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Related Experiment Video

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Benchmark Pathology Report Text Corpus with Cancer Type Classification.

Jenna Kefeli1, Nicholas Tatonetti2

  • 1Department of Systems Biology, Columbia University, New York, New York, 10032, United States.

Medrxiv : the Preprint Server for Health Sciences
|August 23, 2023
PubMed
Summary

This study introduces a new, machine-readable dataset of 9,523 cancer pathology reports. This resource enables advanced AI-driven analysis for improved cancer research and clinical applications.

Keywords:
BERTTCGAcancer pathologycancer typeclassificationlarge language modelsmachine learningpathology reportsresourcetransformer model

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Pathology

Background:

  • Pathology report text is an underutilized data source in cancer research, offering nuanced insights beyond structured data.
  • Existing publicly available datasets for benchmarking pathology report-based models are lacking.
  • Advances in optical character recognition (OCR) and artificial intelligence (AI) natural language processing (NLP) necessitate a benchmark dataset.

Approach:

  • Utilized state-of-the-art OCR and customized post-processing to convert 9,523 publicly available pathology report PDFs from The Cancer Genome Atlas into a machine-readable corpus.
  • Developed and applied NLP models for proof-of-principle cancer-type classification across 32 distinct tissue types.

Key Points:

  • Generated a novel, large-scale, machine-readable corpus of 9,523 cancer pathology reports.
  • Achieved a high performance of 0.992 average AU-ROC in a proof-of-principle cancer-type classification task.
  • The dataset is suitable for benchmarking AI and NLP models in cancer research.

Conclusions:

  • This dataset addresses the need for a benchmark resource for pathology report analysis.
  • Facilitates advancements in AI-driven prediction of clinical targets from pathology reports.
  • Supports researchers across disciplines, including clinical NLP, clinical trials, and cancer research.