Literature DB >> 15973178

Artificial neural network and tissue genotyping of hepatocellular carcinoma in liver-transplant recipients: prediction of recurrence.

Hector Rodriguez-Luna1, Hugo E Vargas, Thomas Byrne, Jorge Rakela.   

Abstract

BACKGROUND: Liver transplantation (LT) is the treatment of choice for early stage hepatocellular carcinoma (HCC) with excellent 5-year survival, with a recurrence rate after LT of 3.4%. An artificial neural network (ANN), combined with genotyping for microsatellite mutations/deletions (TM-GTP), was designed at the University of Pittsburgh to predict tumor recurrence with a discriminatory power of 85%. This study aims to validate the ANN/TM-GTP model on patients receiving transplants in a single center.
METHODS: Nineteen patients with HCC underwent LT at our center between 1999 and 2002 (mean follow-up of 49.3 months). The ANN/TM-GTP analysis was performed blindly to prognosticate the risk of HCC recurrence, which was then validated against the actual clinical outcomes.
RESULTS: Nineteen patients received transplants. The primary diagnosis was hepatitis C (n=16), cryptogenic cirrhosis (n=2), and autoimmune hepatitis (n=1). ANN/TM-GTP was applied to all patients. The combination of ANN/TM-GTP predicted three patients to suffer recurrence of HCC. All three had HCC recurrence within 39 months (11, 23, and 39 months) postLT and died. Fourteen patients were predicted not to have HCC recurrence, and none did. Two patients could not be classified and were termed indeterminate for recurrence.
CONCLUSION: ANN/TM-GTP had a high discriminatory power (17/19, 89.5%) in our cohort, accurately predicting HCC recurrence.

Entities:  

Mesh:

Year:  2005        PMID: 15973178     DOI: 10.1097/01.tp.0000161794.32007.d1

Source DB:  PubMed          Journal:  Transplantation        ISSN: 0041-1337            Impact factor:   4.939


  4 in total

Review 1.  Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.

Authors:  Chrysanthos D Christou; Georgios Tsoulfas
Journal:  World J Gastrointest Oncol       Date:  2022-04-15

2.  Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review.

Authors:  Quirino Lai; Gabriele Spoletini; Gianluca Mennini; Zoe Larghi Laureiro; Diamantis I Tsilimigras; Timothy Michael Pawlik; Massimo Rossi
Journal:  World J Gastroenterol       Date:  2020-11-14       Impact factor: 5.742

Review 3.  Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

Authors:  Zhi-Min Zou; De-Hua Chang; Hui Liu; Yu-Dong Xiao
Journal:  Insights Imaging       Date:  2021-03-06

4.  Applications of machine learning in cancer prediction and prognosis.

Authors:  Joseph A Cruz; David S Wishart
Journal:  Cancer Inform       Date:  2007-02-11
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.