ORNLの研究者ら、ほぼリアルタイムのがん監視のためのAI駆動ツールを開発(Researchers from ORNL develop AI-driven tool for near real-time cancer surveillance)


2024-03-13 オークリッジ国立研究所(ORNL)

オークリッジ国立研究所とルイジアナ州立大学の研究者は、ナショナルキャンサー研究所(NCI)との提携で、癌治療や診断の向上に貢献するAIトランスフォーマーを開発しました。このモデルは、数百万の病理報告書を処理し、癌の診断に関する専門家により正確な情報を提供します。この成果は「Clinical Cancer Informatics」に掲載され、ナショナルキャンサー研究所のSEERプログラムのデータを活用しています。


Path-BigBird: がん病理報告書の分類に対するAI駆動トランスフォーマーアプローチ Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

Mayanka Chandrashekar, PhD; Isaac Lyngaas, PhD; Heidi A. Hanson, PhD, Shang Gao, PhD; Xiao-Cheng Wu, MD, MPH; and John Gounley, PhD
Clinical Cancer Informatics  Published:February 27, 2024


Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports.

We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an off-the-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F1 scores.

We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F1 score of 72.53, macro F1 score of 35.76) and histology (micro F1 score of 80.96, macro F1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F1 score by 1.44 points and the macro F1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from well-curated and deidentified data is the best-performing model.

The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.