Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Alessio Gerussi - , Fondazione IRCCS San Gerardo dei Tintori, University of Milan - Bicocca (Author)
  • Oliver Lester Saldanha - , Else Kröner Fresenius Center for Digital Health, University Hospital Aachen (Author)
  • Giorgio Cazzaniga - , University of Milan - Bicocca (Author)
  • Damiano Verda - , Rulex Inc. (Author)
  • Zunamys I. Carrero - , Else Kröner Fresenius Center for Digital Health (Author)
  • Bastian Engel - , Hannover Medical School (MHH), European Reference Network on Rare Hepatological Diseases (Author)
  • Richard Taubert - , Hannover Medical School (MHH), European Reference Network on Rare Hepatological Diseases (Author)
  • Francesca Bolis - , Fondazione IRCCS San Gerardo dei Tintori, University of Milan - Bicocca (Author)
  • Laura Cristoferi - , Fondazione IRCCS San Gerardo dei Tintori, University of Milan - Bicocca (Author)
  • Federica Malinverno - , Fondazione IRCCS San Gerardo dei Tintori, University of Milan - Bicocca (Author)
  • Francesca Colapietro - , Humanitas University , IRCCS Istituto Clinico Humanitas - Rozzano (Milano) (Author)
  • Reha Akpinar - , Humanitas University , IRCCS Istituto Clinico Humanitas - Rozzano (Milano) (Author)
  • Luca Di Tommaso - , Humanitas University , IRCCS Istituto Clinico Humanitas - Rozzano (Milano) (Author)
  • Luigi Terracciano - , Humanitas University , IRCCS Istituto Clinico Humanitas - Rozzano (Milano) (Author)
  • Ana Lleo - , Humanitas University , IRCCS Istituto Clinico Humanitas - Rozzano (Milano) (Author)
  • Mauro Viganó - , ASST Papa Giovanni XXIII Hospital (Author)
  • Cristina Rigamonti - , University of Eastern Piedmont (Author)
  • Daniela Cabibi - , University of Palermo (Author)
  • Vincenza Calvaruso - , University of Palermo (Author)
  • Fabio Gibilisco - , Gravina and St. Peter's Hospital, University of Catania (Author)
  • Nicoló Caldonazzi - , University of Verona (Author)
  • Alessandro Valentino - , Niguarda Hospital (Author)
  • Stefano Ceola - , University of Milan - Bicocca (Author)
  • Valentina Canini - , University of Milan - Bicocca (Author)
  • Eugenia Nofit - , Fondazione IRCCS San Gerardo dei Tintori, University of Milan - Bicocca (Author)
  • Marco Muselli - , Rulex Inc. (Author)
  • Julien Calderaro - , Université Paris-Est Créteil, Hôpital Henri Mondor, INSERM - Institut national de la santé et de la recherche médicale (Author)
  • Dina Tiniakos - , Aretaieion University Hospital, Newcastle University (Author)
  • Vincenzo L'Imperio - , University of Milan - Bicocca (Author)
  • Fabio Pagni - , University of Milan - Bicocca (Author)
  • Nicola Zucchini - , University of Milan - Bicocca (Author)
  • Pietro Invernizzi - , Fondazione IRCCS San Gerardo dei Tintori, University of Milan - Bicocca (Author)
  • Marco Carbone - , University of Milan - Bicocca, Niguarda Hospital (Author)
  • Jakob Nikolas Kather - , Department of Internal Medicine I, Else Kröner Fresenius Center for Digital Health, National Center for Tumor Diseases (NCT) Heidelberg (Author)

Abstract

Background & Aims: Biliary abnormalities in autoimmune hepatitis (AIH) and interface hepatitis in primary biliary cholangitis (PBC) occur frequently, and misinterpretation may lead to therapeutic mistakes with a negative impact on patients. This study investigates the use of a deep learning (DL)-based pipeline for the diagnosis of AIH and PBC to aid differential diagnosis. 

Methods: We conducted a multicenter study across six European referral centers, and built a library of digitized liver biopsy slides dating from 1997 to 2023. A training set of 354 cases (266 AIH and 102 PBC) and an external validation set of 92 cases (62 AIH and 30 PBC) were available for analysis. A novel DL model, the autoimmune liver neural estimator (ALNE), was trained on whole-slide images (WSIs) with H&E staining, without human annotations. The ALNE model was evaluated against clinico-pathological diagnoses and tested for interobserver variability among general pathologists. 

Results: The ALNE model demonstrated high accuracy in differentiating AIH from PBC, achieving an area under the receiver operating characteristic curve of 0.81 in external validation. Attention heatmaps showed that ALNE tends to focus more on areas with increased inflammation, associating such patterns predominantly with AIH. A multivariate explainable ML model revealed that PBC cases misclassified as AIH more often had ALP values between 1 × upper limit of normal (ULN) and 2 × ULN, coupled with AST values above 1 × ULN. Inconsistency among general pathologists was noticed when evaluating a random sample of the same cases (Fleiss's kappa value 0.09). 

Conclusions: The ALNE model is the first system generating a quantitative and accurate differential diagnosis between cases with AIH or PBC. 

Impact and implications: This study demonstrates the significant potential of the autoimmune liver neural estimator model, a transformer-based deep learning system, in accurately distinguishing between autoimmune hepatitis and primary biliary cholangitis using digitized liver biopsy slides without human annotation. The scientific justification for this work lies in addressing the challenge of differentiating these conditions, which often present with overlapping features and can lead to therapeutic mistakes. In addition, there is need for quantitative assessment of information embedded in liver biopsies, which are currently evaluated on qualitative or semi-quantitative methods. The results of this study are crucial for pathologists, researchers, and clinicians, providing a reliable diagnostic tool that reduces interobserver variability and improves diagnostic accuracy of these conditions. Potential methodological limitations, such as the diversity in scanning techniques and slide colorations, were considered, ensuring the robustness and generalizability of the findings.

Details

Original languageEnglish
Article number101198
JournalJHEP Reports
Volume7
Issue number2
Publication statusPublished - Feb 2025
Peer-reviewedYes

External IDs

ORCID /0000-0001-8501-1566/work/175220823

Keywords

Sustainable Development Goals

Keywords

  • Artificial intelligence, Autoimmunity, Computational pathology, Digital pathology, Liver, Rare liver diseases