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<p>Despite recent advances in medical informatics, extracting tumor information from pathology reports remains a challenge in modern cancer registry and surveillance workflows. These documents often have an unstructured format, complex medical content, and a considerably lengthy context, creating significant challenges for automated phenotypic information extraction. Although some recent language models such as BERT, GatorTron, and GPT-4 have demonstrated efficacy in medical applications, they are either constrained by sequence length limitations or cloud-based computing that violates the handling of protected health information. We introduce two oncology pathology-optimized transformer models <b>OncoPT</b>, based on Longformer and BigBird architectures and trained on real-world pathology reports. OncoPT efficiently processes reports up to 4,096 tokens, making it suitable for hospitals’ onsite deployment with limited resources. We apply <b>OncoPT</b> to a common malignancy (exemplified by breast cancer) and a rare malignancy (exemplified by gastric cancer), across five key tumor phenotypes: Subsite, Histology, Grade, Stage, and Laterality. The results demonstrate that <b>OncoPT</b> achieves state-of-the-art weighted F-1 on a private pathology dataset and surpasses commercial chatbots (ChatGPT 4o and o1) on the public CORAL dataset (up to 30% improvement). These findings highlight the robustness of <b>OncoPT</b> models with the added benefit of preserving the privacy of patient health information.</p>

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OncoPT: long-context transformer models for in hospital tumor phenotype extraction from pathology reports

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Thanh Duong,
  • Dung Le,
  • Vonetta Williams,
  • Sandra Stewart,
  • Yayi Zhao,
  • Muntasir Zitu,
  • Issam El Naqa,
  • Dana Rollison,
  • Thanh Thieu

摘要

Despite recent advances in medical informatics, extracting tumor information from pathology reports remains a challenge in modern cancer registry and surveillance workflows. These documents often have an unstructured format, complex medical content, and a considerably lengthy context, creating significant challenges for automated phenotypic information extraction. Although some recent language models such as BERT, GatorTron, and GPT-4 have demonstrated efficacy in medical applications, they are either constrained by sequence length limitations or cloud-based computing that violates the handling of protected health information. We introduce two oncology pathology-optimized transformer models OncoPT, based on Longformer and BigBird architectures and trained on real-world pathology reports. OncoPT efficiently processes reports up to 4,096 tokens, making it suitable for hospitals’ onsite deployment with limited resources. We apply OncoPT to a common malignancy (exemplified by breast cancer) and a rare malignancy (exemplified by gastric cancer), across five key tumor phenotypes: Subsite, Histology, Grade, Stage, and Laterality. The results demonstrate that OncoPT achieves state-of-the-art weighted F-1 on a private pathology dataset and surpasses commercial chatbots (ChatGPT 4o and o1) on the public CORAL dataset (up to 30% improvement). These findings highlight the robustness of OncoPT models with the added benefit of preserving the privacy of patient health information.