TY - JOUR
T1 - The 2022 Banff Meeting Lung Report
AU - Pavlisko, Elizabeth N.
AU - Adam, Benjamin A.
AU - Berry, Gerald J.
AU - Calabrese, Fiorella
AU - Cortes-Santiago, Nahir
AU - Glass, Carolyn H.
AU - Goddard, Martin
AU - Greenland, John R.
AU - Kreisel, Daniel
AU - Levine, Deborah J.
AU - Martinu, Tereza
AU - Verleden, Stijn E.
AU - Weigt, S. Sam
AU - Roux, Antoine
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/4
Y1 - 2024/4
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - lung transplantation
KW - molecular
KW - pathology
UR - http://www.scopus.com/inward/record.url?scp=85177812413&partnerID=8YFLogxK
U2 - 10.1016/j.ajt.2023.10.022
DO - 10.1016/j.ajt.2023.10.022
M3 - Article
C2 - 37931751
AN - SCOPUS:85177812413
SN - 1600-6135
VL - 24
SP - 542
EP - 548
JO - American Journal of Transplantation
JF - American Journal of Transplantation
IS - 4
ER -