JMIR MEDICAL INFORMATICS

Ji et alOriginal Paper

Integrating Clinical Data and Medical Imaging in Lung Cancer:
Feasibility Study Using the Observational Medical Outcomes
Partnership Common Data Model Extension
Hyerim Ji1,2, MS; Seok Kim1, MPH; Leonard Sunwoo3, MD, PhD; Sowon Jang3, MD; Ho-Young Lee1,4, MD, PhD;
Sooyoung Yoo1, PhD
1

Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea

2

Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republicof Korea
3

Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea

4

Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea

Corresponding Author:
Sooyoung Yoo, PhD
Office of eHealth Research and BusinessSeoul National University Bundang Hospital
172, Dolma-ro
Bundang-gu
Seongnam-si, 13605
Republic of Korea
Phone: 82 317878980
Fax: 82 317874061
Email: yoosoo0@snubh.org

Abstract
Background: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized
medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data.
However, integrating medical imaging remains a challenge.
Objective: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal
research.
Methods: Our approach included the analysis and selection of digital imaging and communications in medicine header tags,
validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources
ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM),
constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patientswith lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables—IMAGING_STUDY, IMAGING_SERIES,IMAGING_ANNOTATION, and FILEPATH—to standardize various imaging-related data and link to clinical data.
Results: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics
and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive dataset, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records,
illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lungcancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrityof the constructed data set, with all checks successfully passed, underscoring the reliability of our findings.
Conclusions: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinicaldata. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics
and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its
wide-ranging utility for medical conditions.
(JMIR Med Inform 2024;12:e59187) doi: 10.2196/59187

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Ji et alKEYWORDS
DICOM; OMOP; CDM; lung cancer; medical imaging; data integration; data quality; Common Data Model; Digital Imaging
and Communications in Medicine; Observational Medical Outcomes Partnership

Introduction
The accessibility and use of health information in various
formats and standards are limited, further limiting thedevelopment of advanced data analytics technologies, especiallyin an era where machine learning and other cutting-edge
technologies have become essential for medical research.
Integrating these sophisticated analytical tools requires aparadigm shift toward the standardization and harmonizationof health care data [1,2]. Standardized data structures are not
only beneficial but essential for the effective application ofmachine learning algorithms as they ensure consistent data
quality (DQ), interoperability, and comprehensive analysis
across different health care domains. By moving to standardized
data formats, we laid the foundation for a more powerful andscalable application of emerging technologies, opening up new
possibilities in medical research and patient care.
Several standardization projects and technologies have emergedin response to the demand for integrated approaches [3,4].
Among these, the Observational Medical Outcomes Partnership(OMOP) Common Data Model (CDM) is noteworthy for its
advantages in converting diverse sources of data into a consistentformat [5,6]. It harmonizes the structure and content of various
clinical data sets, facilitating consistent analytical approaches
in multi-institutional research. These characteristics ensure highefficiency and accuracy of data interpretation and use, thereby
enhancing both the quality and pace of research in the rapidly
evolving field of medicine.
Digital Imaging and Communications in Medicine (DICOM)
is a universal standard for managing, storing, and transmitting
medical images, ensuring interoperability and improved
exchange of medical image data and associated information
between health care systems. Recent research has focused on
the integrated analysis of DICOM and OMOP CDM to promote
accessibility to complex medical imaging data and electronic
health records [7-11]. These efforts aim to combine detailed
imaging metrics with diverse clinical data to contribute to thedevelopment of diagnostic and therapeutic strategies throughcomprehensive data analysis. However, the data duplication
problem caused by constructing an instance-level table and theabsence of a table that can store annotation data such as labeling(commonly used in image analysis) are major limitations. Theselimitations must be addressed to effectively manage the complex
characteristics of medical imaging data and perform an

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integrated analysis with clinical information in the OMOP CDM.
As the complexity of medical imaging data and the range of
DICOM tags increase, effective solutions are required tointegrate data seamlessly and consistently.
Lung cancer is the leading cause of cancer-related deaths
worldwide and accounts for >20% of all cancer fatalities inSouth Korea. The etiology, progression, and therapeutic
response of lung cancer are intricately linked to a myriad of
biological and genetic factors. Therefore, a systematic
understanding of the characteristics of lung cancer is paramountfor its early detection, prevention, and construction of
personalized treatment strategies [12,13]. However, this requires
an approach that efficiently integrates high-resolution data acrossvarious fields.
In this study, we propose a method to integrate medical imaging
data with the OMOP CDM, aimed at enhancing multimodal
research capabilities. This approach involves converting DICOM
metadata and its annotation data to fit within the OMOP CDM
framework and subsequently integrating it into a designed
Imaging Common Data Model (I-CDM). We applied thisintegrated framework to a specific cohort of patients with lungcancer and brain metastases to not only test the feasibility andutility of our approach but also demonstrate its practicalapplication through a series of research scenarios. Additionally,
the use of scenarios was intended to showcase use cases thatvalidate the operational functionality of our proposed model
within real-world research settings.

Methods
Overview
We systematically analyzed and selected the DICOM header
tags, verified their data formats, and mapped them to the OMOPCDM framework. To ensure consistency and interoperability
in the column naming and definitions, we referenced the Fast
Healthcare Interoperability Resources (FHIR) image study
profiles, constructed I-CDM tables incorporating an
entity-attribute-value (EAV) model for scalability, andperformed data preprocessing to maintain data integrity. This
allowed us to construct and validate a series of scenarios thatcombined clinical and imaging information from patients withlung cancer using a structured approach while ensuring
interoperability. Figure 1 provides a visual overview of theprocesses used.

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Ji et alFigure 1. Workflow for I-CDM implementation in lung cancer research. CDM: Common Data Model; DICOM: Digital Imaging and Communicationsin Medicine; I-CDM: Imaging Common Data Model; OMOP: Observational Medical Outcomes Partnership.

DICOM Header Analysis
We selected preliminary DICOM header tags that are universally
applicable across a spectrum of modalities through a systematic
procedure. To ensure the relevance and appropriateness of thesetags, we sought consultation with radiology specialists [14-16].
While the DICOM standard provides a framework, it does notenforce a uniform data format. This lack of uniformity has led
to variations in data formats across medical institutions andmodalities. Accordingly, we validated the extractable data
formats, ensuring that only data from nonempty DICOM header
tags are extracted to maintain DQ and relevance. For data values
where no standard concepts were unavailable, we introduced
new custom concepts that are used consistently andcomprehensively within the Observational Health Data Sciences
and Informatics (OHDSI) framework. Concurrently, we
examined the FHIR standard’s ImagingStudy profile ofradiological image data to determine the appropriate table andcolumn names [17]. This approach was adopted to ensure thatthe selected header tags provided comprehensive coverage withrespect to established imaging information standards.

I-CDM Table Modeling
Our database architecture was designed to consolidate a variety
of medical imaging data types, including DICOM header tags,
study and series-level preprocessing information, and annotation
information through labeling. The organization of
IMAGING_STUDY,IMAGING_SERIES,IMAGING_ANNOTATION, and FILEPATH tables ensured
structuredandaccessible
datacollection.
The
IMAGING_STUDY
table
referstothe
CDM
PROCEDURE_OCCURRENCE table associated with thecorresponding imaging-order information.

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To understand the significance of series-level analysis in medical
imaging with reference to the ImagingStudy profile of the FHIR
standard, we modeled the IMAGING_SERIES table [18-20] tohouse values derived from DICOM header tags pertinent to theseries. Considering the potential variability in series-specificdetails owing to distinct imaging equipment or researchrequirements, we adopted the EAV format. The EAV model
provides a data representation framework for the scalable andflexible storage of entities, in which the number and types of
attributes (properties and parameters) can vary [21,22]. Using
EAV, each attribute-value pair is stored as a separate record,
making it easier to populate new data rows without altering thefoundational database schema. This model facilitates data
expansion without necessitating changes to the foundational
table structure.
To observe the emergence and ubiquity of automated labelingtools in radiology, we designed an IMAGING_ANNOTATION
table [23-26] structured to retain minimal metadata originatingfrom the tools. Similar to the IMAGING_SERIES table, weused an EAV approach to promote the extension of the metadata.
Finally, in response to the importance of the file size inimage-oriented
researchandartificial
intelligence
implementations, we designed a FILEPATH table. This database
captured fundamental attributes, such as file size, location, andspecific format. Figure 2 shows the diagram constructedaccording to the I-CDM table definition. In Multimedia
Appendix 1, we provide detailed definitions for each column
inthe
IMAGING_STUDY,IMAGING_SERIES,IMAGING_ANNOTATION, and FILEPATH tables for I-CDM.
Multimedia Appendix 1 elucidates the data format and captures
broadly the attributes of the I-CDM framework. In addition, it
specifies whether a column is mandatory, ensuring
comprehensive documentation and consistency across data sets.

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Ji et alFigure 2. Diagram of Imaging Common Data Model workflow and attributes.

We analyzed and mapped the categorically constructed columns
to the corresponding CDM concept IDs where feasible. The
modality information was mapped to the concept IDs of theProcedure class in the CDM specifically related to the imaging
equipment. This mapping enabled cross-validation using themethod attributes of the linked procedure. The body partcategory was also aligned with the CDM concept, resulting interms like “chest” being mapped to the Procedure class as “chest
imaging.” For the IMAGING_ANNOTATION table, attributes
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such as the labeling plane and area were harmoniously mapped
to the standard CDM concept IDs. In instances where mapping
to standard CDM codes proved challenging, custom concepts
were designated without limiting the classes and domains. Here,the original data extracted from the images were consistently
included in the source-value column to ensure data fidelity.
Additionally, in anticipation of RadLex potentially being
adopted as a standard vocabulary within the OHDSI framework,
we have also suggested additional RadLex mappings for our
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Ji et aldata set. RadLex, developed by the Radiological Society ofNorth America, is a comprehensive terminology systemdesigned to standardize the names of radiological diagnoses,
findings, and procedures. It encompasses a wide range of terms
used in medical imaging, making it an invaluable resource forenhancing the comprehensiveness of medical imaging
vocabularies within our data set. Multimedia Appendix 2
provides a detailed map of the standard terminology used in our
I-CDM with their corresponding OMOP CDM concept IDs.

Validation of I-CDM for Lung Cancer Studies

We expanded our methodological framework by developing a
Python-based tool, publicly available on GitHub, for automaticconversion and integration of DICOM files into our I-CDM[27]. This tool was designed to work in conjunction with
PostgreSQL to effectively create and populate essential tables
such as IMAGING_STUDY, IMAGING_SERIES, andFILEPATH directly from specified DICOM file directories.
Notably, the tool includes an algorithm to map the extracted
DICOM header data systematically to the corresponding CDM
concept IDs. This functionality ensures that the medical image
data are not only accurately integrated into the I-CDM but alsoaligned with the standardized terminologies and classificationsof the OMOP CDM. For practical applications, we chose the
NSCLC-Radiomics open data set from The Cancer Imaging
Archive. The NSCLC-Radiomics data set was used solely andonly for the purpose of testing our DICOM file–to-PostgreSQL
conversion tool, confirming its functionality with generic
DICOM files, and providing a publicly shareable example ofthe processed output. After reading DICOM files from the
NSCLC-Radiomics data set, our tool methodically constructs
I-CDM tables within PostgreSQL, thereby streamlining the data
integration process.

Scenario 1: Association of Hypertension on Imaging
Frequency in Patients With Epidermal Growth Factor
Receptor Mutation-Positive Lung Cancer Receiving
Osimertinib

Data Preprocessing for I-CDM Table Construction

This scenario investigates the number of CT series, eachconsisting of more than 150 image instances, in patients who
have undergone low-dose CT for lung cancer diagnosis. This
study aimed to evaluate the adequacy of low-dose CT for
providing diagnostic information while minimizing radiation
exposure in patients.

Before data preprocessing, all personal identifiers were removed
to maintain patient confidentiality and protect personal
information. Our first step was to ID and categorize the images
into a series, ensuring that each series-specific folder contained
only pertinent images, thereby maintaining a hierarchicaldirectory structure. The Series Description in a DICOM header
often comprises terms and abbreviations that describe the image
characteristics. We performed a detailed analysis of the Series
Descriptions of selected images to discern imaging attributes,
such as the image plane, the presence of enhanced contrast, anddesignations such as low-dose, T1, or T2, among others. Basedon a combination of these criteria, we designed rule-based
naming conventions for folders, aiming for descriptive andmeaningful names. Furthermore, we extracted information on
the presence of “Black Blood” imaging in magnetic resonanceimaging (MRI) scans using DICOM header data, which assisted
in preparing data for the construction of the imaging series table.
To construct the imaging annotation table, 2 radiology experts
identified and labeled the lesions on the chest computed
tomography (CT) and brain MRI scans. Subsequently, we
extracted metadata related to the labeled regions, such as area
dimensions and characteristics.

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Overview
This study sought to define a research cohort comprising patients
aged 18 years and older with primary lung cancer and structure
the metadata of all chest x-ray, chest CT, and brain MRI images
using I-CDM. Using the structured data, we aimed to elucidate
the unique and major characteristics of patients with lung cancer
by analyzing various scenarios.

We investigated the association of hypertension on imaging
frequency in patients with lung cancer who were prescribed
osimertinib, an epidermal growth factor receptor tyrosine kinase
inhibitor. By comparing the frequency of CT imaging between
groups with and without hypertension, this study aimed todetermine whether the presence of hypertension affects imaging
frequency in patients undergoing osimertinib treatment.

Scenario 2: Correlation Between Ground-Glass Nodules
and Solid Tumor Volume in Lung Cancer
Using annotated data from chest CT scans to compare tumor
volumes in patients with lung cancer who have ground-glass
nodules (GGNs) with those who have solid nodules, we aimed
to explore the relationship between ground-glass opacity nodules
and tumor volume in lung cancer.

Scenario 3: Use of Low-Dose CT in Diagnostic Imaging
for Lung Cancer

Scenario 4: Number of Enhanced T1-Weighted MRI
Images With a Slice Thickness of <1 mm in Patients
With Lung Cancer Diagnosed at Younger Than 60 Years
This scenario targets patients with lung cancer diagnosed atyounger than 60 years of age and involves quantifying thenumber of enhanced T1-weighted MRI images with a slice
thickness of <1 mm. The collected data highlighted the volumeof images available for subsequent annotation, and facilitatedan in-depth radiological analysis of patient demographics.

Ethical Considerations
This study was approved by the Institutional Review Board ofSeoul National University Bundang Hospital (approval
B-2202-738-004; date: April 14, 2022). All procedures
performed in this study involving human participants were inaccordance with the ethical standards of the institutional and/or
national research committee and with the 1964 Helsinki
declaration and its later amendments or comparable ethicalstandards.

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Ji et alStatistical Analysis
Statistical analyses were performed using R software (version
4.2.2; The R Foundation for Statistical Computing). Descriptive
statistics were used to summarize the data, including the
calculation of means for continuous variables and frequency
counts for categorical variables. The data were categorized asnecessary to facilitate further analysis.

Results
Conversion of DICOM Data to the OMOP CDM
Format: Realization and Integration in the I-CDM
Framework
To systematically organize and efficiently manage the extensive
collection of imaging data for the cohort of patients with lungcancer diagnosed between 2010 and 2017, sourced at Seoul
National University Bundang Hospital, we structured the I-CDM
into 4 basic tables: IMAGING_STUDY, IMAGING_SERIES,IMAGING_ANNOTATION, and FILEPATH (Table 1). The
data set included imaging data from follow-ups in 2003-2021.
The I-CDM categorized 282,098 IMAGING_STUDY records,
which were systematically linked to OMOP. This link provides
an extensive overview of patient imaging trajectories and clinicaldata. The database contains 5,674,425 image series
encompassing 47,381,027 individual image instances. Of the
282,098 records in the data set, 282,028 records contained
information across various modalities, each containing acorresponding “number of series and instance” columns within
the database.

Table 1. Imaging Common Data Model (I-CDM) data summary for lung cancer cohort (number of tables and records for data from 2003 to 2021 forpatients diagnosed with lung cancer from 2010 to 2017).
I-CDM table or column nameRecord count, nRecords with data, n (%)

Unique values

IMAGING_STUDY

282,098

Modality

282,098

282,028 (99.9)

3

Manufacturer

282,098

281,940 (99.9)

265

Number of series

282,098

282,098 (100)35

Number of instance

282,098

282,098 (100)1825

IMAGING_SERIESa

5,674,425

Body part examined

382,517382,517 (100)25

Laterality

85,11885,118 (100)3

Slice thickness

411,351

411,351 (100)1099

Series description

654,247

635,208 (100)12,535

Window center

685,526

685,526 (100)29,815

Window width

685,848

685,848 (100)31,393

Patient position

458,770

444,770 (100)11Columns

717,154

717,154 (100)

2020

Rows

717,154

717,154 (100)2066

Number of instance

717,169

717,169 (100)626

BBa/non-BB

12,943

12,943 (100)2

IMAGING_ANNOTATION

48,536

Annotation system

48,536

48,536 (100)5Annotation text

3013

2689 (100)69

Volume11,153

11,153 (100)3298Long axis

31,35331,333 (100)159

Surface3009

3009 (100)2492

FILEPATH

1,000,361

File path

1,000,361

1,000,361 (100)998,844

File size

1,000,361

1,000,361 (100)681,026

BB: blood-brain barrier contrast.

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Ji et alThe IMAGING_SERIES data represents a testament to the scaleand complexity of the data set, with the 5,674,425 series
illustrating the vast range of radiological examinations includedin this framework. A total of 382,517 records detailing theexamined body parts underscored the targeted nature ofradiological diagnostics. The data set was also characterized byan array of parameters, with 685,526 records for window centersand 685,848 for window widths. The structural resolution was
meticulously captured with 717,154 data points for both thecolumns and rows, reflecting the intricate images. Each series
was contextualized with descriptions recording the purpose andcontext of the imaging sequence in 654,247 data records. In
total, 12,943 images were classified into blood-brain barrier
contrast (BB)/non-BB categories to indicate the presence orabsence of BB, highlighting the usefulness of specialized
imaging sequences for detailed neurovascular assessments.
Table 2 displays the distribution of DICOM data across different
modalities for patients with lung cancer, indicating the numberof studies and series for each modality. X-ray examinations
usually consist of a single series, whereas MRI and CT scans
frequently include multiple series per study to accommodate avariety of imaging sequences. This highlights the detailed andcomplex nature of lung cancer diagnosis and monitoring. The
structured categorization within the IMAGING_SERIES tableusing an EAV model, where the MRI data comprise 11
categories, including the BB/non-BB distinction. This implies
a detailed classification of the MRI data, unlike the x-ray data,
which are classified into 7 categories representing the variabilityof the desired parameters for each imaging modality. For x-ray
images (n=1,410,844), the features are distributed as follows:
number of instances (n=201,755, 14.3%), columns (n=201,755,
14.3%), rows (n=201,755, 14.3%), window width (n=200,938,
14.2%), window center (n=201,755, 14.3%), series description(n=201,755, 14.3%), patient position (n=200,938, 14.2%), slice
thickness (n=200,938, 14.2%), body part examined (n=201,755,

14.3%), laterality (n=201,755, 14.3%), and BB_NonBB(n=201,755, 14.3%).
CT images (n=2,982,176) show the following feature
distribution: number of instances (n=360,845, 12.1%), columns(n=360,845, 12.1%), rows (n=360,845, 12.1%), window width(n=360,845, 12.1%), window center (n=360,845, 12.1%), series
description (n=360,845, 12.1%), patient position (n=360,845,
12.1%), slice thickness (n=360,845, 12.1%), body part examined(n=360,845, 12.1%), laterality (n=286,281, 9.6%), and
BB_NonBB (n=116,305, 3.9%).
For MRI images (n=1,281,432), the distribution is: number of
instances (n=140,958, 11%), columns (n=140,958, 11%), rows
(n=140,958, 11%), window width (n=138,395, 10.8%), window
center (n=138,395, 10.8%), series description (n=139,686,
10.9%), patient position (n=142,235, 11.1%), slice thickness(n=142,235, 11.1%), body part examined (n=142,235, 11.1%),
laterality (n=80,733, 6.3%), and BB_NonBB (n=91,984, 7.1%).
The IMAGING_ANNOTATION table had 48,536 annotations
predominantly sourced from CT and MRI scans. These
annotations offer a detailed exploration of the examined regions,with volumetric and long-axis measurements documented. This
granular level of detail is critical for the precise characterizationand monitoring of diseases supported by a comprehensive
understanding of the annotated regions. The FILETATH records
within the I-CDM table (totaling 1,000,361) served as a bridge
between the CDM tables and actual image file paths, spanningapproximately 9.6 terabytes of image data. This illustrates not
only the substantial volume of image data but also the expansive
nature of our image repository within the I-CDM framework.
In addition, DICOM folders were organized in a series, andcommon imaging characteristics were identified through series
descriptions to assign meaningful folder names, further
streamlining the data structure for efficient management andretrieval.

Table 2. Count data based on modality and type.

a

Modality

Study countSeries count

X-ray

201,569

201,770CTa

65,923373,116

MRIb

14,590142,254

CT: computed tomography.

b

MRI: magnetic resonance imaging.

Validation of I-CDM Scenarios for Enhanced Imaging
and Treatment Classification in Patients With Lung
CancerScenario 1: Hypertension and Imaging Frequency inPatients Treated With Osimertinib
Among the total cohort of 7842 patients with lung cancer, 176(2.24% of the total) prescribed osimertinib were diagnosed withhttps://medinform.jmir.org/2024/1/e59187

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hypertension. In the osimertinib arm, 28 (0.36%) patients had
hypertension. The average number of CT scans in patients withand without hypertension was 19.5 and 20.3, respectively,
indicating that hypertension did not significantly affect thefrequency of CT imaging in patients with lung cancer receiving
osimertinib treatment. Figure 3 shows this comparison of CT
scan frequency over hypertension status among patients treatedwith osimertinib.

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Ji et alFigure 3. Diagram of CT scan frequency comparison according to hypertension status among osimertinib-treated patients with lung cancer. CT:
computed tomography.

Scenario 2: Nodule Characterization and Volume
Measurement in CT Imaging
We evaluated 1947 annotated CT scans from 1929 patients andobserved that a significant number of GGNs contained solid
components, necessitating labeling of both components within
the GGNs. Our comparative analysis focused on the mean
volume of solid nodules within GGNs compared with those
without GGNs. In total, 673 GGNs were identified in 626
patients, of which 649 were classified as having solid nodules.
The average volume of the GGN was 8135.616 mm3, whereas
the volume of solid nodules within the GGN was 2578.006
mm3. In contrast, 1343 solid nodules without GGN were foundin 1319 patients, with an average volume of 34,712.58 mm3.
This corroborates the findings of previous studies indicatingthat solid nodules, especially those not associated with GGNs,
tended to be more abundant [28]. Our results provide additional
evidence supporting these observations on the nodal nature oflung pathologies.

Scenario 3: Use of Low-Dose CT and Instance Range
Among the 63,446 CT studies conducted in the cohort of patientswith lung cancer, which included 2,725,899 series, 48,587 wereidentified as low-dose imaging studies. Of these, 41,336
included >150 instances. The instance count in the 3 low-dose
CT images varied significantly, with the smallest and largest
series consisting of three and 633 instances, respectively. Thishighlights the need for low-dose imaging to capture extensive
data while minimizing radiation exposure.

Scenario 4: MRI Imaging With 1-mm Thick Slice and
T1 Enhancement
In Scenario 4, which focused on MRIs of 1-mm thick slices
under T1 enhancement, our analysis of 137,566 MRI series
identified 31,851 series using T1-weighting at the specified
slice thickness, 5235 of which were associated with patients
aged ≤60 years. This scenario allows the preemptive
examination of images to be labeled, integrating image patterns
and nodule characteristics with clinical data. This approach not
only facilitates the identification of the scale of target images
to be annotated but also enables precise quantification of theimages that meet the specified criteria. Table 3 shows thecategorization of the MRI series using I-CDM, providing avisual summary of the data refined by slice thickness and T1
enhancement and further filtered to include patients aged ≤60
years within the studied patient cohort.

Table 3. Magnetic resonance imaging series analysis using Imaging Common Data Model.

a

MRIa series typeCount

Total MRI series

137,566

T1 weighted

74,692Contrast enhanced

51,582

Slice thickness ≤1 mm

31,851Age of 60 years or younger

5235

MRI: magnetic resonance imaging.

I-CDM Data Quality Check
We ensured DQ based on a set of 44 comprehensive DQ rules(outlined in Multimedia Appendix 3), focusing on the Radiation
CDM quality assurance framework. These rules encompassed
a broad spectrum of checks, including the evaluation of DICOM
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tables within the CDM. All data entries successfully met thesecriteria, indicating compliance with quality standards. Amongthese rules, those pertaining to interdata relationships and outlier
detection were instrumental in validating the integrity of thedata set. A selection of these quality checks and their outcomesare presented in Table 4, highlighting the importance of ensuring
data quality and integrity within I-CDM.

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Ji et alTable 4. Selected data quality assurance rules and outlier analysis results from Multimedia Appendix 3.
CDM_TABLE, CONTCEPT_NAME, and check description

Threshold

Result (%)

Error (n)PASS
(99.9)

202

PASS(99.9)

109

PASS(99.9)

1

At least 99% of records
must have a nonmissing

PASS(99.8)

496

VALUE_AS_NUMBER must exist and be a numeric value for at least 99% of therecords

At least 99% of records
must have a nonmissing

PASS
(100)

100

Outliers, defined as values beyond the 1st and 99th percentiles, should be reviewed

Outliers should be under PASS (1.2) 8952
5%

IMAGING_STUDYNUMBER_OF_SERIESThe NUMBER_OF_SERIES must be equal to the number of series in the IMAGAt least 95% match
ING_SERIES table with the same IMAGING_STUDY_ID. This ensures that the
number of series recorded in the IMAGING_STUDY matches the actual series entries
in the related table
NUMBER_OF_INSTANCEThe NUMBER_OF_INSTANCE must equal the sum of VALUE_AS_NUMBER for At least 95% match
entries in the IMAGING_SERIES table where SERIES_CONCEPT_ID equals
NUMBER_OF_INSTANCE, under the condition that they are mapped between the
2 tables. This is to verify that the number of instances (images) reported in the
IMAGING_STUDY corresponds to the aggregated count of instances from the series
data
NUMBER_OF_SERIES, NUMBER_OF_INSTANCE
The presence of a Rule of NUMBER_OF_SERIES necessitates the presence of a Rule At least 95% matchof NUMBER_OF_INSTANCE.
IMAGING_SERIESSERIES_CONCEPT_ID = SliceThickness
VALUE_AS_NUMBER must exist and be a numeric value for at least 99% of therecords
SERIES_CONCEPT_ID = Rows

SERIES_CONCEPT_ID = Columns
PASS
(100)

—a

VALUE_AS_NUMBER must exist and be a numeric value for at least 99% of therecords

At least 99% of records
must have a nonmissing

Outliers, defined as values beyond the 1st and 99th percentiles, should be reviewed

Outliers should be under PASS (1.0) 7251
5%

SERIES_CONCEPT_ID = BB/non-BB
Values must be exclusively “Positive” or “Negative”, ensuring they represent these
specific states without including the concept IDs 45884084 and 45878583

100% of records must
PASS
have as one of the speci- (100)fied valid IDs—

VALUE_AS_NUMBER must exist and be a numeric value

No missing values for
PASS
VALUE_AS_NUMBER (100)—Outliers, defined as values beyond the 1st and 99th percentiles, should be identified
and reviewed to ensure they accurately reflect the intended measurements.

Outliers should be under PASS (0.2) 77
5%

IMAGING_ANNOTATION
ANNOTATION_ CONCEPT_ID = Long axis

ANNOTATION_ CONCEPT_ID = Volume
VALUE_AS_NUMBER must exist and be a numeric value

100% of records must be PASS
numeric and nonnull
(100)

—

100% of records must be PASS
numeric and nonnull
(100)

—

100% of records must be PASS
numeric and nonnull
(100)

—

ANNOTATION_ CONCEPT_ID = annotation_text
VALUE_SOURCE_VALUE must contain a nonempty text value
ANNOTATION_ CONCEPT_ID = surface area
VALUE_AS_NUMBER must exist and be a numeric value

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JMIR MEDICAL INFORMATICSaJi et alNot applicable.

Discussion
Principal Results
This study proposes a method for integrating clinical andimaging data using I-CDM. By converting DICOM data into
the OMOP CDM format and integrating it into the I-CDM
framework, we implemented a systematic approach to efficiently
manage medical imaging data. This approach enabled theconnection and analysis of clinical and imaging data in different
contexts. Additionally, detailed schema overviews for the use
cases of integrating I-CDM can be found in Multimedia
Appendix 4, and a comparison between MI-CDM and I-CDM
is provided in Multimedia Appendix 5.

Limitations
The limitation of this study is its lack of consideration for the
resources required for processing DICOM images andintegrating annotation information. To customize and add dataaccording to researcher needs using the EAV model,
comprehensive knowledge and expertise on DICOM standards
and tags are required [29]. Furthermore, integrating image
annotation data within the I-CDM framework not only demands
sufficient resources [30-32], but also requires advanced data
management strategies to expand the integration andharmonization of data sets from various imaging modalities
beyond chest CT, x-ray, and brain MRI [33,34]. Moreover,focusing exclusively on a cohort of patients with lung cancer
narrowed the scope of the study. Additionally, in our study, thescenarios were designed to validate the functionality of the
proposed model using actual medical data. These scenarios weredeliberately simplified to ensure effective management within
the capabilities of the implemented I-CDM framework. Future
research will benefit from the incorporation of expanded
annotation data, enabling more complex analyses, such as
longitudinal comparisons of tumor sizes pre and posttreatment
in individual patients.

Comparison With Prior WorkIn this study, diverging from previous Radiology-CDM research,
we refined the integration of imaging examination data bylinking them with the PROCEDURE_OCCURRENCE table,
which enables a more efficient analysis through improved data
connectivity. Moreover, unlike previous research that relied on
RadLex for standard terminology, this study directly mapped
DICOM terms to OMOP CDM standard terminologies. This
direct mapping simplifies the process and enables the use ofcustom codes, thus facilitating a deeper analytical integrationof clinical and imaging data. And this study takes a distinct
approach compared with recent I-CDM studies [35,36]. Our
method enhances the analytical scope by facilitating the storageand management of annotation information. This ensures thatimaging-related data, including annotations, can becomprehensively managed within the I-CDM framework. By
using the EAV model, our study introduced flexibility inmanaging various data types and structures, rendering ourapproach adaptable to evolving research needs and datacharacteristics. Consequently, it exhibits good flexibility andhttps://medinform.jmir.org/2024/1/e59187

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adaptability, especially in research requiring integrated analysisof clinical and imaging data. Considering the file sizes
associated with imaging data, effective file management isessential. Our study used the FILEPATH table to connect
I-CDM with the original imaging data, including file extension
and size information, to ensure quick access to file details andfacilitate efficient management.

Scalability and Applicability of the I-CDM
The lung cancer cohort in this study was initially used to validate
the functionality of the proposed I-CDM tables using actual
medical image data. In future studies, the model is not only
adaptable to lung cancer but also designed to accommodate awider spectrum of medical conditions, including various tumor
types and cardiovascular diseases. By leveraging OMOP CDM’s
standard vocabulary for “modality_concept_id” and “body partexamined” (“value_as_concept_id”), the model can be broadly
adapted to accommodate various diseases or different settings
beyond lung cancer. This adaptability ensures that any additional
data items users might require can be seamlessly integrated byaligning with OMOP CDM standard vocabulary, underlining
the framework’s potential for broad application across diverse
medical data and settings. Furthermore, while RadLex is
extensively used in medical imaging vocabularies, it is not yet
included as a standard vocabulary in the OHDSI framework.
Even if RadLex were incorporated, it would not cover allconcepts related to imaging. Therefore, we had to consider
various vocabularies to ensure comprehensive coverage.
Recognizing this, we aimed to build the I-CDM by maximizing
the use of existing OHDSI vocabularies according to OHDSI
principles, rather than proposing new vocabularies. We haveproactively suggested mapping terms compatible with RadLex
within our study wherever possible. In the new scenario, theprinciple for term selection involves mapping the standard
concept to the granularity level of the source data. This isachieved by selecting the term from the standard vocabularythat most accurately represents the clinical meaning. In addition
to the features we have currently mapped, our study focused onlung cancer, but for other diseases, there are important concepts
in imaging that should be considered. For instance:•••

Ultrasound image tags: Commonly used tags include“Transducer Frequency,” “Gain,” and “Depth of Field,”which are critical for analyzing the quality andcharacteristics of ultrasound images.
Spine x-ray tags: Relevant tags such as “KVP” (Kilovoltage
Peak), “Exposure Time,” and “Focal Spot Size” are essential
for understanding the technical parameters that affect image
quality.
Body part imaging concepts: Terms like “Entire Thorax,”“Entire Liver,” and “Entire Pelvis” are crucial for precisely
describing the anatomical region being imaged, which canvary significantly depending on the disease or condition
being studied.

These examples ensure that the I-CDM framework is adaptable
and capable of integrating a wide range of imaging datacharacteristics and supporting diverse medical conditions andresearch scenarios.
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JMIR MEDICAL INFORMATICS

Conclusions
This study implemented a systematic approach for the efficientmanagement of medical imaging data, achieving integration ofclinical and imaging data through the development of the I-CDM
framework and the conversion of DICOM data into the OMOPCDM format. Future efforts should strive to broaden theapplication of the I-CDM framework to encompass various

Ji et aldisease populations and include diverse imaging techniques fordifferent body parts, such as abdominal CT, spine MRI, andliver MRI, thereby enhancing its applicability. Expanding its
scope to incorporate these imaging modalities is crucial for
conducting more comprehensive investigations into the utilityof merging clinical and imaging data across different healthconditions.

Acknowledgments
This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry
Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant HI22C0471).

Conflicts of Interest
None declared.

Multimedia Appendix 1
Imaging Common Data Model table.
[DOCX File , 18 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Imaging Common Data Model mapping Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)
concept ID.
[DOCX File , 22 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Data quality check rule and result.
[DOCX File , 21 KB-Multimedia Appendix 3]

Multimedia Appendix 4
Use case for integrating Imagining Common Data Model: detailed schema overview.
[PNG File , 198 KB-Multimedia Appendix 4]

Multimedia Appendix 5
Comparison of Medical Imaging Common Data Model and Imaging Common Data Model.
[DOCX File , 17 KB-Multimedia Appendix 5]

References
1.
2.
3.

4.

5.

6.

Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat
Med. Jan 2019;25(1):24-29. [FREE Full text] [doi: 10.1038/s41591-018-0316-z] [Medline: 30617335]
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-731. [doi:10.1038/s41551-018-0305-z] [Medline: 31015651]
Rho MJ, Kim SR, Park SH, Jang KS, Park BJ, Hong JY, et al. Common data model for decision support system of adverse
drug reaction to extract knowledge from multi-center database. Inf Technol Manag. Jul 3, 2015;17(1):57-66. [doi:10.1007/s10799-015-0240-6]
Voss EA, Makadia R, Matcho A, Ma Q, Knoll C, Schuemie M, et al. Feasibility and utility of applications of the common
data model to multiple, disparate observational health databases. J Am Med Inform Assoc. May 2015;22(3):553-564. [FREEFull text] [doi: 10.1093/jamia/ocu023] [Medline: 25670757]
Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, et al. Observational health data sciences and informatics(OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574-578. [FREE Full text][Medline: 26262116]
Lim JE, Kim HM, Kim JH, Baek HS, Han MY. Association between dyslipidemia and asthma in children: a systematic
review and multicenter cohort study using a common data model. Clin Exp Pediatr. Aug 2023;66(8):357-365. [FREE Fulltext] [doi: 10.3345/cep.2023.00290] [Medline: 37321588]

https://medinform.jmir.org/2024/1/e59187

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e59187 | p. 11(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
7.

8.

9.

10.

11.

12.
13.
14.
15.

16.
17.

18.

19.

20.
21.

22.
23.

24.
25.

26.

27.
28.

Park CH, You SC, Jeon H, Jeong CW, Choi JW, Park RW. Development and validation of the radiology common data
model (R-CDM) for the international standardization of medical imaging data. Yonsei Med J. Jan 2022;63(Suppl):S74-S83.
[FREE Full text] [doi: 10.3349/ymj.2022.63.S74] [Medline: 35040608]
Kim TH, Noh SH, Kim YR, Lee CS, Kim JE, Jeong CW, et al. Development and validation of a management system anddataset quality assessment tool for the Radiology Common Data Model (R_CDM): a case study in liver disease. Int J Med
Inform. 2022;162:104759. [FREE Full text] [doi: 10.1016/j.ijmedinf.2022.104759] [Medline: 35390589]
Malik B, Jeon K, Alkasab T, Mallol P, You SC, Nagy P. Development of the medical imaging extension for OMOP-CDM.
Observational Health Data Sciences and Informatics. 2022. URL: https://www.ohdsi.org/2022showcase-26/ [accessed2024-06-28]
Praeta J, Scherer M, Smeets D. Application of the R-CDM extension to capture metadata and features extracted fromquantitative brain MRI and CT data. 2023. Presented at: The Fourth European OHDSI Symposium; July 1-3, 2023; Rotterdam,
The Netherlands.
Kalokyri V, Kondylakis H, Sfakianakis S, Nikiforaki K, Karatzanis I, Mazzetti S, et al. MI-common data model: extending
observational medical outcomes partnership-common data model (OMOP-CDM) for registering medical imaging metadataand subsequent curation processes. JCO Clin Cancer Inform. Sep 2023;7:e2300101. [FREE Full text] [doi:10.1200/CCI.23.00101] [Medline: 38061012]
Tsui DCC, Camidge DR, Rusthoven CG. Managing central nervous system spread of lung cancer: the state of the art. J
Clin Oncol. 2022;40(6):642-660. [doi: 10.1200/JCO.21.01715] [Medline: 34985937]
Eaton K. Lung Cancer: Translational and Emerging Therapies. New York, NY. CRC Press; 2007. :-278.
Mildenberger P, Eichelberg M, Martin E. Introduction to the DICOM standard. Eur Radiol. 2002;12(4):920-927. [doi:10.1007/s003300101100] [Medline: 11960249]
Bidgood WD, Horii SC, Prior FW, Van Syckle DE. Understanding and using DICOM, the data interchange standard forbiomedical imaging. J Am Med Inform Assoc. 1997;4(3):199-212. [FREE Full text] [doi: 10.1136/jamia.1997.0040199][Medline: 9147339]
Haripriya P, Porkodi R. A survey paper on data mining techniques and challenges in distributed DICOM. Int J Adv Res
Comput Commun Eng. 2016:741-747. [FREE Full text] [doi: 10.17148/IJARCCE.2016.53179]
Kamel P, Nagy P. Patient-centered radiology with FHIR: an introduction to the use of FHIR to offer radiology a clinicallyintegrated platform. J Digit Imaging. 2018;31(3):327-333. [FREE Full text] [doi: 10.1007/s10278-018-0087-6] [Medline:
29725963]
Tournier J, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: a fast, flexible and open software
framework for medical image processing and visualisation. Neuroimage. 2019;202:116137. [doi:10.1016/j.neuroimage.2019.116137] [Medline: 31473352]
Aiello M, Esposito G, Pagliari G, Borrelli P, Brancato V, Salvatore M. How does DICOM support big data management?
investigating its use in medical imaging community. Insights Imaging. 2021;12(1):164. [FREE Full text] [doi:10.1186/s13244-021-01081-8] [Medline: 34748101]
Onken M, Riesmeier J, Bennett A. Digital imaging and communications in medicine. In: Biomedical Image Processing.
Berlin, Heidelberg. Springer Berlin Heidelberg; 2010:427-454.
Lelong R, Soualmia L, Dahamna B, Griffon N, Darmoni SJ. Querying EHRs with a semantic and entity-oriented querylanguage. Stud Health Technol Inform. 2017;235:121-125. [FREE Full text] [doi: 10.3233/978-1-61499-753-5-121][Medline: 28423767]
Nadkarni P. QAV: querying entity-attribute-value metadata in a biomedical database. Comput Methods Programs Biomed.
Jun 1997;53(2):93-103. [FREE Full text] [doi: 10.1016/s0169-2607(97)01815-4] [Medline: 9186046]
Morozov S, Gombolevskiy V, Elizarov A, Gusev M, Novik V, Prokudaylo S, et al. A simplified cluster model and a tool
adapted for collaborative labeling of lung cancer CT scans. Comput Methods Programs Biomed. 2021;206:106111. [doi:10.1016/j.cmpb.2021.106111] [Medline: 33957377]
Diaz-Pinto A, Alle S, Nath V, Tang Y, Ihsani A, Asad M, et al. MONAI label: a framework for AI-assisted interactive
labeling of 3D medical images. Med Image Anal. 2024;95:103207. [doi: 10.1016/j.media.2024.103207] [Medline: 38776843]
Philbrick K, Weston A, Akkus Z, Kline TL, Korfiatis P, Sakinis T, et al. RIL-Contour: a medical imaging dataset annotation
tool for and with deep learning. J Digit Imaging. Aug 2019;32(4):571-581. [FREE Full text] [doi:10.1007/s10278-019-00232-0] [Medline: 31089974]
Lösel PD, van de Kamp T, Jayme A, Ershov A, Faragó T, Pichler O, et al. Introducing Biomedisa as an open-source online
platform for biomedical image segmentation. Nat Commun. Nov 04, 2020;11(1):5577. [doi: 10.1038/s41467-020-19303-w][Medline: 33149150]
Radiology Common Dada Model (CDM) framework on OMOP CDM. GitHub. URL: https://github.com/HIRC-SNUBH/
ImagingCDM [accessed 2024-06-12]
Chu Z, Zhang Y, Li WJ, Li Q, Zheng YN, Lv FJ. Primary solid lung cancerous nodules with different sizes: computed
tomography features and their variations. BMC Cancer. 2019;19(1):1060. [FREE Full text] [doi: 10.1186/s12885-019-6274-0][Medline: 31699047]

https://medinform.jmir.org/2024/1/e59187

XSL• FO
RenderX

Ji et alJMIR Med Inform 2024 | vol. 12 | e59187 | p. 12(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
29.
30.
31.
32.
33.
34.

35.

36.

Ji et alKhan O, Lim Choi Keung SN, Zhao L, Arvanitis TN. A hybrid EAV-relational model for consistent and scalable captureof clinical research data. Stud Health Technol Inform. 2014;202:32-35. [Medline: 25000008]
Gu Y, Shen M, Yang J, Yang G. Reliable label-efficient learning for biomedical image recognition. IEEE Trans Biomed
Eng. 2019;66(9):2423-2432. [doi: 10.1109/tbme.2018.2889915] [Medline: 30596566]
Wu Y, Zhou Z, Wu W. OneSeg: self-learning and one-shot learning based single-slice annotation for 3D medical image
segmentation. arXiv. Preprint posted online on September 24, 2023. [doi: 10.48550/arXiv.2309.13671]
Dimitrovski I, Kocev D, Loskovska S, Džeroski S. Hierarchical annotation of medical images. Pattern Recognit. Oct
2011;44(10-11):2436-2449. [doi: 10.1016/j.patcog.2011.03.026]
Martínez-García M, Hernández-Lemus E. Data integration challenges for machine learning in precision medicine. Front
Med (Lausanne). 2021;8:784455. [FREE Full text] [doi: 10.3389/fmed.2021.784455] [Medline: 35145977]
Parciak M, Suhr M, Schmidt C, Bönisch C, Löhnhardt B, Kesztyüs D, et al. FAIRness through automation: developmentof an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital. BMC
Med Inform Decis Mak. 2023;23(1):94. [FREE Full text] [doi: 10.1186/s12911-023-02195-3] [Medline: 37189148]
Kalokyri V, Kondylakis H, Sfakianakis S, Nikiforaki K, Karatzanis I, Mazzetti S, et al. MI-common data model: extending
observational medical outcomes partnership-common data model (OMOP-CDM) for registering medical imaging metadataand subsequent curation processes. JCO Clin Cancer Inform. 2023;7:e2300101. [FREE Full text] [doi: 10.1200/CCI.23.00101][Medline: 38061012]
Park W, Jeon K, Schmidt TS, Kondylakis H, Alkasab T, Dewey BE, et al. Development of medical imaging data
standardization for imaging-based observational research: OMOP common data model extension. J Imaging Inform Med.
2024;37(2):899-908. [doi: 10.1007/s10278-024-00982-6] [Medline: 38315345]

Abbreviations
BB: blood-brain barrier contrast
CDM: Common Data Model
CT: computed tomography
DICOM: Digital Imaging and Communications in Medicine
DQ: data quality
EAV: entity-attribute-value
FHIR: Fast Healthcare Interoperability Resources
GGN: ground-glass nodule
I-CDM: Imaging Common Data Model
MRI: magnetic resonance imaging
OHDSI: Observational Health Data Sciences and Informatics
OMOP: Observational Medical Outcomes Partnership

Edited by C Lovis; submitted 04.04.24; peer-reviewed by T Karen, C Jeong, R Kolde; comments to author 25.04.24; revised version
received 10.05.24; accepted 08.06.24; published 12.07.24
Please cite as:Ji H, Kim S, Sunwoo L, Jang S, Lee Ho-Young, Yoo S
Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes
Partnership Common Data Model Extension
JMIR Med Inform 2024;12:e59187
URL: https://medinform.jmir.org/2024/1/e59187
doi: 10.2196/59187
PMID:©Hyerim Ji, Seok Kim, Leonard Sunwoo, Sowon Jang, Ho-Young Lee, Sooyoung Yoo. Originally published in JMIR Medical
Informatics (https://medinform.jmir.org), 12.07.2024. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The
complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyrightand license information must be included.

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