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<p>Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consuming, labor-intensive, and non-standardized. To address this problem, we present HistoGPT, a vision language model that generates pathology reports from a patient’s multiple full-resolution histology images. It is trained on 15,129 whole slide images from 6705 dermatology patients with corresponding pathology reports. The generated reports match the quality of human-written reports for common and homogeneous malignancies, as confirmed by natural language processing metrics and domain expert analysis. We evaluate HistoGPT in an international, multi-center clinical study and show that it can accurately predict tumor subtypes, tumor thickness, and tumor margins in a zero-shot fashion. Our model demonstrates the potential of artificial intelligence to assist pathologists in evaluating, reporting, and understanding routine dermatopathology cases.</p>

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Generating dermatopathology reports from gigapixel whole slide images with HistoGPT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月27日

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  • Manuel Tran,
  • Paul Schmidle,
  • Ruifeng Ray Guo,
  • Sophia J. Wagner,
  • Valentin Koch,
  • Valerio Lupperger,
  • Brenna Novotny,
  • Dennis H. Murphree,
  • Heather D. Hardway,
  • Marina D’Amato,
  • Judith Lefkes,
  • Daan J. Geijs,
  • Annette Feuchtinger,
  • Alexander Böhner,
  • Robert Kaczmarczyk,
  • Tilo Biedermann,
  • Avital L. Amir,
  • Antien L. Mooyaart,
  • Francesco Ciompi,
  • Geert Litjens,
  • Chen Wang,
  • Nneka I. Comfere,
  • Kilian Eyerich,
  • Stephan A. Braun,
  • Carsten Marr,
  • Tingying Peng

摘要

Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consuming, labor-intensive, and non-standardized. To address this problem, we present HistoGPT, a vision language model that generates pathology reports from a patient’s multiple full-resolution histology images. It is trained on 15,129 whole slide images from 6705 dermatology patients with corresponding pathology reports. The generated reports match the quality of human-written reports for common and homogeneous malignancies, as confirmed by natural language processing metrics and domain expert analysis. We evaluate HistoGPT in an international, multi-center clinical study and show that it can accurately predict tumor subtypes, tumor thickness, and tumor margins in a zero-shot fashion. Our model demonstrates the potential of artificial intelligence to assist pathologists in evaluating, reporting, and understanding routine dermatopathology cases.