Skip to main content
Grant Details

Grant Number: 1U24CA248010-01A1 Interpret this number
Primary Investigator: Savova, Guergana
Organization: Boston Children'S Hospital
Project Title: Cancer Deep Phenotype Extraction From Electronic Medical Records
Fiscal Year: 2020


Abstract

Summary 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.



Publications

DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction.
Authors: Hochheiser H. , Finan S. , Yuan Z. , Durbin E.B. , Jeong J.C. , Hands I. , Rust D. , Kavuluru R. , Wu X.C. , Warner J.L. , et al. .
Source: medRxiv : the preprint server for health sciences, 2023-10-26; , .
EPub date: 2023-10-26.
PMID: 37205575
Related Citations

DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction.
Authors: Hochheiser H. , Finan S. , Yuan Z. , Durbin E.B. , Jeong J.C. , Hands I. , Rust D. , Kavuluru R. , Wu X.C. , Warner J.L. , et al. .
Source: JCO clinical cancer informatics, 2023 Sep; 7, p. e2300156.
PMID: 38113411
Related Citations

An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts.
Authors: Bitterman D.S. , Goldner E. , Finan S. , Harris D. , Durbin E.B. , Hochheiser H. , Warner J.L. , Mak R.H. , Miller T. , Savova G.K. .
Source: International journal of radiation oncology, biology, physics, 2023-09-01; 117(1), p. 262-273.
EPub date: 2023-03-27.
PMID: 36990288
Related Citations

Open-source Software Sustainability Models: Initial White Paper From the Informatics Technology for Cancer Research Sustainability and Industry Partnership Working Group.
Authors: Ye Y. , Barapatre S. , Davis M.K. , Elliston K.O. , Davatzikos C. , Fedorov A. , Fillion-Robin J.C. , Foster I. , Gilbertson J.R. , Lasso A. , et al. .
Source: Journal of medical Internet research, 2021-12-02; 23(12), p. e20028.
EPub date: 2021-12-02.
PMID: 34860667
Related Citations

Characterizing the Anticancer Treatment Trajectory and Pattern in Patients Receiving Chemotherapy for Cancer Using Harmonized Observational Databases: Retrospective Study.
Authors: Jeon H. , You S.C. , Kang S.Y. , Seo S.I. , Warner J.L. , Belenkaya R. , Park R.W. .
Source: JMIR medical informatics, 2021-04-06; 9(4), p. e25035.
EPub date: 2021-04-06.
PMID: 33720842
Related Citations

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records.
Authors: Savova G.K. , Danciu I. , Alamudun F. , Miller T. , Lin C. , Bitterman D.S. , Tourassi G. , Warner J.L. .
Source: Cancer research, 2019-11-01; 79(21), p. 5463-5470.
EPub date: 2019-08-08.
PMID: 31395609
Related Citations




Back to Top