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Association of metastatic pattern in breast cancer with tumor and patient-specific factors: a nationwide autopsy study using artificial intelligence

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Abstract

Background

Metastatic spread is characterized by considerable heterogeneity in most cancers. With increasing treatment options for patients with metastatic disease, there is a need for insight into metastatic patterns of spread in breast cancer patients using large-scale studies.

Methods

Records of 2622 metastatic breast cancer patients who underwent autopsy (1974–2010) were retrieved from the nationwide Dutch pathology databank (PALGA). Natural language processing (NLP) and manual information extraction (IE) were applied to identify the tumors, patient characteristics, and locations of metastases.

Results

The accuracy (0.90) and recall (0.94) of the NLP model outperformed manual IE (on 132 randomly selected patients). Adenocarcinoma no special type more frequently metastasizes to the lung (55.7%) and liver (51.8%), whereas, invasive lobular carcinoma mostly spread to the bone (54.4%) and liver (43.8%), respectively. Patients with tumor grade III had a higher chance of developing bone metastases (61.6%). In a subgroup of patients, we found that ER+/HER2+ patients were more likely to metastasize to the liver and bone, compared to ER−/HER2+ patients.

Conclusion

This is the first large-scale study that demonstrates that artificial intelligence methods are efficient for IE from Dutch databanks. Different histological subtypes show different frequencies and combinations of metastatic sites which may reflect the underlying biology of metastatic breast cancer.

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Data availability

The data underlying this article were provided by PALGA and NCR by permission. Data will be shared on reasonable request to the corresponding author with permission from PALGA and NCR.

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Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Pathology 824, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands

    Fatemeh Kazemzadeh, J. A. A. Snoek & Iris D. Nagtegaal

  2. Department of Medical Oncology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands

    Fatemeh Kazemzadeh & Martijn G. H. van Oijen

  3. Therapy Program, Cancer Center Amsterdam, Amsterdam, The Netherlands

    Fatemeh Kazemzadeh & Martijn G. H. van Oijen

  4. Department of Pathology, Albert Schweitzer Hospital, Dordrecht, The Netherlands

    J. A. A. Snoek

  5. PALGA Foundation, Houten, The Netherlands

    Quirinus J. Voorham & Iris D. Nagtegaal

  6. Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands

    Niek Hugen

  7. Department of Surgery, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands

    Niek Hugen

Authors
  1. Fatemeh Kazemzadeh
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  2. J. A. A. Snoek
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  3. Quirinus J. Voorham
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  4. Martijn G. H. van Oijen
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  5. Niek Hugen
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  6. Iris D. Nagtegaal
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Contributions

Conceptualization, MO, NH and IN; Data curation, FK, AS, QV, MO and IN; Funding acquisition, MO and IN; Investigation, FK, AS, MO, NH and IN; Methodology, FK, AS, MO and IN; Project administration, FK, MO and IN; Resources, AS, QV and IN; Supervision, MO and IN; Visualization, FK and IN; Writing—original draft, FK; Writing—review and editing, FK, AS, QV, MO, NH, and IN.

Corresponding authors

Correspondence to Fatemeh Kazemzadeh or Iris D. Nagtegaal.

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Kazemzadeh, F., Snoek, J.A.A., Voorham, Q.J. et al. Association of metastatic pattern in breast cancer with tumor and patient-specific factors: a nationwide autopsy study using artificial intelligence. Breast Cancer 31, 263–271 (2024). https://doi.org/10.1007/s12282-023-01534-6

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  • DOI: https://doi.org/10.1007/s12282-023-01534-6

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