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From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery

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

Abstract

Purpose

Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.

Methods

Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.

Results

The best-performing model using both quantitative evaluation and our visual ranking framework achieved a mean Dice score of 0.959. Quantitative metrics are positively associated with expert visual rankings. However, the predictive value of generated contours was limited with a sensitivity of 0.750 and a specificity of 0.433 when tested against pathology reports.

Conclusion

We present a clinical evaluation of deep learning models trained for intraoperative tumor segmentation in breast-conserving surgery. We demonstrate that automatic contouring is limited in predicting pathology margins despite achieving high performance on quantitative metrics.

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Notes

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Acknowledgements

This work was supported 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. School of Computing, Queen’s University, Kingston, ON, Canada

    Chris Yeung, Tamas Ungi, Amoon Jamzad, Parvin Mousavi & Gabor Fichtinger

  2. School of Medicine, Queen’s University, Kingston, ON, Canada

    Zoe Hu

  3. Department of Surgery, Queen’s University, Kingston, ON, Canada

    Martin Kaufmann, Ross Walker, Shaila Merchant, Cecil Jay Engel & John Rudan

  4. Department of Radiology, Queen’s University, Kingston, ON, Canada

    Doris Jabs

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  1. Chris Yeung
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Correspondence to Chris Yeung.

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All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Yeung, C., Ungi, T., Hu, Z. et al. From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery. Int J CARS 19, 1193–1201 (2024). https://doi.org/10.1007/s11548-024-03133-y

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  • DOI: https://doi.org/10.1007/s11548-024-03133-y

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