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):

Guide	When to Load
index_tables_guide.md	Complex JOINs, schema discovery, DataFrame access
use_cases.md	End-to-end workflow examples (training datasets, batch downloads)
sql_patterns.md	Quick SQL patterns for filter discovery, annotations, size estimation
clinical_data_guide.md	Clinical/tabular data, imaging+clinical joins, value mapping
cloud_storage_guide.md	Direct S3/GCS access, versioning, UUID mapping
dicomweb_guide.md	DICOMweb endpoints, PACS integration
digital_pathology_guide.md	Slide microscopy (SM), annotations (ANN), pathology workflows
bigquery_guide.md	Full DICOM metadata, private elements (requires GCP)
cli_guide.md	Command-line tools (idc download, manifest files)
parquet_access_guide.md	Direct Parquet queries via GCS (no idc-index install needed)

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:

Identifier	Scope	Use for
collection_id	Dataset grouping	Filtering by project/study
PatientID	Patient	Grouping images by patient
StudyInstanceUID	DICOM study	Grouping of related series, visualization
SeriesInstanceUID	DICOM series	Grouping of related series, visualization

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

Table	Row Granularity	Loaded	Description
index	1 row = 1 DICOM series	Auto	Primary metadata for all current IDC data
prior_versions_index	1 row = 1 DICOM series	Auto	Series from previous IDC releases; for downloading deprecated data
collections_index	1 row = 1 collection	fetch_index()	Collection-level metadata and descriptions
analysis_results_index	1 row = 1 analysis result collection	fetch_index()	Metadata about derived datasets (annotations, segmentations)
clinical_index	1 row = 1 clinical data column	fetch_index()	Dictionary mapping clinical table columns to collections
sm_index	1 row = 1 slide microscopy series	fetch_index()	Slide Microscopy (pathology) series metadata
sm_instance_index	1 row = 1 slide microscopy instance	fetch_index()	Instance-level (SOPInstanceUID) metadata for slide microscopy
seg_index	1 row = 1 DICOM Segmentation series	fetch_index()	Segmentation metadata: algorithm, segment count, reference to source image series
ann_index	1 row = 1 DICOM ANN series	fetch_index()	Microscopy Bulk Simple Annotations series metadata; references annotated image series
ann_group_index	1 row = 1 annotation group	fetch_index()	Detailed annotation group metadata: graphic type, annotation count, property codes, algorithm
contrast_index	1 row = 1 series with contrast info	fetch_index()	Contrast agent metadata: agent name, ingredient, administration route (CT, MR, PT, XA, RF)
volume_geometry_index	1 row = 1 CT/MR/PT series	fetch_index()	3D volume geometry validation for single-frame CT, MR, and PT series; boolean checks for orientation, spacing, dimensions, and slice positions; composite regularly_spaced_3d_volume flag
rtstruct_index	1 row = 1 RTSTRUCT series	fetch_index()	RT Structure Set metadata: total ROI count, ROI names, generation algorithms, interpreted types, and the referenced image series UID

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.

Join Column	Tables	Use Case
collection_id	index, prior_versions_index, collections_index, clinical_index	Link series to collection metadata or clinical data
SeriesInstanceUID	index, prior_versions_index, sm_index, sm_instance_index	Link series across tables; connect to slide microscopy details
StudyInstanceUID	index, prior_versions_index	Link studies across current and historical data
PatientID	index, prior_versions