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Perspectives on making big data analytics work for oncology.

Issam El Naqa1

  • 1University of Michigan, Department of Radiation Oncology, Ann Arbor, MI, United States.

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Summary
This summary is machine-generated.

Big data analytics in oncology offers potential to improve cancer treatment safety and outcomes by integrating diverse patient data. This study addresses challenges in data structure and analytics, proposing methods to counter statistical inference problems for better decision-making.

Keywords:
Big dataClinical decision supportMachine learningOncology

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Area of Science:

  • Oncology
  • Big Data Analytics
  • Bioinformatics

Background:

  • Oncology generates vast, heterogeneous data (5Vs of big data) from treatments like chemoradiotherapy.
  • This data includes demographics, dosimetry, imaging, and biological markers, spanning days to weeks.
  • Current efforts focus on data aggregation and secure analytics, but data structure and effective decision-support tools remain challenges.

Purpose of the Study:

  • To explore the application of big data analytics in oncology for enhanced treatment safety and outcomes.
  • To address challenges in representing oncology data structures and developing effective analytics tools.
  • To discuss methods for overcoming statistical inference problems (p >> n) in big data oncology.

Main Methods:

  • Discussing methods for processing mixed structured and unstructured oncology data (relational/NoSQL databases).
  • Exploring advanced analytics and image feature extraction techniques for oncology.
  • Presenting "small thinking" methodologies to counter big data inference issues, including prior knowledge, information theory, and ensemble machine learning.

Main Results:

  • Oncology data presents unique big data challenges, including the p >> n inference problem exacerbated by 'p-omics' growth.
  • Potential for undesirable effects like echo chamber anomalies and Simpson's paradox in big data oncology analysis.
  • Evaluation of various approaches to improve big data mining in oncology.

Conclusions:

  • Big data analytics holds significant promise for advancing oncology care.
  • Addressing data structure, analytics tools, and statistical inference challenges is crucial for realizing this potential.
  • Methodologies combining prior knowledge, information theory, and machine learning offer promising avenues for improved oncology big data mining.