Year: 2025
Type: article
Abstract: 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.
Source: Nature Communications
Authors: 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
Institutions: Helmholtz Munich, Technical University of Munich, University of Freiburg, University Medical Center Freiburg, Mayo Clinic, Mayo Clinic in Florida, Munich Leukemia Laboratory (Germany), Radboud University Nijmegen, Radboud University Medical Center, Oncode Institute, Erasmus MC, University Hospital Münster, Heinrich Heine University Düsseldorf
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
OpenAlex ID: W4410734748