||1U24CA248010-01A1 Interpret this number
||Boston Children'S Hospital
||Cancer Deep Phenotype Extraction From Electronic Medical Records
Precise phenotype information is needed to advance translational cancer research, particularly to unravel the
effects of genetic, epigenetic, and systems changes on tumor behavior and responsiveness. Examples of
phenotypic variables in cancer include: tumor morphology (e.g. histopathologic diagnosis), co-morbid
conditions (e.g. associated immune disease), laboratory findings (e.g. gene amplification status), specific tumor
behaviors (e.g. metastasis) and response to treatment (e.g. effect of a chemotherapeutic agent on tumor).
Current models for correlating EMR data with –omics data largely ignore the clinical text, which remains one of
the most important sources of phenotype information for cancer patients. Unlocking the value of clinical text
has the potential to enable new insights about cancer initiation, progression, metastasis, and response to
treatment. We propose further collaboration to enhance the DeepPhe platform with new methods for cancer
deep phenotyping. Several aims propose investigation of biomedical information extraction where there has
been little or no previous work (e.g. clinical genomic). Visualization of extracted data, usability of the software,
and dissemination are also emphasized. A diverse set of oncology studies led by accomplished translational
investigators in Breast Cancer, Melanoma, Ovarian Cancer, Colorectal Cancer and Diffuse Large B-cell
Lymphoma will demonstrate the utility of the software. These labs will contribute phenotype variables for
extraction, test utility and usability of the software, and provide the setting for an extrinsic evaluation. The
proposed research bridges novel methods to automate cancer deep phenotype extraction from clinical text with
emerging standards in phenotype knowledge representation and NLP. This work is highly aligned with recent
calls in the scientific literature to advance scalable and robust methods of extracting and representing
phenotypes for precision medicine and translational research.
Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records.
, Danciu I.
, Alamudun F.
, Miller T.
, Lin C.
, Bitterman D.S.
, Tourassi G.
, Warner J.L.
Cancer research, 2019-11-01; 79(21), p. 5463-5470.