Imaging Data Commons

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__

def _parts(version):
    # Compare numerically: "0.9.0" < "0.11.14" is False as a string comparison.
    return tuple(int(p) if p.isdigit() else 0 for p in version.split(".")[:3])

if _parts(installed) < _parts(REQUIRED_VERSION):
    print(f"Upgrading idc-index from {installed} to {REQUIRED_VERSION}...")
    import subprocess
    # Pin to the tested version — an unpinned upgrade installs whatever is
    # newest on PyPI, into system packages, and may still not satisfy the check.
    subprocess.run(
        ["pip3", "install", "--break-system-packages", f"idc-index=={REQUIRED_VERSION}"],
        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

Nine capability areas, each with worked code, are documented in references/core_capabilities.md:

1. Data discovery and exploration — summary statistics, and enumerating the actual Modality and BodyPartExamined values before filtering on them.
2. Querying metadata with SQL — against index, collections_index, and analysis_results_index, returning pandas DataFrames.
3. Downloading DICOM files — Python and CLI, by collection, series UID, or manifest, with control over directory hierarchy (full, simplified, or flat).
4. Visualizing IDC images — single series or a whole study in the OHIF viewer.
5. Licenses and citations — per-collection license checks, and citations in APA or BibTeX.
6. Batch processing and filtering — scanner- and protocol-level filters, manifests, and batched downloads that avoid timeouts.
7. Advanced BigQuery queries — for joins and aggregations beyond the index API.
8. Tool selection guide — which access path fits which task.
9. Integration with analysis pipelines — reading series with pydicom, processing with SimpleITK, and converting to NIfTI.

Always explore the real column values first: filtering on a guessed Modality or BodyPartExamined string is the most common cause of an empty result set.

Common Use Cases

See references/use_cases.md for complete end-to-end workflow examples including:

• Building deep learning training datasets from lung CT scans
• Comparing image quality across scanner manufacturers
• Previewing data in browser before downloading
• License-aware batch downloads for commercial use

Best Practices

• Verify IDC version before generating responses - Always call client.get_idc_version() at the start of a session to confirm you're using the expected data version (currently v23). If using an older version, recommend pip install --upgrade idc-index
• Check licenses before use - Always query the license_short_name field and respect licensing terms (CC BY vs CC BY-NC)
• Generate citations for attribution - Use citations_from_selection() to get properly formatted citations from source_DOI values; include these in publications
• Start with small queries - Use LIMIT clause when exploring to avoid long downloads and understand data structure
• Use mini-index for simple queries - Only use BigQuery when you need comprehensive metadata or complex JOINs
• Organize downloads with dirTemplate - Use meaningful directory structures like %collection_id/%PatientID/%Modality
• Cache query results - Save DataFrames to CSV files to avoid re-querying and ensure reproducibility
• Estimate size first - Check collection size before downloading - some collection sizes are in terabytes!
• Save manifests - Always save query results with Series UIDs for reproducibility and data provenance
• Read documentation - IDC data structure and metadata fields are documented at https://learn.canceridc.dev/
• Use IDC forum - Search for questions/answers and ask your questions to the IDC maintainers and users at https://discourse.canceridc.dev/

Troubleshooting

Issue: ModuleNotFoundError: No module named 'idc_index'

• Cause: idc-index package not installed
• Solution: Install with pip install --upgrade idc-index

Issue: Download fails with connection timeout

• Cause: Network instability or large download size
• Solution:
  • Download smaller batches (e.g., 10-20 series at a time)
  • Check network connection
  • Use dirTemplate to organize downloads by batch
  • Implement retry logic with delays

Issue: BigQuery quota exceeded or billing errors

• Cause: BigQuery requires billing-enabled GCP project
• Solution: Use idc-index mini-index for simple queries (no billing required), or see references/bigquery_guide.md for cost optimization tips

Issue: Series UID not found or no data returned

• Cause: Typo in UID, data not in current IDC version, or wrong field name
• Solution:
  • Check if data is in current IDC version (some old data may be deprecated)
  • Use LIMIT 5 to test query first
  • Check field names against metadata schema documentation

Issue: Downloaded DICOM files won't open

• Cause: Corrupted download or incompatible viewer
• Solution:
  • Check DICOM object type (Modality and SOPClassUID attributes) - some object types require specialized tools
  • Verify file integrity (check file sizes)
  • Use pydicom to validate: pydicom.dcmread(file, force=True)
  • Try different DICOM viewer (3D Slicer, Horos, RadiAnt, QuPath)
  • Re-download the series

Common SQL Query Patterns

See references/sql_patterns.md for quick-reference SQL patterns including:

• Filter value discovery (modalities, body parts, manufacturers)
• Annotation and segmentation queries (including seg_index, ann_index joins)
• Slide microscopy queries (sm_index patterns)
• Download size estimation
• Clinical data linking

For segmentation and annotation details, also see references/digital_pathology_guide.md.

Related Skills

The following skills complement IDC workflows for downstream analysis and visualization:

DICOM Processing

• pydicom - Read, write, and manipulate downloaded DICOM files. Use for extracting pixel data, reading metadata, anonymization, and format conversion. Essential for working with IDC radiology data (CT, MR, PET).

Pathology and Slide Microscopy

See references/digital_pathology_guide.md for DICOM-compatible tools (highdicom, wsidicom, TIA-Toolbox, Slim viewer).

Metadata Visualization

• matplotlib - Low-level plotting for full customization. Use for creating static figures summarizing IDC query results (bar charts of modalities, histograms of series counts, etc.).
• seaborn - Statistical visualization with pandas integration. Use for quick exploration of IDC metadata distributions, relationships between variables, and categorical comparisons with attractive defaults.
• plotly - Interactive visualization. Use when you need hover info, zoom, and pan for exploring IDC metadata, or for creating web-embeddable dashboards of collection statistics.

Data Exploration

• exploratory-data-analysis - Comprehensive EDA on scientific data files. Use after downloading IDC data to understand file structure, quality, and characteristics before analysis.

Resources

Schema Reference (Primary Source)

Always use client.indices_overview for current column schemas. This ensures accuracy with the installed idc-index version:

# Get all column names and types for any table
schema = client.indices_overview["index"]["schema"]
columns = [(c['name'], c['type'], c.get('description', '')) for c in schema['columns']]

Reference Documentation

See the Quick Navigation section at the top for the full list of reference guides with decision triggers.

• indices_reference - External documentation for index tables (may be ahead of the installed version)

External Links

• IDC Portal: https://portal.imaging.datacommons.cancer.gov/explore/
• Documentation: https://learn.canceridc.dev/
• Tutorials: https://github.com/ImagingDataCommons/IDC-Tutorials
• User Forum: https://discourse.canceridc.dev/
• idc-index GitHub: https://github.com/ImagingDataCommons/idc-index
• Citation: Fedorov, A., et al. "National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence." RadioGraphics 43.12 (2023). https://doi.org/10.1148/rg.230180

Skill Updates

This skill version is available in skill metadata. To check for updates:

• Visit the releases page
• Watch the repository on GitHub (Watch → Custom → Releases)

Source: K-Dense-AI/scientific-agent-skills → skills/imaging-data-commons/SKILL.md