A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging

Computerized Medical Imaging and Graphics

Volumn 46, Issue , 2015, Pages 219-226

  Giannini, Valentina ^a   Mazzetti, Simone ^a   Vignati, Anna ^a   Russo, Filippo ^a   Bollito, Enrico ^b   Porpiglia, Francesco ^b   Stasi, Michele ^a   Regge, Daniele ^a  

^a CANDIOLO CANCER INSTITUTE   (Italy)

^b UNIVERSITY OF TURIN   (Italy)

Author keywords

Computer aided detection; Image analysis; Multiparametric MRI; Prostate cancer; SVM classifier

Indexed keywords

COMPUTER AIDED ANALYSIS; COMPUTER PERIPHERAL EQUIPMENT; DIAGNOSIS; DISEASES; IMAGE ANALYSIS; MAGNETIC LEVITATION VEHICLES; MAGNETIC RESONANCE IMAGING; UROLOGY;

AREA UNDER THE ROC CURVE; COMPUTER AIDED DETECTION; COMPUTER AIDED DIAGNOSIS SYSTEMS; COMPUTER AIDED DIAGNOSIS(CAD); HISTOLOGICAL SECTION; PROSTATE CANCER DETECTION; PROSTATE CANCERS; SVM CLASSIFIERS;

COMPUTER AIDED DIAGNOSIS;

GADOBUTROL;

ADULT; AGED; ARTICLE; AUTOMATION; CANCER DIAGNOSIS; CANCER RADIOTHERAPY; CANCER STAGING; COHORT ANALYSIS; COMPUTER AIDED DIAGNOSIS; DIAGNOSTIC PROCEDURE; FALSE POSITIVE RESULT; HUMAN; MAJOR CLINICAL STUDY; MALE; MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; PROSTATE BIOPSY; PROSTATE CANCER; PROSTATECTOMY; RADIOLOGIST; RECEIVER OPERATING CHARACTERISTIC; TISSUE SECTION; ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; MULTIMODAL IMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; PROSTATE; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MALE; MULTIMODAL IMAGING; PATTERN RECOGNITION, AUTOMATED; PROSTATE; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84983127840     PISSN: 08956111     EISSN: 18790771     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2015.09.001     Document Type: Article

References (40)

1. Niaf E., Lartizien C., Bratan F., Roche L., Rabilloud M., Mège-Lechevallier F., et al. Prostate focal peripheral zone lesions: characterization at multiparametric MR imaging-influence of a computer-aided diagnosis system. Radiology 2014, 271(3):761-769.
2. Wang S., Burtt K., Turkbey B., Choyke P., Summers R.M. Computer aided-diagnosis of prostate cancer on multiparametric MRI: a technical review of current research. BioMed Res Int 2014, 2014:789561.
3. Rothwax J.T., George A.K., Wood B.J., Pinto P.A. Multiparametric MRI in biopsy guidance for prostate cancer: fusion-guided. BioMed Res Int 2014, 2014:439171.
4. Barentsz J.O., Richenberg J., Clements R., et al. ESUR prostate MR guidelines 2012. Eur Radiol 2012, 22:746-757.
5. Litjens G., Debats O., Barentsz J., Karssemeijer N., Huisman H. Computer-aided detection of prostate cancer in MRI. IEEE Trans Med Imaging 2014, 33(5):1083-1092.
6. Vos P.C., Barentsz J.O., Karssemeijer N., Huisman H.J. Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis. Phys Med Biol 2012, 57(6):1527-1542.
7. Litjens G.J.S., Vos P.C., Barentsz J.O., Karssemeijer N., Huisman H.J. Automatic computer aided detection of abnormalities in multi-parametric prostate MRI, Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630T, (4 March 2011); . doi:10.1117/12.877844.
8. Iussich G., Correale L., Senore C., Hassan C., Segnan N., Campanella D., et al. Computer-aided detection for computed tomographic colonography screening: a prospective comparison of a double-reading paradigm with first-reader computer-aided detection against second-reader computer-aided detection. Invest Radiol 2014, 49(3):173-182.
9. Chan I., Wells W., Mulkern R.V., Haker S., Zhang J., Zou K.H., et al. Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. Med Phys 2003, 30(9):2390-2398.
10. Niaf E., Rouvière O., Mège-Lechevallier F., Bratan F., Lartizien C. Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI. Phys Med Biol 2012, 57(12):3833-3851.
11. Stamey T.A., Freiha F.S., McNeal J.E., Redwine E.A., Whittemore A.S., Schmid H.P. Localized prostate cancer relationship of tumor volume to clinical significance for treatment of prostate cancer. Cancer 1993, 71(Suppl. 3):933-938.
12. Montironi R., Lopez-Beltran A., Mazzucchelli R., Scarpelli M., Bollito E. Assessment of radical prostatectomy specimens and diagnostic reporting of pathological findings. Pathologica 2001, 93(3):226-232.
13. Johnson H.J., McCormick M., Ibañez L. The ITK software guide 2013, Kitware, Inc, 〈http://www.itk.org/ItkSoftwareGuide.pdf〉. 3rd ed.
14. Giannini V., Vignati A., De Luca M., Mazzetti S., Russo F., Armando E., et al. A novel and fully automated registration method for prostate cancer detection using Multiparametric Magnetic Resonance Imaging. J Med Imag Health 2015, (Accepted for publication in J Med Imag health in Dec. 2015).
15. McVary K.T. Clinical evaluation of benign prostatic hyperplasia. Rev Urol 2003, 5(Suppl. 4):S3-S11.
16. Otsu N. A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern 1789, 9(1):62-66.
17. Peng Y., Jiang Y., Antic T., Giger M.L., Eggener S.E., Oto A. Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a cross-image study. Radiology 2014, 271(2):461-471.
18. Giannini V., Vignati A., Mirasole S., Mazzetti S., Russo F., Stasi M., et al. MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness. Comput Methods Biomech Biomed Eng: Imaging Visual 2014, [Epub ahead of print]. 10.1080/21681163.2014.910476.
19. Tustison N.J., Avants B.B., Cook P.A., Zheng Y., Egan A., Yushkevich P.A., et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010, 29(6):1310-1320.
20. Khalifa F., Soliman A., El-Baz A., Abou El-Ghar M., El-Diasty T., Gimel'farb G., et al. Models and methods for analyzing DCE-MRI: a review. Med Phys 2014, 41(12):124301.
21. Fan X., Medved M., Karczmar G.S., Yang C., Foxley S., Arkani S., et al. Diagnosis of suspicious breast lesions using an empirical mathematical model for dynamic contrast-enhanced MRI. Magn Reson Imaging 2007, 25(5):593-603.
22. Gliozzi A.S., Mazzetti S., Delsanto P.P., Regge D., Stasi M. Phenomenological universalities: a novel tool for the analysis of dynamic contrast enhancement in magnetic resonance imaging. Phys Med Biol 2011, 56(3):573-586.
23. Horsfield M.A., Morgan B. Algorithms for calculation of kinetic parameters from T1-weighted dynamic contrast-enhanced magnetic resonance imaging. J Magn Reson Imaging 2004, 20:723-729.
24. 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.
25. Medved M., Karczmar G., Yang C., Dignam J., Gajewski T.F., Kindler H., et al. Semiquantitative analysis of dynamic contrast enhanced MRI in cancer patients: Variability and changes in tumor tissue over time. J Magn Reson Imaging 2004, 20(1):122-128.
26. Castorina P., Delsanto P.P., et al. Classification scheme for phenomenological universalities in growth problems in physics and other sciences. Phys Rev Lett 2006, 96:188701.
27. Markwardt, CB. Non-linear least squares fitting in IDL with MPFIT, Proc. Astronomical Data Analysis Software and astronomical data analysis software and systems 2009, vol. 411, Quebec, Canada.
28. Yu L., Liu H. Feature selection for high dimensional data: a fast correlation-based filter solution. Proc. 20th Int. Conf. on machine learning (ICML-2003) 2003.
29. Chang C.C., Lin C.J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011, 2:1-27.
30. Matsuki M., Inada Y., Tatsugami F., Tanikake M., Narabayashi I., Katsuoka Y. Diffusion-weighted MR imaging for urinary bladder carcinoma: initial results. Eur Radiol 2007, 17:201-204.
31. Langer D.L., van der Kwast T.H., Evans A.J., Trachtenberg J., Wilson B.C., Haider M.A. Prostate cancer detection with multi-parametric MRI: logistic regression analysis of quantitative T2, diffusion weighted imaging, and dynamic contrast-enhanced MRI. J Magn Reson Imaging 2009, 30(2):327-334.
32. Vos P.C., Hambrock T., Barenstz J.O., Huisman H.J. Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI. Phys Med Biol 2010, 55(6):1719-1734.
33. Ross H.M., Kryvenko O.N., Cowan J.E., Simko J.P., Wheeler T.M., Epstein J.I. Do adenocarcinomas of the prostate with Gleason score (GS) ≤6 have the potential to metastasize to lymph nodes?. Am J Surg Pathol 2012, 36(9):1346-1352.
34. Loeb S., Bjurlin M.A., Nicholson J., Tammela T.L., Penson D.F., Carter H.B., et al. Overdiagnosis and overtreatment of prostate cancer. Eur Urol 2014, 65(6):1046-1055.
35. Le J.D., Tan N., Shkolyar E., Lu D.Y., Kwan L., Marks L.S., et al. Multifocality and prostate cancer detection by multiparametric magnetic resonance imaging: correlation with whole-mount histopathology. Eur Urol 2014, (Sep). (Epub ahead of print; pii: S0302-2838(14)00891-4). 10.1016/j.eururo.2014.08.079.
36. Chappelow J., Viswanath S., Monaco J., Rosen M., Tomaszewski J., Feldman M., et al. Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer. Proc. SPIE 6915, medical imaging 2008: computer-aided diagnosis, 69150V 2008, 10.1117/12.770703.
37. Shah V., Turkbey B., Mani H., Pang Y., Pohida T., Merino M.J., et al. Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging. Med Phys 2012, 39(7):4093-4103. 10.1118/1.4722753.
38. De Luca M1, Giannini V., Vignati A., Mazzetti S., Bracco C., Stasi M., et al. A fully automatic method to register the prostate gland on T2-weighted and EPI-DWI images. Conf Proc IEEE Eng Med Biol Soc 2011, 2011:8029-8032. 10.1109/IEMBS.2011.6091980.
39. Viswanath S., Bloch B.N., Rosen M., Chappelow J., Toth R., Rofsky N., Integrating Structural, et al. Functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI. Proc Soc Photo Opt Instrum Eng 2009, 7260(Feb):72603I.
40. Melbourne A., Hipwell J., Modat M., Mertzanidou T., Huisman H., Ourselin S., et al. The effect of motion correction on pharmacokinetic parameter estimation in dynamic-contrast-enhanced MRI. Phys Med Biol 2011, 56:7693-7708.