Integrative analysis of cancer imaging readouts by networks

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations


Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time-space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node- and/or edge-valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre-clinical and clinical impact.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalMolecular Oncology
Issue number1
StatePublished - Jan 1 2015


  • Cancer hallmarks
  • Integrative inference
  • Molecular imaging
  • Networks

ASJC Scopus subject areas

  • Cancer Research
  • Genetics
  • Molecular Medicine


Dive into the research topics of 'Integrative analysis of cancer imaging readouts by networks'. Together they form a unique fingerprint.

Cite this