Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology)

Tommy Ivanics, Madhukar S. Patel, Lauren Erdman, Gonzalo Sapisochin

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

PURPOSE OF REVIEW: To highlight recent efforts in the development and implementation of machine learning in transplant oncology - a field that uses liver transplantation for the treatment of hepatobiliary malignancies - and particularly in hepatocellular carcinoma, the most commonly treated diagnosis in transplant oncology. RECENT FINDINGS: The development of machine learning has occurred within three domains related to hepatocellular carcinoma: identification of key clinicopathological variables, genomics, and image processing. SUMMARY: Machine-learning classifiers can be effectively applied for more accurate clinical prediction and handling of data, such as genetics and imaging in transplant oncology. This has allowed for the identification of factors that most significantly influence recurrence and survival in disease, such as hepatocellular carcinoma, and thus help in prognosticating patients who may benefit from a liver transplant. Although progress has been made in using these methods to analyse clinicopathological information, genomic profiles, and image processed data (both histopathological and radiomic), future progress relies on integrating data across these domains.

Original languageEnglish (US)
Pages (from-to)426-434
Number of pages9
JournalCurrent opinion in organ transplantation
Volume25
Issue number4
DOIs
StatePublished - Aug 1 2020
Externally publishedYes

ASJC Scopus subject areas

  • Immunology and Allergy
  • Transplantation

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