Imaging Data Commons

Overview

Use the idc-index Python package to query and download public cancer imaging data from the National Cancer Institute Imaging Data Commons (IDC). No authentication required for data access.

Current IDC Data Version: v23 (always verify with IDCClient().get_idc_version())

Primary tool: idc-index (GitHub)

CRITICAL - Check package version and upgrade if needed (run this FIRST):

import idc_index

REQUIRED_VERSION = "0.11.14"  # Must match metadata.idc-index in this file
installed = idc_index.__version__

if installed < REQUIRED_VERSION:
    print(f"Upgrading idc-index from {installed} to {REQUIRED_VERSION}...")
    import subprocess
    subprocess.run(["pip3", "install", "--upgrade", "--break-system-packages", "idc-index"], check=True)
    print("Upgrade complete. Restart Python to use new version.")
else:
    print(f"idc-index {installed} meets requirement ({REQUIRED_VERSION})")

Verify IDC data version and check current data scale:

from idc_index import IDCClient
client = IDCClient()

# Verify IDC data version (should be "v23")
print(f"IDC data version: {client.get_idc_version()}")

# Get collection count and total series
stats = client.sql_query("""
    SELECT
        COUNT(DISTINCT collection_id) as collections,
        COUNT(DISTINCT analysis_result_id) as analysis_results,
        COUNT(DISTINCT PatientID) as patients,
        COUNT(DISTINCT StudyInstanceUID) as studies,
        COUNT(DISTINCT SeriesInstanceUID) as series,
        SUM(instanceCount) as instances,
        SUM(series_size_MB)/1000000 as size_TB
    FROM index
""")
print(stats)

Core workflow:

1. Query metadata → client.sql_query()
2. Download DICOM files → client.download_from_selection()
3. Visualize in browser → client.get_viewer_URL(seriesInstanceUID=...)

When to Use This Skill

• Finding publicly available radiology (CT, MR, PET) or pathology (slide microscopy) images
• Selecting image subsets by cancer type, modality, anatomical site, or other metadata
• Downloading DICOM data from IDC
• Checking data licenses before use in research or commercial applications
• Visualizing medical images in a browser without local DICOM viewer software

Quick Navigation

Core Sections (inline):

• IDC Data Model - Collection and analysis result hierarchy
• Index Tables - Available tables and joining patterns
• Installation - Package setup and version verification
• Core Capabilities - Essential API patterns (query, download, visualize, license, citations, batch)
• Best Practices - Usage guidelines
• Troubleshooting - Common issues and solutions

Reference Guides (load on demand):

IDC Data Model

IDC adds two grouping levels above the standard DICOM hierarchy (Patient → Study → Series → Instance):

• collection_id: Groups patients by disease, modality, or research focus (e.g., tcga_luad, nlst). A patient belongs to exactly one collection.
• analysis_result_id: Identifies derived objects (segmentations, annotations, radiomics features) across one or more original collections.

Use collection_id to find original imaging data, may include annotations deposited along with the images; use analysis_result_id to find AI-generated or expert annotations.

Key identifiers for queries:

Index Tables

The idc-index package provides multiple metadata index tables, accessible via SQL or as pandas DataFrames.

Complete index table documentation: Use https://idc-index.readthedocs.io/en/latest/indices_reference.html for quick check of available tables and columns without executing any code.

Important: Use client.indices_overview to get current table descriptions and column schemas. This is the authoritative source for available columns and their types — always query it when writing SQL or exploring data structure.

Available Tables

Auto = loaded automatically when IDCClient() is instantiated fetch_index() = requires client.fetch_index("table_name") to load

Joining Tables

Key columns are not explicitly labeled, the following is a subset that can be used in joins.

Note: Subjects, Updated, and Description appear in multiple tables but have different meanings (counts vs identifiers, different update contexts).

For detailed join examples, schema discovery patterns, key columns reference, and DataFrame access, see references/index_tables_guide.md.

Clinical Data Access

# Fetch clinical index (also downloads clinical data tables)
client.fetch_index("clinical_index")

# Query clinical index to find available tables and their columns
tables = client.sql_query("SELECT DISTINCT table_name, column_label FROM clinical_index")

# Load a specific clinical table as DataFrame
clinical_df = client.get_clinical_table("table_name")

See references/clinical_data_guide.md for detailed workflows including value mapping patterns and joining clinical data with imaging.

Data Access Options

Cloud storage organization

IDC maintains all DICOM files in public cloud storage buckets mirrored between AWS S3 and Google Cloud Storage. Files are organized by CRDC UUIDs (not DICOM UIDs) to support versioning.

Files are stored as <crdc_series_uuid>/<crdc_instance_uuid>.dcm. Access is free (no egress fees) via AWS CLI, gsutil, or s5cmd with anonymous access. Use series_aws_url column from the index for S3 URLs; GCS uses the same path structure.

See references/cloud_storage_guide.md for bucket details, access commands, UUID mapping, and versioning.

DICOMweb access

IDC data is available via DICOMweb interface (Google Cloud Healthcare API implementation) for integration with PACS systems and DICOMweb-compatible tools.

See references/dicomweb_guide.md for endpoint URLs, code examples, supported operations, and implementation details.

Direct Parquet access

All idc-index metadata tables are published as Parquet files to a public GCS bucket (idc-index-data-artifacts) with unrestricted CORS. This enables DuckDB or pandas queries without installing idc-index, including cross-table joins and queries against volume_geometry_index and rtstruct_index.

See references/parquet_access_guide.md for URL patterns, available files, and DuckDB query examples.

Installation and Setup

Required (for basic access):

pip install --upgrade idc-index

Important: New IDC data release will always trigger a new version of idc-index. Always use --upgrade flag while installing, unless an older version is needed for reproducibility.

IMPORTANT: IDC data version v23 is current. Always verify your version:

print(client.get_idc_version())  # Should return "v23"

If you see an older version, upgrade with: pip install --upgrade idc-index

Tested with: idc-index 0.11.14 (IDC data version v23)

Optional (for data analysis):

pip install pandas numpy pydicom

Core Capabilities

1. Data Discovery and Exploration

Discover what imaging collections and data are available in IDC:

from idc_index import IDCClient

client = IDCClient()

# Get summary statistics from primary index
query = """
SELECT
  collection_id,
  COUNT(DISTINCT PatientID) as patients,
  COUNT(DISTINCT SeriesInstanceUID) as series,
  SUM(series_size_MB) as size_mb
FROM index
GROUP BY collection_id
ORDER BY patients DESC
"""
collections_summary = client.sql_query(query)

# For richer collection metadata, use collections_index
client.fetch_index("collections_index")
collections_info = client.sql_query("""
    SELECT collection_id, CancerTypes, TumorLocations, Species, Subjects, SupportingData
    FROM collections_index
""")

# For analysis results (annotations, segmentations), use analysis_results_index
client.fetch_index("analysis_results_index")
analysis_info = client.sql_query("""
    SELECT analysis_result_id, analysis_result_title, Subjects, Collections, Modalities
    FROM analysis_results_index
""")

collections_index provides curated metadata per collection: cancer types, tumor locations, species, subject counts, and supporting data types — without needing to aggregate from the primary index.

analysis_results_index lists derived datasets (AI segmentations, expert annotations, radiomics features) with their source collections and modalities.

2. Querying Metadata with SQL

Query the IDC mini-index using SQL to find specific datasets.

First, explore available values for filter columns:

from idc_index import IDCClient

client = IDCClient()

# Check what Modality values exist
modalities = client.sql_query("""
    SELECT DISTINCT Modality, COUNT(*) as series_count
    FROM index
    GROUP BY Modality
    ORDER BY series_count DESC
""")
print(modalities)

# Check what BodyPartExamined values exist for MR modality
body_parts = client.sql_query("""
    SELECT DISTINCT BodyPartExamined, COUNT(*) as series_count
    FROM index
    WHERE Modality = 'MR' AND BodyPartExamined IS NOT NULL
    GROUP BY BodyPartExamined
    ORDER BY series_count DESC
    LIMIT 20
""")
print(body_parts)

Then query with validated filter values:

# Find breast MRI scans (use actual values from exploration above)
results = client.sql_query("""
    SELECT
      collection_id,
      PatientID,
      SeriesInstanceUID,
      Modality,
      SeriesDescription,
      license_short_name
    FROM index
    WHERE Modality = 'MR'
      AND BodyPartExamined = 'BREAST'
    LIMIT 20
""")

# Access results as pandas DataFrame
for idx, row in results.iterrows():
    print(f"Patient: {row['PatientID']}, Series: {row['SeriesInstanceUID']}")

To filter by cancer type, join with collections_index:

client.fetch_index("collections_index")
results = client.sql_query("""
    SELECT i.collection_id, i.PatientID, i.SeriesInstanceUID, i.Modality
    FROM index i
    JOIN collections_index c ON i.collection_id = c.collection_id
    WHERE c.CancerTypes LIKE '%Breast%'
      AND i.Modality = 'MR'
    LIMIT 20
""")

Available metadata fields (use client.indices_overview for complete list):

• Identifiers: collection_id, PatientID, StudyInstanceUID, SeriesInstanceUID
• Imaging: Modality, BodyPartExamined, Manufacturer, ManufacturerModelName
• Clinical: PatientAge, PatientSex, StudyDate
• Descriptions: StudyDescription, SeriesDescription
• Licensing: license_short_name

Note: Cancer type is in collections_index.CancerTypes, not in the primary index table.

3. Downloading DICOM Files

Download imaging data efficiently from IDC's cloud storage:

Download entire collection:

from idc_index import IDCClient

client = IDCClient()

# Download small collection (RIDER Pilot ~1GB)
client.download_from_selection(
    collection_id="rider_pilot",
    downloadDir="./data/rider"
)

Download specific series:

# First, query for series UIDs
series_df = client.sql_query("""
    SELECT SeriesInstanceUID
    FROM index
    WHERE Modality = 'CT'
      AND BodyPartExamined = 'CHEST'
      AND collection_id = 'nlst'
    LIMIT 5
""")

# Download only those series
client.download_from_selection(
    seriesInstanceUID=list(series_df['SeriesInstanceUID'].values),
    downloadDir="./data/lung_ct"
)

Custom directory structure:

Default dirTemplate: %collection_id/%PatientID/%StudyInstanceUID/%Modality_%SeriesInstanceUID

# Simplified hierarchy (omit StudyInstanceUID level)
client.download_from_selection(
    collection_id="tcga_luad",
    downloadDir="./data",
    dirTemplate="%collection_id/%PatientID/%Modality"
)
# Results in: ./data/tcga_luad/TCGA-05-4244/CT/

# Flat structure (all files in one directory)
client.download_from_selection(
    seriesInstanceUID=list(series_df['SeriesInstanceUID'].values),
    downloadDir="./data/flat",
    dirTemplate=""
)
# Results in: ./data/flat/*.dcm

Downloaded file names:

Individual DICOM files are named using their CRDC instance UUID: <crdc_instance_uuid>.dcm (e.g., 0d73f84e-70ae-4eeb-96a0-1c613b5d9229.dcm). This UUID-based naming:

• Enables version tracking (UUIDs change when file content changes)
• Matches cloud storage organization (s3://idc-open-data/<crdc_series_uuid>/<crdc_instance_uuid>.dcm)
• Differs from DICOM UIDs (SOPInstanceUID) which are preserved inside the file metadata

To identify files, use the crdc_instance_uuid column in queries or read DICOM metadata (SOPInstanceUID) from the files.

Command-Line Download

The idc download command provides command-line access to download functionality without writing Python code. Available after installing idc-index.

Auto-detects input type: manifest file path, or identifiers (collection_id, PatientID, StudyInstanceUID, SeriesInstanceUID, crdc_series_uuid).

# Download entire collection
idc download rider_pilot --download-dir ./data

# Download specific series by UID
idc download "1.3.6.1.4.1.9328.50.1.69736" --download-dir ./data

# Download multiple items (comma-separated)
idc download "tcga_luad,tcga_lusc" --download-dir ./data

# Download from manifest file (auto-detected)
idc download manifest.txt --download-dir ./data

Options:

Manifest files:

Manifest files contain S3 URLs (one per line) and can be:

• Exported from the IDC Portal after cohort selection
• Shared by collaborators for reproducible data access
• Generated programmatically from query results

Format (one S3 URL per line):

s3://idc-open-data/cb09464a-c5cc-4428-9339-d7fa87cfe837/*
s3://idc-open-data/88f3990d-bdef-49cd-9b2b-4787767240f2/*

Example: Generate manifest from Python query:

from idc_index import IDCClient

client = IDCClient()

# Query for series URLs
results = client.sql_query("""
    SELECT series_aws_url
    FROM index
    WHERE collection_id = 'rider_pilot' AND Modality = 'CT'
""")

# Save as manifest file
with open('ct_manifest.txt', 'w') as f:
    for url in results['series_aws_url']:
        f.write(url + '\n')

Then download:

idc download ct_manifest.txt --download-dir ./ct_data

4. Visualizing IDC Images

View DICOM data in browser without downloading:

from idc_index import IDCClient
import webbrowser

client = IDCClient()

# First query to get valid UIDs
results = client.sql_query("""
    SELECT SeriesInstanceUID, StudyInstanceUID
    FROM index
    WHERE collection_id = 'rider_pilot' AND Modality = 'CT'
    LIMIT 1
""")

# View single series
viewer_url = client.get_viewer_URL(seriesInstanceUID=results.iloc[0]['SeriesInstanceUID'])
webbrowser.open(viewer_url)

# View all series in a study (useful for multi-series exams like MRI protocols)
viewer_url = client.get_viewer_URL(studyInstanceUID=results.iloc[0]['StudyInstanceUID'])
webbrowser.open(viewer_url)

The method automatically selects OHIF v3 for radiology or SLIM for slide microscopy. Viewing by study is useful when a DICOM Study contains multiple Series (e.g., T1, T2, DWI sequences from a single MRI session).

5. Understanding and Checking Licenses

Check data licensing before use (critical for commercial applications):

from idc_index import IDCClient

client = IDCClient()

# Check licenses for all collections
query = """
SELECT DISTINCT
  collection_id,
  license_short_name,
  COUNT(DISTINCT SeriesInstanceUID) as series_count
FROM index
GROUP BY collection_id, license_short_name
ORDER BY collection_id
"""

licenses = client.sql_query(query)
print(licenses)

License types in IDC:

• CC BY 4.0 / CC BY 3.0 (~97% of data) - Allows commercial use with attribution
• CC BY-NC 4.0 / CC BY-NC 3.0 (~3% of data) - Non-commercial use only
• Custom licenses (rare) - Some collections have specific terms (e.g., NLM Terms and Conditions)

Important: Always check the license before using IDC data in publications or commercial applications. Each DICOM file is tagged with its specific license in metadata.

Generating Citations for Attribution

The source_DOI column contains DOIs linking to publications describing how the data was generated. To satisfy attribution requirements, use citations_from_selection() to generate properly formatted citations:

from idc_index import IDCClient

client = IDCClient()

# Get citations for a collection (APA format by default)
citations = client.citations_from_selection(collection_id="rider_pilot")
for citation in citations:
    print(citation)

# Get citations for specific series
results = client.sql_query("""
    SELECT SeriesInstanceUID FROM index
    WHERE collection_id = 'tc