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Coarse label refinement for improving prostate cancer detection in ultrasound imaging

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

Abstract

Purpose:

Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by a high rate of false negatives and low diagnostic yield of the current systematic, non-targeted approaches. Developing machine learning models for accurately identifying cancerous tissue in ultrasound would help sample tissues from regions with higher cancer likelihood. A plausible approach for this purpose is to use individual ultrasound signals corresponding to a core as inputs and consider the histopathology diagnosis for the entire core as labels. However, this introduces significant amount of label noise to training and degrades the classification performance. Previously, we suggested that histopathology-reported cancer involvement can be a reasonable approximation for the label noise.

Methods:

Here, we propose an involvement-based label refinement (iLR) method to correct corrupted labels and improve cancer classification. The difference between predicted and true cancer involvements is used to guide the label refinement process. We further incorporate iLR into state-of-the-art methods for learning with noisy labels and predicting cancer involvement.

Results:

We use 258 biopsy cores from 70 patients and demonstrate that our proposed label refinement method improves the performance of multiple noise-tolerant approaches and achieves a balanced accuracy, correlation coefficient, and mean absolute error of 76.7%, 0.68, and 12.4, respectively.

Conclusions:

Our key contribution is to leverage a data-centric method to deal with noisy labels using histopathology reports, and improve the performance of prostate cancer diagnosis through a hierarchical training process with label refinement.

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Notes

  1. The Iverson bracket [P] returns 1 if P is true; 0 otherwise.

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Acknowledgements

We thank NSERC and CIHR for supporting our research.

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Author notes

  1. Minh Nguyen Nhat To and Fahimeh Fooladgar have contributed equally to this work.

Authors and Affiliations

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

    Minh Nguyen Nhat To, Fahimeh Fooladgar, Golara Javadi, Sharareh Bayat, Samira Sojoudi, Antonio Hurtado, Silvia Chang, Peter Black & Purang Abolmaesumi

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

    Parvin Mousavi

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  1. Minh Nguyen Nhat To
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Conflict of interest:

All authors 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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Informed consent was obtained from all individual participants included in the study.

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To, M.N.N., Fooladgar, F., Javadi, G. et al. Coarse label refinement for improving prostate cancer detection in ultrasound imaging. Int J CARS 17, 841–847 (2022). https://doi.org/10.1007/s11548-022-02606-2

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  • DOI: https://doi.org/10.1007/s11548-022-02606-2

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