Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI

Physics in Medicine and Biology

Volumn 57, Issue 12, 2012, Pages 3833-3851

  Niaf, Emilie ^a,b,c   Rouvière, Olivier ^a,b,d,e   Mège Lechevallier, Florence ^d   Bratan, Flavie ^d   Lartizien, Carole ^c  

^a LABTAU   (France)

^b UNIVERSITÉ DE LYON   (France)

^c UNIVERSITÉ DE LYON   (France)

^d EDOUARD HERRIOT HOSPITAL   (France)

^e UNIVERSITÉ LYON 1   (France)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER THE ROC CURVE; BAYES CLASSIFIER; BENIGN TISSUES; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC PERFORMANCE; DYNAMIC CONTRAST ENHANCED MRI; DYNAMIC PARAMETERS; FEATURE SELECTION METHODS; FEATURE SETS; FIRST-ORDER STATISTICS; GOLD STANDARDS; GRADIENT FEATURE; GREY-LEVEL IMAGES; HARALICK FEATURES; HUMAN EXPERT; K-NEAREST NEIGHBOURS; LINEAR DISCRIMINANT ANALYSIS; MR IMAGES; MR SEQUENCE; MUTUAL INFORMATIONS; NONLINEAR SUPPORT VECTOR MACHINES; PROSTATE CANCERS; QUANTITATIVE EVALUATION; RADICAL PROSTATECTOMY; RECEIVER OPERATING CHARACTERISTIC CURVE ANALYSIS; REGIONS OF INTEREST; SVM CLASSIFIERS; TEST BENCHES;

COMPUTER AIDED DIAGNOSIS; DISEASES; HISTOLOGY; IMAGE RETRIEVAL; RATING; SUPPORT VECTOR MACHINES; TISSUE; UROLOGY;

MAGNETIC RESONANCE;

AGED; ARTICLE; COMPUTER ASSISTED DIAGNOSIS; DISCRIMINANT ANALYSIS; HUMAN; MALE; METHODOLOGY; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PROSTATE TUMOR; RADIOGRAPHY; RECEIVER OPERATING CHARACTERISTIC;

AGED; DIAGNOSIS, COMPUTER-ASSISTED; DISCRIMINANT ANALYSIS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PROSTATIC NEOPLASMS; ROC CURVE;

EID: 84862021630     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/57/12/3833     Document Type: Article

References (29)

1. Artan Y, Haider M A, Langer D L, Kwast T H van der, Evans A J, Yang Y, Wernick M N, Trachtenberg J and Yetik I S 2010 Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields IEEE Trans. Image Process. 19 244455
2. Canu S, Grandvalet Y, Guigue V and Rakotomamonjy A 2005 SVM and Kernel Methods: Matlab Toolbox (Perception Systemes et Information, INSA de Rouen, Rouen, France) (Available at http://asi.insa-rouen.fr/enseignants/∼arakotom/ toolbox/index.html)
3. Chan I, Wells 3rd W, 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 23908 (Pubitemid 37129745)
4. Cheikh A, Girouin N, Colombel M, Marechal J M, Gelet A, Bissery A, Rabilloud M, Lyonnet D and Rouvière O 2009 Evaluation of t2-weighted and dynamic contrast-enhanced MRI in localizing prostate cancer before repeat biopsy Eur. Radiol. 19 7708
5. Dickinson L et al 2011 Magnetic resonance imaging for the detection, localisation and characterisation of prostate cancer: recommendations from a European Consensus Meeting Eur. Urol. 59 47794
6. Ding C and Peng H 2005 Minimum redundancy feature selection from microarray gene expression data J. Bioinform. Comput. Biol. 3 185205 (Pubitemid 40561382)
7. Girouin N, Mège-Lechevallier F, Senes A T, Bissery A, Rabilloud M, Maréchal J M, Colombel M, Lyonnet D and Rouvière O 2007 Prostate dynamic contrast-enhanced mri with simple visual diagnostic criteria: Is it reasonable? Eur. Radiol. 17 1498509 (Pubitemid 46767598)
8. Haas G P, Delongchamps N, Brawley O W, Wang C Y and de la Roza G 2008 The worldwide epidemiology of prostate cancer: perspectives from autopsy studies Can. J. Urol. 15 386671 PMID: 18304396
9. Haider M A, van der Kwast T H, Tanguay J, Evans A J, Hashmi A T, Lockwood G and Trachtenberg J 2007 Combined T2-weighted and diffusion-weighted MRI for localization of prostate cancer Am. J. Roentgenol. 189 3238 (Pubitemid 47195916)
10. Haralick R M, Shanmugam K and Dinstein I 1973 Textural features for image classification IEEE Trans. Syst. Man Cybern. 3 61021
11. Kirkham A P, Emberton M and Allen 2006 How good is MRI at detecting and characterising cancer within the prostate? Eur. Urol. 50 116374 (Pubitemid 44692933)
12. Kozlowski P, Chang S D, Jones E C, Berean K W, Chen H and Goldenberg S L 2006 Combined diffusion-weighted and dynamic contrast-enhanced MRI for prostate cancer diagnosis. Correlation with biopsy and histopathology J. Magn. Reson. Imaging 24 10813
13. Langer D L, van der Kwast T H, Evans A J, Trachtenberg J, Wilson B C and Haider M A 2009 Prostate cancer detection with multiparametric MRI: logistic regression analysis of quantitative t2, diffusion-weighted imaging and dynamic contrast-enhanced MRI J. Magn. Reson. Imaging 30 32734
14. Lopes R, Ayache A, Makni N, Puech P, Villers A, Mordon S and Betrouni N 2011 Prostate cancer characterization on mr images using fractal features Med. Phys. 38 8395
15. 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 161125 (Pubitemid 41812028)
16. Madabhushi A, Shi J, Rosen M, Tomaszewski J E and Feldman M D 2006 Comparing classification performance of feature ensembles: detecting prostate cancer from high resolution MRI Computer Vision Methods in Medical Image Analysis (In Conjunction with ECCV vol 4241) (Berlin: Springer) pp 2536 (Pubitemid 44703393)
17. Niemeijer M, Loog M, Abramoff M D, Viergever M A, Prokop M and van Ginneken B 2011 On combining computer-aided detection systems IEEE Trans. Med. Imaging 30 21523
18. Ocak I, Bernardo M, Metzger G, Barrett T, Pinto P, Albert P S and Choyke P L 2007 Dynamic contrast-enhanced MRI of prostate cancer at 3 T: a study of pharmacokinetic parameters Am. J. Roentgenol. 189 W192201
19. Ozer S, Langer D L, Liu X, Haider M A, van der Kwast T H, Evans A J, Yang M N Wernick Y and Yetik I S 2010 Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI Med. Phys. 37 1873
20. Platt J 1999 Probabilistic outputs for support vector machines Advances in Large Margin Classifiers (Cambridge, MA: MIT Press) pp 6174
21. Puech P, Betrouni N, Makni N, Dewalle A S, Villers A and Lemaitre L 2009 Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results Int. J. Comput. Assist. Radiol. Surg. 4 110
22. Rutter C M 2000 Bootstrap estimation of diagnostic accuracy with patient-clustered data Acad. Radiol. 7 4139 (Pubitemid 30341573)
23. Vapnik V N 1998 Statistical Learning Theory 1st edn (New York: Wiley-Interscience)
24. Viswanath S, Bloch B, Genega E, Rofsky N, Lenkinski R, Chappelow J, Toth R and Madabhushi A 2008a A comprehensive segmentation, registration and cancer detection scheme on 3 Tesla in vivo prostate DCE-MRI MICCAI: Int. Conf. on Medical Image Computing and Computer-Assisted Intervention pp 6629
25. Viswanath S, Bloch B N, Rosen M, Chappelow J, Toth R, Rofsky N, Lenkinski R, Genega E, Kalyanpur A and Madabhushi A 2009 Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol SPIE Medical Imaging vol 7260 (Miami: FL) 72603I
26. Viswanath S, Rosen M and Madabhushi A 2008b A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery SPIE Medical Imaging (San Diego, CA,) 69150U.112
27. Vos P C, Hambrock T, Barentsz J O and Huisman H J 2010 Computer-assisted analysis of peripheral zone prostate lesions using t2-weighted and dynamic contrast enhanced t1-weighted MRI Phys. Med. Biol. 55 1719
28. Vos P C, Hambrock T, Kaa C A Hulsbergen van de, Futterer J J, Barentsz J O and Huisman H J 2008 Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI Med. Phys. 35 888 (Pubitemid 351322670)
29. Wiart M, Curiel L, Gelet A, Lyonnet D, Chapelon J Y and Rouvière O 2007 Influence of perfusion on high-intensity focused ultrasound prostate ablation: a first-pass MRI study Magn. Reson. Med. 58 11927 (Pubitemid 47229924)