MAY 11, 2016 10:30 AM PDT

Radiomics: The Potential of Image-Based Phenotyping for Precision Medicine

Speaker
  • Director/PI, Computational Imaging and Bioinformatics Laboratory (CIBL), Assistant Professor of Radiation Oncology, Harvard Medical School, Brigham and Women's Hospital, Dana Farber Cancer In
    Biography
      Dr. Aerts is Director of the Computational Imaging and Bioinformatics Laboratory (CIBL) at Harvard-DFCI. Dr. Aerts' group focuses on the development and application of advanced computational approaches applied to medical imaging data, pathology, and genomic data. Furthermore, he is a PI-member of the Quantitative Imaging Network (QIN) and Informatics Technology for Cancer Research (ITCR) initiative of the NIH.

    Abstract

    Imaging-based techniques have traditionally been restricted to the diagnosis and staging of cancer. But technological advances are moving imaging modalities into the heart of patient care. Imaging can address a critical barrier in precision medicine as solid tumors can be spatial and temporal heterogeneous, and the standard approach to tumor sampling, often invasive needle biopsy, is unable to fully capture the spatial state of the tumor. Image-based phenotyping, which represents quantification of tumor phenotype on medical imaging, is a promising development for precision medicine. Medical imaging can provide a comprehensive macroscopic picture of tumor phenotype and its environment, ideally suited to quantifying the development of tumor phenotype before, during, and after treatment. As a noninvasive technology, medical imaging can be performed at low risk and inconvenience to the patient. Radiomics can quantify this phenotype using advanced data characterization algorithms that can be used to develop biomarkers which complement those derived from biopsies. The ultimate goal of radiomics is to improve precision medicine strategies by allowing clinicians to monitor disease in real time as patients move through treatment. In this talk, Dr. Aerts will discuss recent developments from his group and collaborators performing research at the intersection of radiology and bioinformatics. Also, he will discuss recent work of building a computational image analysis system to extract a rich radiomics set and use these features to build prognostic radiomics signatures. The presentation will conclude with a discussion of future work on building integrative systems incorporating both molecular and phenotypic data to improve cancer therapies.

    Learning objectives:

    • Learn about the motivation and methodology for Computational Imaging & Radiomics.
    • Learn about the existing and future potential role of radiomics with other –omics data and within precision medicine.

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