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Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

This paper presents the results of a large study involving fusion prostate biopsies to demonstrate that temporal ultrasound can be used to accurately classify tissue labels identified in multi-parametric magnetic resonance imaging (mp-MRI) as suspicious for cancer.

Methods

We use deep learning to analyze temporal ultrasound data obtained from 255 cancer foci identified in mp-MRI. Each target is sampled in axial and sagittal planes. A deep belief network is trained to automatically learn the high-level latent features of temporal ultrasound data. A support vector machine classifier is then applied to differentiate cancerous versus benign tissue, verified by histopathology. Data from 32 targets are used for the training, while the remaining 223 targets are used for testing.

Results

Our results indicate that the distance between the biopsy target and the prostate boundary, and the agreement between axial and sagittal histopathology of each target impact the classification accuracy. In 84 test cores that are 5 mm or farther to the prostate boundary, and have consistent pathology outcomes in axial and sagittal biopsy planes, we achieve an area under the curve of 0.80. In contrast, all of these targets were labeled as moderately suspicious in mp-MR.

Conclusion

Using temporal ultrasound data in a fusion prostate biopsy study, we achieved a high classification accuracy specifically for moderately scored mp-MRI targets. These targets are clinically common and contribute to the high false-positive rates associated with mp-MRI for prostate cancer detection. Temporal ultrasound data combined with mp-MRI have the potential to reduce the number of unnecessary biopsies in fusion biopsy settings.

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Notes

  1. Canadian cancer society: http://www.cancer.ca/, and American cancer society: http://www.cancer.org/.

References

  1. Azizi S, Imani F, Zhuang B, Tahmasebi A, Kwak JT, Xu S, Uniyal N, Turkbey B, Choyke P, Pinto P, Wood B (2015) Ultrasound-based detection of prostate cancer using automatic feature selection with deep belief networks. Med Image Comput Comput-Assist Interv—MICCAI 2015, pp 70–77. Springer (2015)

  2. Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153

    Google Scholar 

  3. Correas JM, Tissier AM, Khairoune A, Khoury G, Eiss D, Hélénon O (2013) Ultrasound elastography of the prostate: state of the art. Diagn Interv Imaging 94(5):551–560

    Article  PubMed  Google Scholar 

  4. Daoud MI, Mousavi P, Imani F, Rohling R, Abolmaesumi P (2013) Tissue classification using ultrasound-induced variations in acoustic backscattering features. IEEE Trans Biomed Eng 60(2):310–320

    Article  PubMed  Google Scholar 

  5. Epstein JI, Feng Z, Trock BJ, Pierorazio PM (2012) Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades. Eur Urol 61(5):1019–1024

    Article  PubMed  PubMed Central  Google Scholar 

  6. Erhan D, Bengio Y, Courville A, Manzagol PA, Vincent P, Bengio S (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660

    Google Scholar 

  7. Fawcett T (2006) An introduction to roc analysis. Pattern Recognit Lett 27(8):861–874

    Article  Google Scholar 

  8. Feleppa, E., Porter, C., Ketterling, J., Dasgupta, S., Ramachandran, S., Sparks, D.: Recent advances in ultrasonic tissue-type imaging of the prostate. In: Acoustical imaging, pp 331–339. Springer (2007)

  9. Goossen T, Wijkstra H (2003) Transrectal ultrasound imaging and prostate cancer. Archivio Italiano di Urologia Andrologia 75(1):68–74

    Google Scholar 

  10. Hinton G (2010) A practical guide to training restricted Boltzmann machines. Momentum 9(1):926

    Google Scholar 

  11. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  PubMed  Google Scholar 

  12. Imani F, Abolmaesumi P, Gibson E, Khojaste A, Gaed M, Moussa M, Gomez JA, Romagnoli C, Siemens DR, Leviridge M, Chang S, Fenster A, Ward AD, Mousavi P (2013) Ultrasound-based characterization of prostate cancer: an in vivo clinical feasibility study. Med Image Comput Comput-Assist Interv–MICCAI 2013, pp 279–286, Springer

  13. Imani F, Abolmaesumi P, Gibson E, Khojaste A, Gaed M, Moussa M, Siemens DR, Fenster A, Ward A, Mousavi P (2015) Computer-aided prostate cancer detection using ultrasound RF time series: in vivo feasibility study. IEEE Trans Med Imaging 34(11):2248–2257

    Article  PubMed  Google Scholar 

  14. Imani F, Ramezani M, Nouranian S, Gibson E, Khojaste A, Gaed M, Moussa M, Gomez J, Romagnoli C, Leveridge M, Chang S (2015) Ultrasound-based characterization of prostate cancer using joint independent component analysis. IEEE Trans Biomed Eng 62(7):1796–1804

    Article  PubMed  Google Scholar 

  15. Imani, F., Zhuang, B., Tahmasebi, A., Kwak, J.T., Xu S, Agarwal H, Bharat S, Uniyal N, Turkbey I, Choyke P, Pinto P (2015) Augmenting MRI-transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study. Int J Comput Assist Radiol Surg, pp 1–9 (2015)

  16. Kuru TH, Roethke MC, Seidenader J, Simpfendörfer T, Boxler S, Alammar K, Rieker P, Popeneciu V, Roth W, Pahernik S, Schlemmer H (2013) Critical evaluation of magnetic resonance imaging targeted, transrectal ultrasound guided transperineal fusion biopsy for detection of prostate cancer. J Urol 190(4):1380–1386

    Article  PubMed  Google Scholar 

  17. Marks L, Young S, Natarajan S (2013) MRI-ultrasound fusion for guidance of targeted prostate biopsy. Curr Opin Urol 23(1):43

    Article  PubMed  PubMed Central  Google Scholar 

  18. Miyagawa T, Tsutsumi M, Matsumura T, Kawazoe N, Ishikawa S, Shimokama T, Miyanaga N, Akaza H (2009) Real-time elastography for the diagnosis of prostate cancer: evaluation of elastographic moving images. Jpn J Clin Oncol 39(6):394–398

    Article  PubMed  Google Scholar 

  19. Moradi M, Abolmaesumi P, Mousavi P (2010) Tissue typing using ultrasound RF time series: experiments with animal tissue samples. Med Phys 37(8):4401–4413

    Article  PubMed  Google Scholar 

  20. Moradi, M., Mahdavi, S.S., Nir, G., Jones, E.C., Goldenberg, S.L., Salcudean, S.E.: Ultrasound RF time series for tissue typing: first in vivo clinical results. In: SPIE Medical Imaging, pp. 86,701I–86,701I. International Society for Optics and Photonics (2013)

  21. Moradi M, Mahdavi SS, Nir G, Mohareri O, Koupparis A, Gagnon L, Fazli L, Casey R, Ischia J, Jones E, Goldenberg S (2014) Multiparametric 3D in vivo ultrasound vibroelastography imaging of prostate cancer: Preliminary results. Med Phys 41(7)

  22. Moradi M, Mousavi P, Boag A, Sauerbrei EE, Siemens D, Abolmaesumi P (2009) Augmenting detection of prostate cancer in transrectal ultrasound images using SVM and RF time series. IEEE Trans Biomed Eng 56(9):2214–2224

    Article  PubMed  Google Scholar 

  23. Nelson ED, Slotoroff CB, Gomella LG, Halpern EJ (2007) Targeted biopsy of the prostate: The impact of color Doppler imaging and elastography on prostate cancer detection and Gleason score. Urology 70(6):1136–1140

    Article  PubMed  Google Scholar 

  24. Park S, Aglyamov SR, Emelianov SY (2007) Elasticity imaging using conventional and high-frame rate ultrasound imaging: Experimental study. IEEE Trans Ultrason Ferroelectr Freq Control 54(11):2246–2256

    Article  PubMed  Google Scholar 

  25. Rapiti E, Schaffar R, Iselin C, Miralbell R, Pelte MF, Weber D, Zanetti R, Neyroud-Caspar I, Bouchardy C (2013) Importance and determinants of Gleason score undergrading on biopsy sample of prostate cancer in a population-based study. BMC Urol 13(1):19

    Article  PubMed  PubMed Central  Google Scholar 

  26. de Rooij M, Hamoen EH, Fütterer JJ, Barentsz JO, Rovers MM (2014) Accuracy of multiparametric MRI for prostate cancer detection: a meta-analysis. Am J Roentgenol 202(2):343–351

    Article  Google Scholar 

  27. Schmitz G, Ermert H, Senge T (1999) Tissue-characterization of the prostate using RF ultrasonic signals. IEEE Trans Ultrason Ferroelectr Freq Control 46(1):126–138

    Article  CAS  PubMed  Google Scholar 

  28. Tanaka M, Okutomi M (2014) A novel inference of a restricted Boltzmann machine. In: International conference on pattern recognition (ICPR), 2014 22nd, pp 1526–1531. IEEE (2014)

  29. Turkbey B, Mani H, Aras O, Ho J, Hoang A, Rastinehad A, Agarwal H, Shah V, Bernardo M, Pang Y, Daar D (2013) Prostate cancer: Can mp-MR imaging help identify patients who are candidates for active surveillance? Radiology 268(1):144–152

  30. Xie SW, Li HL, Du J, Xia JG, Guo YF, Xin M, Li FH (2013) Influence of serum PSA level, prostate volume, and PSA density on prostate cancer detection with contrast-enhanced sonography using contrast-tuned imaging technology. J Ultrasound Med 32(5):741–748

    Article  PubMed  Google Scholar 

  31. Xu S, Kruecker J, Turkbey B, Glossop N, Singh AK, Choyke P, Pinto P, Wood B (2008) Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies. Comput Aided Surg 13(5):255–264

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yosinski J, Lipson H (2012) Visually debugging restricted Boltzmann machine training with a 3D example. In: 29th international conference on machine learning, representation learning workshop 2012

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Acknowledgments

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Institutes of Health Research (CIHR).

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Authors and Affiliations

  1. University of British Columbia, Vancouver, British Columbia, Canada

    Shekoofeh Azizi, Farhad Imani & Purang Abolmaesumi

  2. Queen’s University, Kingston, Ontario, Canada

    Sahar Ghavidel & Parvin Mousavi

  3. Philips Research North America, Cambridge, Massachusetts, USA

    Amir Tahmasebi

  4. National Institutes of Health, Bethesda, Maryland, USA

    Jin Tae Kwak, Sheng Xu, Baris Turkbey, Peter Choyke, Peter Pinto & Bradford Wood

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  1. Shekoofeh Azizi
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  2. Farhad Imani
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Correspondence to Shekoofeh Azizi.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Azizi, S., Imani, F., Ghavidel, S. et al. Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study. Int J CARS 11, 947–956 (2016). https://doi.org/10.1007/s11548-016-1395-2

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  • DOI: https://doi.org/10.1007/s11548-016-1395-2

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