The Emerging Role of Generative Artificial Intelligence in Transplant Medicine.

Generative artificial intelligence (AI), a subset of machine learning that creates new content based on training data, has witnessed tremendous advances in recent years. Practical applications have been identified in healthcare in general, and there is significant opportunity in transplant medicine for generative AI to simplify tasks in research, medical education as well as clinical practice. Additionally, patients stand to benefit from patient education that is more readily provided by generative AI applications. This review aims to catalyze the development and adoption of generative AI in transplantation by introducing basic AI and generative AI concepts to the transplant clinician, and summarizing its current and potential applications within the field. We provide an overview of applications to the clinician, researcher, educator, and patient. We also highlight the challenges involved in bringing these applications to the bedside, and need for ongoing refinement of generative AI applications to sustainably augment the transplantation field.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

A Comprehensive Review of Liver Allograft Fibrosis and Steatosis: From Cause to Diagnosis.

Despite advances in posttransplant care, long-term outcomes for liver transplant recipients remain unchanged. Approximately 25% of recipients will advance to graft cirrhosis and require retransplantation. Graft fibrosis progresses in the context of de novo or recurrent disease. Recurrent hepatitis C virus infection was previously the most important cause of graft failure but is now curable in the majority of patients. However, with an increasing prevalence of obesity and diabetes and nonalcoholic fatty liver disease as the most rapidly increasing indication for liver transplantation, metabolic dysfunction-associated liver injury is anticipated to become an important cause of graft fibrosis alongside alloimmune hepatitis and alcoholic liver disease. To better understand the landscape of the graft fibrosis literature, we summarize the associated epidemiology, cause, potential mechanisms, diagnosis, and complications. We additionally highlight the need for better noninvasive methods to ameliorate the management of graft fibrosis. Some examples include leveraging the microbiome, genetic, and machine learning methods to address these limitations. Overall, graft fibrosis is routinely seen by transplant clinicians, but it requires a better understanding of its underlying biology and contributors that can help inform diagnostic and therapeutic practices.

Artificial intelligence, machine learning, and deep learning in liver transplantation.

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Compound computer vision workflow for efficient and automated immunohistochemical analysis of whole slide images.

AIMS: Immunohistochemistry (IHC) assessment of tissue is a central component of the modern pathology workflow, but quantification is challenged by subjective estimates by pathologists or manual steps in semi-automated digital tools. This study integrates various computer vision tools to develop a fully automated workflow for quantifying Ki-67, a standard IHC test used to assess cell proliferation on digital whole slide images (WSIs). METHODS: We create an automated nuclear segmentation strategy by deploying a Mask R-CNN classifier to recognise and count 3,3'-diaminobenzidine positive and negative nuclei. To further improve automation, we replaced manual selection of regions of interest (ROIs) by aligning Ki-67 WSIs with corresponding H&E-stained sections, using scale-invariant feature transform (SIFT) and a conventional histomorphological convolutional neural networks to define tumour-rich areas for quantification. RESULTS: The Mask R-CNN was tested on 147 images generated from 34 brain tumour Ki-67 WSIs and showed a high concordance with aggregate pathologists' estimates ([Formula: see text] assessors; [Formula: see text] r=0.9750). Concordance of each assessor's Ki-67 estimates was higher when compared with the Mask R-CNN than between individual assessors (ravg=0.9322 vs 0.8703; p=0.0213). Coupling the Mask R-CNN with SIFT-CNN workflow demonstrated ROIs can be automatically chosen and partially sampled to improve automation and dramatically decrease computational time (average: 88.55-19.28 min; p<0.0001). CONCLUSIONS: We show how innovations in computer vision can be serially compounded to automate and improve implementation in clinical workflows. Generalisation of this approach to other ancillary studies has significant implications for computational pathology.

PD-L1 assessment in cytology samples predicts treatment response to checkpoint inhibitors in NSCLC.

BACKGROUND: Testing for tumor programmed death ligand-1 (PD-L1) expression was initially developed with histology specimens in non-small cell lung cancer (NSCLC). However, cytology specimens are widely used for primary diagnosis and biomarker studies in clinical practice. Limited clinical data exist on the predictiveness of cytology-derived PD-L1 scores for response to immune checkpoint inhibitor (ICI) therapy. METHODS: We reviewed all NSCLC specimens clinically tested at the University Health Network (UHN) for PD-L1 with 22C3pharmDx, from 01/2013 to 04/2021. Treatment outcomes in patients treated with single agent ICI therapy were reviewed and compared according to cytology- and histology-derived PD-L1 scores. RESULTS: We identified 494 and 1942 unique patients with cytology- and histology-derived tumor proportion scores, respectively, during the study period. Informative testing rates were 95 % vs 98 % for cytology and histology, respectively. Clinical data were available for 152 patients treated with single agent ICI: 61 cytology and 91 histology. Overall response rates (ORR) were similar for cytology and histology (36 % vs 34 %; p = 0.23), as well as median progression free survival (PFS) (4.9 vs 4.2 months; p = 0.99) and overall survival (23.4 vs 19.7 months; p = 0.99). The results remained similar even after adjusting for PD-L1 expression levels and line of ICI treatment (PFS HR 1.15; 95 %CI 0.78-1.70; p = 0.47). CONCLUSIONS: Treatment outcomes to single agent ICI based on cytology-derived PD-L1 scores were comparable to histology controls. Our results support PD-L1 biomarker testing on both cytology and histology specimens.

Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning.

BACKGROUND: Modern molecular pathology workflows in neuro-oncology heavily rely on the integration of morphologic and immunohistochemical patterns for analysis, classification, and prognostication. However, despite the recent emergence of digital pathology platforms and artificial intelligence-driven computational image analysis tools, automating the integration of histomorphologic information found across these multiple studies is challenged by large files sizes of whole slide images (WSIs) and shifts/rotations in tissue sections introduced during slide preparation. METHODS: To address this, we develop a workflow that couples different computer vision tools including scale-invariant feature transform (SIFT) and deep learning to efficiently align and integrate histopathological information found across multiple independent studies. We highlight the utility and automation potential of this workflow in the molecular subclassification and discovery of previously unappreciated spatial patterns in diffuse gliomas. RESULTS: First, we show how a SIFT-driven computer vision workflow was effective at automated WSI alignment in a cohort of 107 randomly selected surgical neuropathology cases (97/107 (91%) showing appropriate matches, AUC = 0.96). This alignment allows our AI-driven diagnostic workflow to not only differentiate different brain tumor types, but also integrate and carry out molecular subclassification of diffuse gliomas using relevant immunohistochemical biomarkers (IDH1-R132H, ATRX). To highlight the discovery potential of this workflow, we also examined spatial distributions of tumors showing heterogenous expression of the proliferation marker MIB1 and Olig2. This analysis helped uncover an interesting and unappreciated association of Olig2 positive and proliferative areas in some gliomas (r = 0.62). CONCLUSION: This efficient neuropathologist-inspired workflow provides a generalizable approach to help automate a variety of advanced immunohistochemically compatible diagnostic and discovery exercises in surgical neuropathology and neuro-oncology.