Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: A pilot study

Alessandro Cucchetti, Fabio Piscaglia, Antonia D Errico Grigioni, Matteo Ravaioli, Matteo Cescon, Matteo Zanello, Gian Luca Grazi, Rita Golfieri, Walter Franco Grigioni, Antonio Daniele Pinna

Research output: Contribution to journalArticlepeer-review

Abstract

Background & Aims: Hepatocellular carcinoma (HCC) prognosis strongly depends upon nuclear grade and the presence of microscopic vascular invasion (MVI). The aim of this study was to develop an artificial neural network (ANN) that is able to predict tumour grade and MVI on the basis of non-invasive variables. Methods: Clinical, radiological, and histological data from 250 cirrhotic patients resected (n = 200) or transplanted (n = 50) for HCC were analyzed. ANN and logistic regression models were built on a training group of 175 randomly chosen patients and tested on the remaining testing group of 75. Receiver operating characteristics curve (ROC) and k-statistics were used to analyze model accuracy in the prediction of the final histological assessment of tumour grade (G1-G2 vs. G3-G4) and MVI (absent vs. present). Results: Pathologic examination showed G3-G4 in 69.6% of cases and MVI in 74.4%. Preoperative serum alpha-fetoprotein (AFP), tumour number, size, and volume were related to tumour grade and MVI (p

Original languageEnglish
Pages (from-to)880-888
Number of pages9
JournalJournal of Hepatology
Volume52
Issue number6
DOIs
Publication statusPublished - Jun 2010

Keywords

  • Artificial neural network
  • Hepatocellular carcinoma
  • Liver resection
  • Liver transplantation
  • Microscopic vascular invasion
  • Tumour grade

ASJC Scopus subject areas

  • Hepatology

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