Imaging Data Commons

Overview

Use the

idc-index

Current IDC Data Version: v23 (always verify with

IDCClient().get_idc_version()

Primary tool:

idc-index

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

import idc_index

REQUIRED_VERSION = "0.11.10"  # 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):

index_tables_guide.md

use_cases.md

sql_patterns.md

clinical_data_guide.md

cloud_storage_guide.md

dicomweb_guide.md

digital_pathology_guide.md

bigquery_guide.md

cli_guide.md

idc download

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

analysis_result_id

Key identifiers for queries:

collection_id

PatientID

StudyInstanceUID

SeriesInstanceUID

Index Tables

The

idc-index

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

Available Tables

index

prior_versions_index

collections_index

analysis_results_index

clinical_index

sm_index

sm_instance_index

seg_index

ann_index

ann_group_index

contrast_index

Auto = loaded automatically when

IDCClient()

client.fetch_index("table_name")

Joining Tables

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

collection_id

SeriesInstanceUID

StudyInstanceUID

PatientID

analysis_result_id

source_DOI

crdc_series_uuid

Modality

SeriesInstanceUID

segmented_SeriesInstanceUID

referenced_SeriesInstanceUID

Note:

Subjects

Updated

Description

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

Data Access Options

idc-index

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.

idc-open-data

idc-open-data

idc-open-data-two

idc-open-idc1

idc-open-data-cr

idc-open-cr

Files are stored as

<crdc_series_uuid>/<crdc_instance_uuid>.dcm

series_aws_url

See

references/cloud_storage_guide.md

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

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

--upgrade

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 0.11.10 (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

analysis_results_index

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

• 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

index

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

0d73f84e-70ae-4eeb-96a0-1c613b5d9229.dcm

• 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

Command-Line Download

The

idc download

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:

--download-dir

--dir-template

%collection_id/%PatientID/%StudyInstanceUID/%Modality_%SeriesInstanceUID

--log-level

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

citations_from_selection()

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 = 'tcga_luad' LIMIT 5
""")
citations = client.citations_from_selection(
    seriesInstanceUID=list(results['SeriesInstanceUID'].values)
)

# Alternative format: BibTeX (for LaTeX documents)
bibtex_citations = client.citations_from_selection(
    collection_id="tcga_luad",
    citation_format=IDCClient.CITATION_FORMAT_BIBTEX
)

Parameters:

• collection_id

  : Filter by collection(s)

• patientId

  : Filter by patient ID(s)

• studyInstanceUID

  : Filter by study UID(s)

• seriesInstanceUID

  : Filter by series UID(s)

• citation_format

  : Use

  IDCClient.CITATION_FORMAT_*

  constants:

  • CITATION_FORMAT_APA

    (default) - APA style

  • CITATION_FORMAT_BIBTEX

    - BibTeX for LaTeX

  • CITATION_FORMAT_JSON

    - CSL JSON

  • CITATION_FORMAT_TURTLE

    - RDF Turtle

Best practice: When publishing results using IDC data, include the generated citations to properly attribute the data sources and satisfy license requirements.

6. Batch Processing and Filtering

Process large datasets efficiently with filtering:

from idc_index import IDCClient
import pandas as pd

client = IDCClient()

# Find chest CT scans from GE scanners
query = """
SELECT
  SeriesInstanceUID,
  PatientID,
  collection_id,
  ManufacturerModelName
FROM index
WHERE Modality = 'CT'
  AND BodyPartExamined = 'CHEST'
  AND Manufacturer = 'GE MEDICAL SYSTEMS'
  AND license_short_name = 'CC BY 4.0'
LIMIT 100
"""

results = client.sql_query(query)

# Save manifest for later
results.to_csv('lung_ct_manifest.csv', index=False)

# Download in batches to avoid timeout
batch_size = 10
for i in range(0, len(results), batch_size):
    batch = results.iloc[i:i+batch_size]
    client.download_from_selection(
        seriesInstanceUID=list(batch['SeriesInstanceUID'].values),
        downloadDir=f"./data/batch_{i//batch_size}"
    )

7. Advanced Queries with BigQuery

For queries requiring full DICOM metadata, complex JOINs, clinical data tables, or private DICOM elements, use Google BigQuery. Requires GCP account with billing enabled.

Quick reference:

• Dataset:

  bigquery-public-data.idc_current.*

• Main table:

  dicom_all

  (combined metadata)
• Full metadata:

  dicom_metadata

  (all DICOM tags)
• Private elements:

  OtherElements

  column (vendor-specific tags like diffusion b-values)

See

references/bigquery_guide.md

Before using BigQuery, always check if a specialized index table already has the metadata you need:

1. Use

   client.indices_overview

   or the idc-index indices reference to discover all available tables and their columns
2. Fetch the relevant index:

   client.fetch_index("table_name")

3. Query locally with

   client.sql_query()

   (free, no GCP account needed)

Common specialized indices:

seg_index

ann_index

ann_group_index

sm_index

collections_index

8. Tool Selection Guide

idc-index

references/bigquery_guide.md

Default choice: Use

idc-index

9. Integration with Analysis Pipelines

Integrate IDC data into imaging analysis workflows:

Read downloaded DICOM files:

import pydicom
import os

# Read DICOM files from downloaded series
series_dir = "./data/rider/rider_pilot/RIDER-1007893286/CT_1.3.6.1..."

dicom_files = [os.path.join(series_dir, f) for f in os.listdir(series_dir)
               if f.endswith('.dcm')]

# Load first image
ds = pydicom.dcmread(dicom_files[0])
print(f"Patient ID: {ds.PatientID}")
print(f"Modality: {ds.Modality}")
print(f"Image shape: {ds.pixel_array.shape}")

Build 3D volume from CT series:

import pydicom
import numpy as np
from pathlib import Path

def load_ct_series(series_path):
    """Load CT series as 3D numpy array"""
    files = sorted(Path(series_path).glob('*.dcm'))
    slices = [pydicom.dcmread(str(f)) for f in files]

    # Sort by slice location
    slices.sort(key=lambda x: float(x.ImagePositionPatient[2]))

    # Stack into 3D array
    volume = np.stack([s.pixel_array for s in slices])

    return volume, slices[0]  # Return volume and first slice for metadata

volume, metadata = load_ct_series("./data/lung_ct/series_dir")
print(f"Volume shape: {volume.shape}")  # (z, y, x)

Integrate with SimpleITK:

import SimpleITK as sitk
from pathlib import Path

# Read DICOM series
series_path = "./data/ct_series"
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(series_path)
reader.SetFileNames(dicom_names)
image = reader.Execute()

# Apply processing
smoothed = sitk.CurvatureFlow(image1=image, timeStep=0.125, numberOfIterations=5)

# Save as NIfTI
sitk.WriteImage(smoothed, "processed_volume.nii.gz")

Common Use Cases

See

references/use_cases.md

• 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 questons/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

• 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

• 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

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

# 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)