The 2022 Banff Meeting Lung Report

Elizabeth N. Pavlisko, Benjamin A. Adam, Gerald J. Berry, Fiorella Calabrese, Nahir Cortes-Santiago, Carolyn H. Glass, Martin Goddard, John R. Greenland, Daniel Kreisel, Deborah J. Levine, Tereza Martinu, Stijn E. Verleden, S. Sam Weigt, Antoine Roux

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The Lung Session of the 2022 16th Banff Foundation for Allograft Pathology Conference—held in Banff, Alberta—focused on non-rejection lung allograft pathology and novel technologies for the detection of allograft injury. A multidisciplinary panel reviewed the state-of-the-art of current histopathologic entities, serologic studies, and molecular practices, as well as novel applications of digital pathology with artificial intelligence, gene expression analysis, and quantitative image analysis of chest computerized tomography. Current states of need as well as prospective integration of the aforementioned tools and technologies for complete assessment of allograft injury and its impact on lung transplant outcomes were discussed. Key conclusions from the discussion were: (1) recognition of limitations in current standard of care assessment of lung allograft dysfunction; (2) agreement on the need for a consensus regarding the standardized approach to the collection and assessment of pathologic data, inclusive of all lesions associated with graft outcome (eg, non-rejection pathology); and (3) optimism regarding promising novel diagnostic modalities, especially minimally invasive, which should be integrated into large, prospective multicenter studies to further evaluate their utility in clinical practice for directing personalized therapies to improve graft outcomes.

Original languageEnglish
Pages (from-to)542-548
Number of pages7
JournalAmerican Journal of Transplantation
Volume24
Issue number4
DOIs
StatePublished - Apr 2024

Keywords

  • artificial intelligence
  • lung transplantation
  • molecular
  • pathology

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