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Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients

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Abstract

Purpose

Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC.

Methods

Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined.

Results

In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR).

Conclusion

Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Code generated during the current study is available from the corresponding author on reasonable request.

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Acknowledgments

The authors wish to thank the Breast Site Group at Sunnybrook Health Sciences Centre for their continued support and intellectual discussions.

Funding

Dr. Lu, Dr. Sadeghi-Naini, and Dr. Tran received funding from the tri-council (Government of Canada) New Frontiers in Research Fund. Dr. Tran lab is funded in part, by the Terry Fox Research Institute and the Women’s Health Golf Classic Foundation Fund. Dr. Sadeghi-Naini holds a York Research Chair in Quantitative Imaging and Smart Biomarkers, and received funding from the Natural Sciences and Engineering Research Council of Canada and the Terry Fox Research Institute.

Author information

Authors and Affiliations

  1. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada

    David W. Dodington & Fang-I Lu

  2. Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada

    Andrew Lagree, Majid Mohebpour & William T. Tran

  3. Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

    Sami Tabbarah, Ali Sadeghi-Naini & William T. Tran

  4. Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada

    Ali Sadeghi-Naini

  5. Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada

    William T. Tran

  6. Department of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Rm E423a, Toronto, ON, M4N 3M5, Canada

    Fang-I Lu

  7. Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada

    William T. Tran

Authors
  1. David W. Dodington
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  2. Andrew Lagree
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  3. Sami Tabbarah
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  4. Majid Mohebpour
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  5. Ali Sadeghi-Naini
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  6. William T. Tran
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  7. Fang-I Lu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by DWD, AL, and ST. The first draft of the manuscript was written by DWD and AL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fang-I Lu.

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This study was approved by the institutional research ethics board.

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Waived as per the institutional research ethics board.

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Dodington, D.W., Lagree, A., Tabbarah, S. et al. Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Res Treat 186, 379–389 (2021). https://doi.org/10.1007/s10549-020-06093-4

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  • DOI: https://doi.org/10.1007/s10549-020-06093-4

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