Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Tumor Progression

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Transforming unstructured breast cancer pathology reports into the Observational Medical Outcomes Partnership Common Data Model.

BMC medical informatics and decision making·2026

Same author

Epidemiology and Clinical Characteristics of Community-Acquired Pneumonia in Hospitalized Patients at a Single Tertiary Center in Korea.

Journal of Korean medical science·2025

Same author

Pertuzumab Enhances the Antitumor Activity of T-DXd in HER2-Positive Gastric Cancer Cells.

Molecular cancer therapeutics·2025

Same author

Bevacizumab plus Erlotinib in Advanced Solid Cancers with Krebs Cycle Gene Mutations: A Multicenter Phase II Study (BRISK; KCSG AL22-16).

Clinical cancer research : an official journal of the American Association for Cancer Research·2025

Same author

COPS5 inhibition synergizes with the antitumor effect of trastuzumab by PTEN upregulation in HER2-amplified gastric cancer.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association·2025

Same author

Surgical treatment of hepatocellular carcinoma: an expert consensus-based practical recommendation from the Korean Liver Cancer Association.

Annals of surgical treatment and research·2025

Search research articles

Related Experiment Video

Updated: Nov 26, 2025

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer.

Borim Ryu¹, Eunsil Yoon¹, Seok Kim¹

¹Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

Journal of Medical Internet Research

|December 9, 2020

Summary

This study developed a colon cancer pathology data system using natural language processing to standardize electronic health records. The system enables better utilization of clinical and omics data for future cancer research.

Keywords:

clinical data colon cancer common data model electronic health record natural language processing oncology oncology module pathology

More Related Videos

Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication

Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication

Published on: December 15, 2023

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Related Experiment Videos

Last Updated: Nov 26, 2025

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication

Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication

Published on: December 15, 2023

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

Medical Informatics
Bioinformatics
Oncology

Background:

Common Data Models (CDMs) are crucial for standardizing electronic health record (EHR) data in research.
Analyzing pathology reports is essential for building data infrastructure for colon cancer research.
Existing Observational Medical Outcomes Partnership (OMOP) CDM requires conversion of free-text pathology reports, with limited use cases for cancer data.

Purpose of the Study:

To construct a CDM database for colon cancer pathology data using natural language processing (NLP).
To create a research platform integrating clinical and omics data for colon cancer studies.
To extract, standardize, and convert essential text entities from pathology reports into the OMOP CDM format.

Main Methods:

Extracted clinical text entities from pathology reports using NLP and regular expressions in Python.
Mapped extracted entities to standard concepts in Observational Health Data Sciences and Informatics (OHDSI) vocabularies and Systematized Nomenclature of Medicine (SNOMED).
Built CDM tables and defined relationships, incorporating a custom dictionary for normalization and standardization of terms like biomarkers and gene names.

Main Results:

Standardized 1848 immunohistochemical, 3890 molecular, and 12,352 surgical specimen pathology reports (2017-2018).
Constructed a database containing extracted colorectal entities: NOTE_NLP, MEASUREMENT, CONDITION_OCCURRENCE, SPECIMEN, and FACT_RELATIONSHIP.
Successfully integrated pathology data into OMOP CDM tables for research utilization.

Conclusions:

Prepared CDM data for a research platform at Seoul National University Bundang Hospital to leverage comprehensive clinical and omics data for colon cancer pathology.
Highlighted the need for more sophisticated pathology data preparation for cancer genomics research.
Identified various text narratives as targets for future research on CDM data utilization.