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Towards targeted ultrasound-guided prostate biopsy by incorporating model and label uncertainty in cancer detection

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

Abstract

Purpose

Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false negatives. Development of a machine-learning model that can reliably identify suspicious cancer regions is highly desirable. However, the models proposed to-date do not consider the uncertainty present in their output or the data to benefit clinical decision making for targeting biopsy.

Methods

We propose a deep network for improved detection of prostate cancer in systematic biopsy considering both the label and model uncertainty. The architecture of our model is based on U-Net, trained with temporal enhanced ultrasound (TeUS) data. We estimate cancer detection uncertainty using test-time augmentation and test-time dropout. We then use uncertainty metrics to report the cancer probability for regions with high confidence to help the clinical decision making during the biopsy procedure.

Results

Experiments for prostate cancer classification includes data from 183 prostate biopsy cores of 41 patients. We achieve an area under the curve, sensitivity, specificity and balanced accuracy of 0.79, 0.78, 0.71 and 0.75, respectively.

Conclusion

Our key contribution is to automatically estimate model and label uncertainty towards enabling targeted ultrasound-guided prostate biopsy. We anticipate that such information about uncertainty can decrease the number of unnecessary biopsy with a higher rate of cancer yield.

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Author information

Author notes

  1. Golara Javadi and Sharareh Bayat are joint first authors.

  2. Peter Black, Parvin Mousavi and Purang Abolmaesumi are joint senior authors.

Authors and Affiliations

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

    Golara Javadi, Sharareh Bayat, Mohammad Mahdi Kazemi Esfeh, Samareh Samadi, Samira Sojoudi & Purang Abolmaesumi

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

    Alireza Sedghi & Parvin Mousavi

  3. Vancouver General Hospital, Vancouver, BC, Canada

    Antonio Hurtado, Silvia Chang & Peter Black

Authors
  1. Golara Javadi
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  2. Sharareh Bayat
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  3. Mohammad Mahdi Kazemi Esfeh
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  4. Samareh Samadi
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  5. Alireza Sedghi
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  6. Samira Sojoudi
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  8. Silvia Chang
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  9. Peter Black
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  10. Parvin Mousavi
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  11. Purang Abolmaesumi
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Corresponding author

Correspondence to Golara Javadi.

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Conflict of interest

G. Javadi, S. Bayat, S. Samadi, A. Sedghi, S. Sojoudi, A. Hurtado, S. Chang, P. Black, P. Mousavi and P. Abolmaesumi confirm that there are no known conflicts of interest with this publication.

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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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This work is funded in part by the Canadian Institutes of Health Research (CIHR) and in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). This work is also supported by Borealis AI, through the Borealis AI Global Fellowship Award.

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Javadi, G., Bayat, S., Kazemi Esfeh, M.M. et al. Towards targeted ultrasound-guided prostate biopsy by incorporating model and label uncertainty in cancer detection. Int J CARS 17, 121–128 (2022). https://doi.org/10.1007/s11548-021-02485-z

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  • DOI: https://doi.org/10.1007/s11548-021-02485-z

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