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Updated: Jul 2, 2026

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
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Mapping the Advanced-Stage Epithelial Ovarian Cancer Landscape Goes Beyond Words: Two Large Language Models, Eight

Michela Quaranta1, Alexandros Laios1, Charlie Rogers1

  • 1Department of Gynaecologic Oncology, ESGO Centre of Excellence for Ovarian Cancer Surgery, St James's University Hospital, Leeds LS9 7TF, UK.

Journal of Clinical Medicine
|April 12, 2025
PubMed
Summary

Domain-specific language models like GatorTron show promise in predicting outcomes from surgical notes for advanced-stage epithelial ovarian cancer (aEOC) cytoreduction. However, predicting post-operative events remains challenging using text alone.

Keywords:
GatorTronRoBERTaepithelial ovarian cancernatural language processingoperative notestransfer learning

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Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning in Healthcare
  • Oncology

Background:

  • NLP advancements offer potential for healthcare, but analysis of clinical notes is underdeveloped.
  • Transformer-based deep neural networks are being explored for predicting surgical outcomes.
  • Advanced-stage epithelial ovarian cancer (aEOC) cytoreduction presents complex data analysis challenges.

Purpose of the Study:

  • To investigate the efficacy of deep learning models, specifically transformer-based NLP, for predicting intraoperative and post-operative outcomes in aEOC.
  • To compare a general-purpose model (RoBERTa) with a domain-specific model (GatorTron) for analyzing unstructured surgical notes.
  • To assess the predictive capabilities of NLP models using clinical text for aEOC cytoreductive surgery outcomes.

Main Methods:

  • Evaluated RoBERTa and GatorTron models on eight binary classification tasks using 560 surgical records from aEOC patients.
  • Concatenated "operative findings" and "operative notes" to enhance contextual information for models.
  • Converted predictive outcomes into binary features for classification.

Main Results:

  • Domain-specific models (GatorTron) generally outperformed general models (RoBERTa) in predicting outcomes from clinical text.
  • Both models faced difficulties predicting post-operative events like major complications and hospital stay length.
  • The limitations suggest that surgical notes alone may not fully capture post-operative recovery complexities.

Conclusions:

  • Employing domain-specific language models offers tangible benefits for clinical text analysis in oncology.
  • AI systems utilizing NLP can potentially enhance the delivery of modern healthcare for aEOC.
  • Further research is needed to incorporate additional data sources for predicting complex post-operative outcomes.