Generative-AI based system for accurate prediction of deceased donor liver-transplant (DDLT) outcome and viability

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PROJECT SUMMARY There are 4.5 million US patients with chronic liver disease, acute liver failures, and liver cancer. At end stages, patients need to undergo deceased donor liver transplant (DDLT), where the whole liver is explanted from clinically dead donors. After liver-explant, the tissue is rapidly frozen, and a small sample from the donor organ is sliced and processed for rapid histopathological evaluation. Evaluation must be completed during the highly limited time the organ can remain viable for transplantation. The procedure of histopathological evaluation using “frozen sections” differs from standard “paraffin-embedded” one, which is time-consuming due to additional steps to extract water and other substances from tissue. Those steps limit how tissue can be stained with dyes to produce special-stains for histopathological evaluation for fibrosis and connective tissue. Masson’s Trichrome (MT) is not available to pathologists during DDLT. Thus, histopathological evaluation of frozen sections is highly challenging, which can lead to poor outcomes and rejection of viable marginal livers, which could be transplanted in patients. To overcome these limitations, the objective of this proposal is to develop and validate a Generative AI-based system that can produce in-silico “virtual” slides to assist pathologists and surgeons during DDLT. The proposed system will also include a Predictive-AI model for DDLT outcome. The generative module will use a transformation model trained on the texture patterns of fibrosis and connective tissue in input hematoxylin and eosin (HE) sections. The predictive module will i) extract features from both generated MT and input HE slides, ii) fuse them with recipient clinical data, and iii) use deep-learning based model to predict post-transplant outcome. The proposed system can significantly help organ donor organizations in the process of DDLT recipients’ selection while enhancing DDLT viability rates. Our preliminary study shows that the proposed system can produce diagnostic-enabled virtual slides that are validated by experienced pathologists. Also, the study shows our predictive model can perform automatic quantification of fibrosis in the virtual slides with accuracy of 86%. In this proposal (1) we will develop a deep learning-based software that can produce augmented MT layers on the digital HE slides of liver allograft’s frozen sections, and (2) we will extend the software to be capable of predicting the outcome of liver-transplant by learning histopathological and other clinical markers from recipients that predict post- transplant viability. The impacts of our technology are: a) efficient and accurate histopathological evaluation of liver allograft during DDLT, b) improved post-transplant outcome and survival rate, c) reduced operational cost in histology laboratories, and d) accelerated pathology workflows and digital pathology transformation.