dynamic gadolinium-enhanced MR imaging of urinary bladder and prostate cancer. J Magn Reson Imaging 1999, 10(3):295-304.
13. Buckley D.L., Roberts C., Parker G.J., Logue J.P., Hutchinson C.E. Prostate cancer: evaluation of vascular characteristics with dynamic contrast-enhanced T1-weighted MR imaging - initial experience. Radiology 2004, 233(3):709-715.
14. Padhani A.R., Gapinski C.J., Macvicar D.A., Parker G.J., Suckling J., Revell P.B., et al. Dynamic contrast enhanced MRI of prostate cancer: correlation with morphology and tumour stage, histological grade and PSA. Clin Radiol 2000, 55(2):99-109.
15. Kozlowski P., Chang S.D., Meng R., Mädler B., Bell R., Jones E.C., et al. Combined prostate diffusion tensor imaging and dynamic contrast enhanced MRI at 3T quantitative correlation with biopsy. Magn Reson Imaging 2010, 28(5):621-628.
16. Moradi M., Salcudean S.E., Chang S.D., Jones E.C., Buchan N., Casey R.G., et al. Multiparametric MRI maps for detection and grading of dominant prostate tumors. J Magn Reson Imaging 2012, 35(6):1403-1413.
17. Ozer S., Langer D.L., Liu X., Haider M.A., van der Kwast T.H., Evans A.J., et al. Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. Med Phys 2010, 37:1873-1883.
18. Tofts P.S., Berkowitz B., Schnall M.D. Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magn Res Med 1995, 33(4):564-568.
19. Parker G.J., Roberts C., Macdonald A., Buonaccorsi G.A., Cheung S., Buckley D.L., et al. Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med 2006, 56(5):993-1000.
20. Meng R., Chang S.D., Jones E.C., Goldenberg S.L., Kozlowski P. Comparison between population average and experimentally measured arterial input function in predicting biopsy results in prostate cancer. Acad Radiol 2010, 17(4):520-525.
21. Roberts C., Issa B., Stone A., Jackson A., Waterton J.C., Parker G.J. Comparative study into the robustness of compartmental modeling and model-free analysis in DCE-MRI studies. J Magn Reson Imaging 2006, 23(4):554-563.
22. Eyal E., Degani H. Model-based and model-free parametric analysis of breast dynamic-contrast-enhanced MRI. NMR Biomed 2009, 22(1):40-53.
23. Engelbrecht M.R., Huisman H.J., Laheij R.J., Jager G.J., van Leenders G.J., Hulsbergen-Van De Kaa C.A., et al. Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. Radiology 2003, 229(1):248-254.
24. Hoeks C.M., Barentsz J.O., Hambrock T., Yakar D., Somford D.M., Heijmink S.W., et al. Prostate cancer: multiparametric MR imaging for detection, localization, and staging. Radiology 2011, 261(1):46-66.
25. Salomao D.R., Graham S.D., Bostwick D.G. Microvascular invasion in prostate cancer correlates with pathologic stage. Arch Pathol Lab Med 1995, 119(11):1050-1054.
26. van Niekerk C.G., van der Laak J.A., Börger M.E., Huisman H.J., Witjes J.A., Barentsz J.O., et al. Computerized whole slide quantification shows increased microvascular density in pT2 prostate cancer as compared to normal prostate tissue. Prostate 2009, 69(1):62-69.
27. Eyal E., Badikhi D., Furman-Haran E., Kelcz F., Kirshenbaum K.J., Degani H. Principal component analysis of breast DCE-MRI adjusted with a model-based method. J Magn Reson Imaging 2009, 30(5):989-998.
28. Eyal E., Bloch B.N., Rofsky N.M., Furman-Haran E., Genega E.M., Lenkinski R.E., et al. Principal component analysis of dynamic contrast enhanced MRI in human prostate cancer. Investig Radiol 2010, 45(4):174-181.
29. Parker G.J., Suckling J., Tanner S.F., Padhani A.R., Revell P.B., Husband J.E., et al. Probing tumor microvascularity by measurement, analysis and display of contrast agent uptake kinetics. J Magn Reson Imaging 1997, 7(3):564-574.
30. Drew B., Jones E.C., Reinsberg S., Yung A.C., Goldenberg S.L., Kozlowski P. Device for sectioning prostatectomy specimens to facilitate comparison between histology and in vivo MRI. J Magn Reson Imaging 2010, 32(4):992-996.
31. Klein S., Staring M., Murphy K., Viergever M.A., Pluim J.P. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 2010, 29(1):196-205.
32. Abdi H., Williams L.J. Principal component analysis. Wiley Interdiscip Rev: Comput Stat 2010, 2(4):433-459.
33. Tibshirani R. Regression shrinkage and selection via the LASSO. J R Stat Soc B: Methodol 1996, 58(1):267-288.
34. Friedman J., Hastie T., Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010, 33(1):1-22.
35. Tofts P.S., Brix G., Buckley D.L., Evelhoch J.L., Henderson E., Knopp M.V., et al. Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999, 10(3):223-232.
36. Chang C.C., Lin C.J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011, 2(3). 27:1-27:27.
37. Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 1999, 10(3):61-74.
38. Wu T.F., Lin C.J., Weng R.C. Probability estimates for multi-class classification by pairwise coupling. J Mach Learn Res 2004, 5(975-1005):4.
39. Haq N.F., Kozlowski P., Jones E.C., Chang S.D., Goldenberg S.L., Moradi M. Improved parameter extraction and classification for dynamic contrast enhanced MRI of prostate. SPIE Medical Imaging, vol. 9035 2014, International Society for Optics and Photonics, p. 903511-903511-11.
40. de Rooij M., Hamoen E.H.J., Fütterer J.J., Barentsz J.O., Rovers M.M. Accuracy of multiparametric MRI for prostate cancer detection: a meta-analysis. Am J Roentgenol 2014, 202(2):343-351.