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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Published on: October 10, 2018

Using decision tree learning to predict the responsiveness of hepatitis C patients to drug treatment

Yoshihiro Kawamura1, Shigeru Takasaki, Masashi Mizokami

  • 1The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Konodai, Ichikawa, Chiba 272-8516, Japan.

FEBS Open Bio
|May 8, 2013
PubMed

Insights

Predicting hepatitis C virus (HCV) genotype 1 treatment success is crucial. A new decision tree model using genetic data accurately predicts patient response to pegylated interferon-α and ribavirin therapy.

Area of Science:

  • Hepatology
  • Genetics
  • Bioinformatics

Background:

  • Standard hepatitis C treatment (pegylated interferon-α plus ribavirin) has limited efficacy for genotype 1.
  • Predicting treatment outcomes is essential for personalized medicine in chronic hepatitis C.

Purpose of the Study:

  • To develop a predictive model for sustained virologic response in hepatitis C virus genotype 1 patients.
  • To utilize genome-wide association study data and decision tree learning for treatment outcome prediction.

Main Methods:

  • A decision tree learning model was applied to genetic data (SNPs) from 142 Japanese patients with HCV genotype 1.
  • Patient data included those with null virologic response (78) and virologic response (64).

Main Results:

  • The decision tree model achieved a high prediction accuracy of 93% for treatment response.
  • The model effectively distinguishes patients likely to benefit from standard therapy.

Conclusions:

  • A novel, accurate prediction method for HCV genotype 1 treatment response has been developed.
  • This approach, using genetic markers and machine learning, can guide clinical decisions for hepatitis C patients.

Keywords:
Chronic hepatitis C genotype 1Decision tree learningGDI, Gini diversity indexGWAS, genome-wide association studyGenome-wide association studyHCV, hepatitis C virusHet, one major and one minor genotypeMM, both major genotypesNVR, null virologic responseNull virologic responseOR, Odds ratioPEG-IFN-α, pegylated interferon αPegylated interferon αRBV, ribavirinRebavirinSNPs, single nucleotide polymorphismsSVR, sustained virologic responseSingle nucleotide polymorphismSustained virologic responsemm, both minor genotypes

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