Raja R Narayan1, Natasha Abadilla1, Linfeng Yang2, Simon B Chen3, Mac Klinkachorn2, Hyrum S Eddington4, Amber W Trickey4, John P Higgins3, Marc L Melcher5. 1. Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA. 2. Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, USA. 3. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA. 4. Stanford-Surgery Policy Improvement Research and Education Center, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA. 5. Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: melcherm@stanford.edu.
Abstract
BACKGROUND: Donor livers undergo subjective pathologist review of steatosis before transplantation to mitigate the risk for early allograft dysfunction (EAD). We developed an objective, computer vision artificial intelligence (CVAI) platform to score donor liver steatosis and compared its capability for predicting EAD against pathologist steatosis scores. METHODS: Two pathologists scored digitized donor liver biopsy slides from 2014 to 2019. We trained four CVAI platforms with 1:99 training:prediction split. Mean intersection-over-union (IU) characterized CVAI model accuracy. We defined EAD using liver function tests within 1 week of transplantation. We calculated separate EAD logistic regression models with CVAI and pathologist steatosis and compared the models' discrimination and internal calibration. RESULTS: From 90 liver biopsies, 25,494 images trained CVAI models yielding peak mean IU = 0.80. CVAI steatosis scores were lower than pathologist scores (median 3% vs 20%, P < 0.001). Among 41 transplanted grafts, 46% developed EAD. The median CVAI steatosis score was higher for those with EAD (2.9% vs 1.9%, P = 0.02). CVAI steatosis was independently associated with EAD after adjusting for donor age, donor diabetes, and MELD score (aOR = 1.34, 95%CI = 1.03-1.75, P = 0.03). CONCLUSION: The CVAI steatosis EAD model demonstrated slightly better calibration than pathologist steatosis, meriting further investigation into which modality most accurately and reliably predicts post-transplantation outcomes.
BACKGROUND: Donor livers undergo subjective pathologist review of steatosis before transplantation to mitigate the risk for early allograft dysfunction (EAD). We developed an objective, computer vision artificial intelligence (CVAI) platform to score donor liver steatosis and compared its capability for predicting EAD against pathologist steatosis scores. METHODS: Two pathologists scored digitized donor liver biopsy slides from 2014 to 2019. We trained four CVAI platforms with 1:99 training:prediction split. Mean intersection-over-union (IU) characterized CVAI model accuracy. We defined EAD using liver function tests within 1 week of transplantation. We calculated separate EAD logistic regression models with CVAI and pathologist steatosis and compared the models' discrimination and internal calibration. RESULTS: From 90 liver biopsies, 25,494 images trained CVAI models yielding peak mean IU = 0.80. CVAI steatosis scores were lower than pathologist scores (median 3% vs 20%, P < 0.001). Among 41 transplanted grafts, 46% developed EAD. The median CVAI steatosis score was higher for those with EAD (2.9% vs 1.9%, P = 0.02). CVAI steatosis was independently associated with EAD after adjusting for donor age, donor diabetes, and MELD score (aOR = 1.34, 95%CI = 1.03-1.75, P = 0.03). CONCLUSION: The CVAI steatosis EAD model demonstrated slightly better calibration than pathologist steatosis, meriting further investigation into which modality most accurately and reliably predicts post-transplantation outcomes.
Authors: Ștefan Busnatu; Adelina-Gabriela Niculescu; Alexandra Bolocan; George E D Petrescu; Dan Nicolae Păduraru; Iulian Năstasă; Mircea Lupușoru; Marius Geantă; Octavian Andronic; Alexandru Mihai Grumezescu; Henrique Martins Journal: J Clin Med Date: 2022-04-18 Impact factor: 4.964
Authors: Katherine Xu; Siavash Raigani; Angela Shih; Sofia G Baptista; Ivy Rosales; Nicola M Parry; Stuti G Shroff; Joseph Misdraji; Korkut Uygun; Heidi Yeh; Katherine Fairchild; Leigh Anne Dageforde Journal: Transplant Direct Date: 2022-08-04