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Updated: Jun 27, 2026

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Retrospective Cohort Study: Extracting Coexisting Background Breast-Lesion Features from Stage I-III Invasive Breast

Ryan Jak Yang Lim1, Phyu Nitar2, Kah Weng Lau3

  • 1Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore.

Cancers
|June 26, 2026
PubMed

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Summary
This summary is machine-generated.

Background breast features identified by natural language processing (NLP) reflect tumor biology but do not independently predict patient outcomes in invasive breast cancer. This study demonstrates NLP

Area of Science:

  • Oncology
  • Pathology
  • Bioinformatics

Background:

  • Background breast features are often documented in pathology reports but their prognostic significance is poorly understood.
  • Existing research has not fully characterized the relationship between these features, tumor characteristics, and patient outcomes.

Purpose of the Study:

  • To investigate the association between background breast features and tumor characteristics in invasive breast cancer.
  • To evaluate the prognostic value of background breast features for patient survival.
  • To assess the feasibility of using natural language processing (NLP) for large-scale extraction of these features from pathology reports.

Main Methods:

  • Retrospective cohort study of 7603 patients with Stage I-III invasive breast cancer.
Keywords:
benign breast diseasesdata extractionfree-texthistological featuresnatural language processing (NLP)pathologystructured variables

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  • Natural language processing (NLP) applied to over 9754 free-text pathology reports to extract background breast features.
  • Unsupervised hierarchical clustering to categorize extracted features; multinomial logistic regression and Cox proportional hazards models used for analysis.

    • Main Results:

    • NLP achieved over 90% accuracy in extracting background breast features.
    • Specific features like lobular neoplasia and benign proliferative changes correlated with less aggressive tumor characteristics.
    • Early neoplastic and papillary lesions were more common in HER2-enriched and luminal B subtypes; benign proliferative changes showed a trend towards better survival, attenuated by stage and subtype.

    Conclusions:

    • NLP-enabled extraction of background breast features from pathology reports is feasible and scalable.
    • Extracted background features correlate with underlying tumor biology and subtypes.
    • These features do not provide independent prognostic information beyond established clinical variables like stage and subtype.