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Constraint based temporal event sequence mining for Glioblastoma survival prediction.

Kunal Malhotra1, Shamkant B Navathe1, Duen Horng Chau1

  • 1College of Computing, Georgia Institute of Technology, Atlanta, GA, USA.

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|April 12, 2016
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Summary

Predicting Glioblastoma (GBM) survival is key for personalized medicine. This study identifies clinical, genomic, and treatment pattern factors that accurately predict patient survival, aiding in treatment plan development.

Keywords:
ClassificationGlioblastomaGraph miningPredictive modelSequential pattern miningTreatment patterns

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Area of Science:

  • Oncology
  • Bioinformatics
  • Medical Informatics

Background:

  • Glioblastoma (GBM) is a rare cancer with significant treatment challenges.
  • Developing personalized treatment plans for GBM patients is crucial for improving outcomes.

Purpose of the Study:

  • To predict which Glioblastoma (GBM) patients will survive longer than the median survival time.
  • To assess the predictive power of treatment patterns in conjunction with clinical and genomic factors.

Main Methods:

  • Developed a predictive model using clinical and genomic data from approximately 300 newly diagnosed GBM patients.
  • Employed sequential mining algorithms with 'exact-order' and 'temporal overlap' constraints to extract treatment patterns.
  • Utilized logistic regression and Cox regression models to predict patient survival outcomes.

Main Results:

  • Identified key predictive features including gene mRNA expression levels, age, Karnofsky performance score, and prescribed therapeutic agents.
  • Achieved a c-statistic of 0.85 with the logistic regression model and 0.84 with the Cox regression model.
  • Demonstrated the significant impact of diverse feature sources on survival prediction accuracy.

Conclusions:

  • Diverse data sources, including clinical, genomic, and treatment patterns, are vital for predicting GBM patient survival.
  • The developed predictive model represents a foundational step towards personalized treatment strategies for GBM.
  • The methodology can be extended to other cancer types for broader application in personalized oncology.