Abstract
In this article, we describe a system for detecting dominant prostate tumors, based on a combination of features extracted from a novel multi-parametric quantitative ultrasound elastography technique. The performance of the system was validated on a data-set acquired from n = 10 patients undergoing radical prostatectomy. Multi-frequency steady-state mechanical excitations were applied to each patient’s prostate through the perineum and prostate tissue displacements were captured by a transrectal ultrasound system. 3D volumetric data including absolute value of tissue elasticity, strain and frequency-response were computed for each patient. Based on the combination of all extracted features, a random forest classification algorithm was used to separate cancerous regions from normal tissue, and to compute a measure of cancer probability. Registered whole mount histopathology images of the excised prostate gland were used as a ground truth of cancer distribution for classifier training. An area under receiver operating characteristic curve of 0.82±0.01 was achieved in a leave-one-patient-out cross validation. Our results show the potential of multi-parametric quantitative elastography for prostate cancer detection for the first time in a clinical setting, and justify further studies to establish whether the approach can have clinical use.
Chapter PDF
Similar content being viewed by others
Keywords
- Radical Prostatectomy
- Random Forest
- Peripheral Zone
- Acoustic Radiation Force Impulse
- Shear Wave Elastography
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Salcudean, S.E., Sahebjavaher, R.S., et al.: Biomechanical modeling of the prostate for procedure guidance and simulation. In: Soft Tissue Biomechanical Modeling for Computer Assisted Surgery, vol. 11, pp. 169–198. Springer (2012)
Brock, M., Von Bodman, C., et al.: The impact of real-time elastography guiding a systematic prostate biopsy to improve cancer detection rate: A prospective study of 353 patients. J. Urol. 187(6), 2039–2043 (2012)
Zhang, M., Nigwekar, P., et al.: Quantitative characterization of viscoelastic properties of human prostate correlated with histology. Ultrasound Med. Biol. 34(7), 1033–1042 (2008)
Zhai, L., Madden, J., et al.: Acoustic radiation force impulse imaging of human prostates ex vivo. Ultrasound Med. Biol. 36(4), 576–588 (2010)
Ahmad, S., Cao, R., et al.: Transrectal quantitative shear wave elastography in the detection and characterisation of prostate cancer. Surg. Endosc. 27(9), 3280–3287 (2013)
Brock, M., Eggert, T., et al.: Multiparametric ultrasound of the prostate: adding contrast enhanced ultrasound to real-time elastography to detect histopathologically confirmed cancer. J. Urol. 189(1), 93–98 (2013)
Turgay, E., Salcudean, S., et al.: Identifying mechanical properties of tissue by ultrasound. Ultrasound Med. Biol. 32(2), 221–235 (2008)
Zahiri-Azar, R., Salcudean, S.E.: Motion estimation in ultrasound images using time domain cross correlation with prior estimates. IEEE Trans. Biomed. Eng. 53(10), 1990–(2000)
Muthupillai, R., Lomas, D.J.: Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science 269(5232), 1854–1857 (1995)
Adebar, T., Salcudean, S., Mahdavi, S., Moradi, M., Nguan, C., Goldenberg, L.: A robotic system for intra-operative trans-rectal ultrasound and ultrasound elastography in radical prostatectomy. In: Taylor, R.H., Yang, G.-Z. (eds.) IPCAI 2011. LNCS, vol. 6689, pp. 79–89. Springer, Heidelberg (2011)
Sahebjavaher, R.S., Baghani, A., et al.: Transperineal prostate mr elastography: Initial in vivo results. Magn. Reson. Med. 69(2), 411–420 (2013)
Eskandari, H., Goksel, O., et al.: Bandpass sampling of high-frequency tissue motion. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 58(7), 1332–1343 (2011)
Nir, G., Salcudean, S.E.: Registration of whole-mount histology and tomography of the prostate using particle filtering. In: Proc. SPIE, vol. 8676, pp. 86760E–86760E–9 (2013)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
McNeal, J.E., Redwine, A.E., et al.: Zonal distribution of prostatic adenocarcinoma. correlation with histologic pattern and direction of spread. Am. J. Surg. Pathol. 12(12), 897–906 (1988)
Salomon, G., Kollerman, J., et al.: Evaluation of prostate cancer detection with ultrasound real-time elastography: A comparison with step section pathological analysis after radical prostatectomy. Eur. Urol. 54(6), 1354–1362 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mohareri, O. et al. (2014). Multi-parametric 3D Quantitative Ultrasound Vibro-Elastography Imaging for Detecting Palpable Prostate Tumors. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_70
Download citation
DOI: https://doi.org/10.1007/978-3-319-10404-1_70
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10403-4
Online ISBN: 978-3-319-10404-1
eBook Packages: Computer ScienceComputer Science (R0)