A diffusion-weighted imaging based diagnostic system for early detection of prostate cancer


A new framework for early diagnosis of prostate cancer using Diffusion-Weighted Imaging (DWI) is proposed. The proposed diagnostic approach consists of the following four steps to detect locations that are suspicious for prostate cancer: 1) In the first step, we isolate the prostate from the surrounding anatomical structures based on a Maximum A Posteriori (MAP) estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of prostate tissues and its background (surrounding anatomical structures); 2) In order to take into account any local deformation between the segmented prostates at different b-values that could occur during the scanning process due to local motion, a non-rigid registration algorithm is employed; 3) A KNN-based classifier is used to classify the prostate into benign or malignant based on three appearance features extracted from registered images; and 4) The tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Preliminary experiments on 28 patients (17 malignant and 11 benign) resulted in 100% correct classification, showing that the proposed method is a promising supplement to current technologies (biopsy-based diagnostic systems) for the early diagnosis of prostate cancer.

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Firjani, A. , Elnakib, A. , Khalifa, F. , Gimel’farb, G. , El-Ghar, M. , Elmaghraby, A. and El-Baz, A. (2013) A diffusion-weighted imaging based diagnostic system for early detection of prostate cancer. Journal of Biomedical Science and Engineering, 6, 346-356. doi: 10.4236/jbise.2013.63A044.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Siegel, R., DeSantis, C., Virgo, K., Stein, K., Mariotto, A., Smith, T., et al. (2012) Cancer treatment and survivorship statistics. A Cancer Journal for Clinicians, 62, 220-241.
[2] American Cancer Society (2012) Cancer treatment and survivorship facts & figures 2012-2013. http://www.cancer.org/acs
[3] Deem, S., DeFade, B., Lohri, J., Tierney, J., Modak, A. and Emmett, M. (2010) Prostate cancer screening: A primary care survey. Health, 2, 1179-1183. doi:10.4236/health.2010.210173
[4] Stenman, U.H., Leinonen, J., Zhang, W.M. and Finne, P. (1999) Prostate-specific antigen. Seminars in Cancer Biology, 9, 83-93. doi:10.1006/scbi.1998.0086
[5] Halpern, E.J. (2006) Contrast-enhanced ultrasound imaging of prostate cancer. Urology, 8, 29-37.
[6] Ives, E.P., Burke, M.A., Edmonds, P.R., Gomella, L.G. and Halpern, E.J. (2005) Quantitative computed tomo-graphy perfusion of prostate cancer: Correlation with whole-mount pathology. Clinical Prostate Cancer, 4, 109-112. doi:10.3816/CGC.2005.n.018
[7] Shah, N., Sattar, A., Benanti, M., Hollander, S. and Cheuck, L. (2006) Magnetic resonance spectroscopy as an imaging tool for cancer: A review of the literature. Journal of the American Osteopathic Association, 106, 23-27.
[8] Noworolski, S.M., Henry, R.G., Vigneron, D.B. and Kur-hanewicz, J. (2005) Dynamic contrast-enhanced MRI in normal and abnormal prostate tissues as defined by biopsy, MRI, and 3D MRSI. Magnetic Resonance in Medicine, 53, 249-255. doi:10.1002/mrm.20374
[9] Kajihara, H., Hayashida, Y., Murakami, R., Katahira, K., Nishimura, R., Hamada, Y., et al. (2009) Usefulness of diffusion-weighted imaging in the localization of prostate cancer. International Journal of Radiation Oncology Biology Physics, 74, 399-403. doi:10.1016/j.ijrobp.2008.08.017
[10] Ellis, W.J. and Brawer, M.K. (1995) Repeat prostate needle biopsy: Who needs it? Journal of Urology, 153, 1496-1498. doi:10.1016/S0022-5347(01)67444-5
[11] Applewhite, J.C., Matlaga, B.R., McCullough, D.L. and Hall, M.C. (2001) Transrectal ultrasound and biopsy in the early diagnosis of prostate cancer. Cancer Control Journal, 8, 141-150.
[12] Gossner, J. (2012) Computed tomography of the prostate-A review. The Internet Journal of Radiology, 14, 1.
[13] Bouchelouche, K., Turkbey, B., Choyke, P. and Capala, J. (2010) Imaging prostate cancer: An update on positron emission tomography and magnetic resonance imaging. Current Urology Reports, 11, 180-190. doi:10.1007/s11934-010-0105-9
[14] Fuchsjager, M., Shukla-Dave, A., Akin, O., Barentsz, J. and Hricak, H. (2008) Prostate cancer imaging. Acta Radiologica, 49, 107-120. doi:10.1080/02841850701545821
[15] Choi, Y.J., Kim, J.K., Kim, N., Kim, K.W., Choi, E.K. and Cho, K.S. (2007) Functional MR imaging of prostate cancer. RadioGraphics, 27, 63-75. doi:10.1148/rg.271065078
[16] Zakian, K.L., Eberhardt, S., Hricak, H., Shukla-Dave, A., Kleinman, S., Muruganandham, M., et al. (2003) Transition zone prostate cancer: Metabolic characteristics at 1H MR spectroscopic imaging-initial results. Radiology, 229, 241-247. doi:10.1148/radiol.2291021383
[17] Punwani, S., Emberton, M., Walkden, M., Sohaib, A., Freeman, A., Ahmed, H., et al. (2012) Prostatic cancer surveillance following whole-gland high-intensity focused ultrasound: Comparison of MRI and prostate-specific an-tigen for detection of residual or recurrent disease. British Journal of Radiology, 85, 720-728. doi:10.1259/bjr/61380797
[18] Bhave, G., Lewis, Julia, B. and Chang, S. (2008) Association of gadolinium based magnetic resonance imaging contrast agents and nephrogenic systemic fibrosis. Journal of Urology, 180, 830-835. doi:10.1016/j.juro.2008.05.005
[19] Yu, K.K. and Hricak, H. (2000) Imaging prostate cancer. Radiologic Clinics of North America, 38, 59-85. doi:10.1016/S0033-8389(05)70150-0
[20] Ikonen, S., Karkkainen, P., Kivisaari, L., Salo, J.O., Taari, K., Vehmas, T., et al. (2001) Endorectal magnetic resonance imaging of prostatic cancer: Comparison between fat-suppressed T2-weighted fast spin echo and three-dimensional dual-echo, steady-state sequences. European Radiology, 11, 236-241. doi:10.1007/s003300000598
[21] Shimofusa, R., Fujimoto, H., Akamata, H., Motoori, K., Yamamoto, S., Ueda, T., et al. (2005) Diffusion-weighted imaging of prostate cancer. Journal of Computer Assisted Tomography, 29, 149-153. doi:10.1097/01.rct.0000156396.13522.f2
[22] Hacklander, T., Scharwachter, C., Golz, R. and Mertens, H. (2006) Value of diffusion-weighted imaging for diagnosing vertebral metastases due to prostate cancer in comparison to other primary tumors. Rofo, 178, 416-424.
[23] Yoshimitsu, K., Kiyoshima, K., Irie, H., Tajima, T., Asayama, Y., Hirakawa, M., et al. (2008) Usefulness of apparent diffusion coefficient map in diagnosing prostate carcinoma: Correlation with stepwise histopathology. Journal of Magnetic Resonance Imaging, 27, 132-139. doi:10.1002/jmri.21181
[24] Lim, H.K., Kim, J.K., Kim, K.A. and Cho, K.S. (2009) Prostate cancer: Apparent diffusion coefficient map with T2-weighted images for detection a multireader study. Radiology, 250, 145-151. doi:10.1148/radiol.2501080207
[25] Haider, M.A., van der Kwast, T.H., Tanguay, J., Evans, A.J., Hashmi, A.T., Lockwood, G., et al. (2007) Combined T2-weighted and diffusion-weighted MRI for localization of prostate cancer. American Journal of Roentgenology, 189, 323-328. doi:10.2214/AJR.07.2211
[26] Padhani, A.R., Liu, G., Koh, D.M., Chenevert, T.L., Thoeny, H.C., Takahara, T., et al. (2009) Diffusion- weighted magnetic resonance imaging as a cancer bio-marker: Consensus and recommendations. Neoplasia, 11, 102-125.
[27] Kitajima, K., Kaji, Y., Fukabori, Y., Yoshida, K., Suganuma, N. and Sugimura, K. (2010) Prostate cancer detection with 3T MRI: Comparison of diffusion-weighted imaging and dynamic contrast-enhanced MRI in combination with T2-weighted imaging. Journal of Magnetic Resonance Imaging, 31, 625-631. doi:10.1002/jmri.22075
[28] Iwazawa, J., Mitani, T., Sassa, S. and Ohue, S. (2011) Prostate cancer detection with MRI: Is dynamic contrast-enhanced imaging necessary in addition to diffusion-weighted imaging? Diagnostic and Interventional Radiology, 17, 243-248.
[29] Kim, C.K., Park, B.K. and Lee, H.M. (2009) Prediction of locally recurrent prostate cancer after radiation therapy: Incremental value of 3T diffusion-weighted MRI. Journal of Magnetic Resonance Imaging, 29, 391-397. doi:10.1002/jmri.21645
[30] Tan, C.H., Wang, J. and Kundra, V. (2011) Diffusion weighted imaging in prostate cancer. European Radiology, 21, 593-603. doi:10.1007/s00330-010-1960-y
[31] Chan, I., Wells, III, W., Mulkern, R.V., Haker, S., Zhang, J., Zou, K.H., et al. (2003) Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. Journal of Medical Physics, 30, 2390-2398. doi:10.1118/1.1593633
[32] Huisman, H., Vos, P., Litjens, G., Hambrock, T. and Barentsz, J. (2010) Computer aided detection of prostate cancer using t2w, DWI and DCE-MRI: Methods and clinical applications. Proceedings of International Workshop, Held in Conjunction with MICCAI 2010, Beijing, 20-24 September 2010, 4-14.
[33] Frangi, A.F., Niessen, W.J., Vincken, K.L. and Viergever, M.A. (1998) Multiscale vessel enhancement filtering. Proceedings of the 1st Annual International Conference of the MICCA, Cambridge, 11-13 October 1998, 130-137.
[34] Viswanath, S., Bloch, B.N., Genega, E., Rofsky, N., Lenkinski, R., Chappelow, J., et al. (2008) A comprehensive segmentation, registration, and cancer detection scheme on 3 Tesla in vivo prostate DCE-MRI. Medical Image Computing and Computer-Assisted Intervention, 11, 662- 669.
[35] Langer, Deanna, L., van der Kwast Theodorus, H., Evans, Andrew, J., et al. (2009) Prostate cancer detection with multi-parametric MRI: Logistic regression analysis of quantitative T2, diffusion-weighted imaging, and dynamic contrast-enhanced MRI. Journal of Magnetic Resonance Imaging, 30, 327-334. doi:10.1002/jmri.21824
[36] Villeirs, G.M., L Verstraete, K., De Neve, W.J. and De Meerleer, G.O. (2005) Magnetic resonance imaging anatomy of the prostate and periprostatic area: A guide for radiotherapists. Radiotherapy and Oncology, 76, 99-106. doi:10.1016/j.radonc.2005.06.015
[37] Zhan, Y. and Shen, D. (2003) Automated segmentation of 3D US prostate images using statistical texture-based matching method. Proceedings of the 6th Annual International Conference of the MICCA, Montreal, 15-18 November 2003, 2878, 688-696.
[38] Lixin, G., Pathak, S.D., Haynor, D.R., Cho, P.S. and Kim, Y. (2004) Parametric shape modeling using deformable superellipses for prostate segmentation. IEEE Transactions on Medical Imaging, 23, 340-349. doi:10.1109/TMI.2004.824237
[39] Zwiggelaar, R., Zhu, Y. and Williams, S. (2003) Semi- automatic segmentation of the prostate. Pattern Recognition and Image Analysis, 2652, 1108-1116. doi:10.1007/978-3-540-44871-6_128
[40] Zhu, Y., Williams, S. and Zwiggelaar, R. (2004) Segmentation of volumetric prostate MRI data using hybrid 2d+3d shape modeling. Medical Image Understanding and Analysis, 61-64.
[41] Toth, R., Tiwari, P., Rosen, M., Kalyanpur, A., Pungavkar, S. and Madabhushi, A. (2008) A multi-modal prostate segmentation scheme by combining spectral clustering and active shape models. Proceedings of the International Society of Optics and Photonic, San Jose, 69144S-69144S.
[42] Klein, S., van der Heide, U.A., Raaymakers, B.W., Kotte, A.N.T.J., Staring, M. and Pluim, J.P.W. (2007) Segmentation of the prostate in MR images by atlas matching. Proceedings of the 4th Annual IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Washington DC, 12-15 April 2007, 1300-1303.
[43] Vikal, S., Haker, S., Tempany, C. and Fichtinger, G. (2009) Prostate contouring in MRI guided biopsy. Proceedings of the International Society of Optics and Photonics, 7259, 72594A.
[44] Martin, S., Troccaz, J. and Daanenc, V. (2010) Auto- mated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model. Journal of Medical Physics, 37, 1579-1590. doi:10.1118/1.3315367
[45] Hambrock, T., Somford, D.M., Hoeks, C., Bouwense, S. A., Huisman, H., Yakar, D., et al. (2010) Magnetic resonance imaging guided prostate biopsy in men with repeat negative biopsies and increased prostate specific antigen. Journal of Urology, 183, 520-527. doi:10.1016/j.juro.2009.10.022
[46] Firjani, A., Khalifa, F., Elnakib, A., Gimel’farb, G., El-Ghar, M.A., Elmaghraby, A. and El-Baz, A. (2011) 3D automatic approach for precise segmentation of the prostate from diffusion-weighted magnetic resonance imaging. Proceedings of 18th Annual IEEE International Conference on Image Processing (ICIP), Brussels, 11-14 September 2011, 2285-2288.
[47] Firjani, A., Khalifa, F., Elnakib, A., Gimel’farb, G., El-Ghar, M.A., Elmaghraby, A. and El-Baz, A. (2012) A novel image-based approach for early detection of prostate cancer. Proceedings of 19th Annual IEEE International Conference on Image Processing (ICIP), Orlando, 30 September-3 October 2012, 2849-2852.
[48] Firjani, A., Elnakib, A., Khalifa, F., Gimel’farb, G., El-Ghar, M.A., Elmaghraby, A. and El-Baz, A. (2011) A new 3D automatic segmentation framework for accurate extraction of prostate from diffusion imaging. Proceedings of 3rd Annual Biomedical Sciences and Engineering Conference (BSEC), Knoxville, 15-17 March 2011, 1-4.
[49] El-Baz, A., Gimel’farb, G., Falk, R., Holland, T. and Shaffer, T. (2008) A new stochastic framework for accurate lung segmentation. Proceedings of the 11th Annual International Conference of the MICCA, New York, 6-10 September 2008, 322-330.
[50] El-Baz, A., Elnakib, A., Khalifa, F., Abou El-Ghar, M., McClure, P., Soliman, A. and Gimel’farb, G. (2012) Precise segmentation of 3-D magnetic resonance angio-graphy. IEEE Transactions on Biomedical Engineering, 7, 2019-2029. doi:10.1109/TBME.2012.2196434
[51] El-Baz, A. and Gimel’farb, G. (2007) EM based approximation of empirical distributions with linear combinations of discrete Gaussians. Proceedings of 14th Annual IEEE International Conference on Image Processing (ICIP), San Antonio, 16-19 September 2007, 4, 373-376.
[52] Farag, A.A., El-Baz, A. and Gimel’farb, G. (2006) Precise segmentation of multi-modal images. IEEE Transactions on Image Processing, 15, 952-968. doi:10.1109/TIP.2005.863949
[53] El-Baz, A. (2006) Novel stochastic models for medical image analysis. Ph.D. Thesis, University of Louisville, Louisville.
[54] Viola, P. and Wells, W.M. (1997) Alignment by maximization of mutual information. International Journal of Computer Vision, 24, 137-154. doi:10.1023/A:1007958904918
[55] Besag, J. (1986) On the statistical-analysis of dirty pictures. Journal of the Royal Statistical Society Series B-Methodological, 48, 259-302.
[56] Khalifa, F., El-Baz, A., Gimel’farb, G. and Abu El-Ghar. M. (2010) Non-invasive image-based approach for early detection of acute renal rejection. Proceedings of the 13th Annual International Conference of the MICCA, Beijing, 20-24 September 2010, 1, 10-18.
[57] Patterson, D.M., Padhani, A.R. and Collins, D.J. (2008) Technology insight: Water diffusion MRI—A potential new biomarker of response to cancer therapy. Nature Clinical Practice Oncology, 5, 220-233. doi:10.1038/ncponc1073
[58] Bouman, C. and Sauer, K. (1993) A generalized Gaussian image model for edge-preserving MAP estimation. IEEE Transactions on Image Processing, 2, 296-310. doi:10.1109/83.236536
[59] Khalifa, F., Beache, G.M., Gimelrfarb, G., Giridharan, G.A. and El-Baz, A. (2012) Accurate automatic analysis of cardiac cine images. IEEE Transactions on Biomedical Engineering, 59, 445-455. doi:10.1109/TBME.2011.2174235
[60] Tsai, A., Yezzi, Jr., A., Wells, W., Tempany, C., Tucker, D., Fan, A., et al. (2003) A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22, 137-154. doi:10.1109/TMI.2002.808355
[61] Dice, L.R. (1945) Measures of the amount of ecologic association between species. Ecology, 26, 297-302. doi:10.2307/1932409
[62] Walker-Samuel, S., Orton, M., Boult, J.K. and Robinson, S.P. (2011) Improving apparent diffusion coefficient estimates and elucidating tumor heterogeneity using Bayesian adaptive smoothing. Magnetic Resonance in Medicine, 65, 438-447. doi:10.1002/mrm.22572

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