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imaging-data-commons

Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.

This skill is provided under the MIT License. IDC data itself has individual licensing (mostly CC-BY, some CC-NC) that must be respected when using the data.

skill-author
Andrey Fedorov, @fedorov

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.

Primary tool: idc-index (GitHub)

Check current data scale for the latest version:

from idc_index import IDCClient
client = IDCClient()

# get IDC data version
print(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:

Query metadata → client.sql_query()
Download DICOM files → client.download_from_selection()
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

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.

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

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_index	Link patients across current and historical data
`analysis_result_id`	index, analysis_results_index	Link series to analysis result metadata (annotations, segmentations)
`source_DOI`	index, analysis_results_index	Link by publication DOI
`crdc_series_uuid`	index, prior_versions_index	Link by CRDC unique identifier
`Modality`	index, prior_versions_index	Filter by imaging modality
`SeriesInstanceUID`	index, seg_index	Link segmentation series to its index metadata
`segmented_SeriesInstanceUID`	seg_index → index	Link segmentation to its source image series (join seg_index.segmented_SeriesInstanceUID = index.SeriesInstanceUID)

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

Example joins:

from idc_index import IDCClient
client = IDCClient()

# Join index with collections_index to get cancer types
client.fetch_index("collections_index")
result = client.sql_query("""
    SELECT i.SeriesInstanceUID, i.Modality, c.CancerTypes, c.TumorLocations
    FROM index i
    JOIN collections_index c ON i.collection_id = c.collection_id
    WHERE i.Modality = 'MR'
    LIMIT 10
""")

# Join index with sm_index for slide microscopy details
client.fetch_index("sm_index")
result = client.sql_query("""
    SELECT i.collection_id, i.PatientID, s.ObjectiveLensPower, s.min_PixelSpacing_2sf
    FROM index i
    JOIN sm_index s ON i.SeriesInstanceUID = s.SeriesInstanceUID
    LIMIT 10
""")

# Join seg_index with index to find segmentations and their source images
client.fetch_index("seg_index")
result = client.sql_query("""
    SELECT
        s.SeriesInstanceUID as seg_series,
        s.AlgorithmName,
        s.total_segments,
        src.collection_id,
        src.Modality as source_modality,
        src.BodyPartExamined
    FROM seg_index s
    JOIN index src ON s.segmented_SeriesInstanceUID = src.SeriesInstanceUID
    WHERE s.AlgorithmType = 'AUTOMATIC'
    LIMIT 10
""")

Accessing Index Tables

Via SQL (recommended for filtering/aggregation):

from idc_index import IDCClient
client = IDCClient()

# Query the primary index (always available)
results = client.sql_query("SELECT * FROM index WHERE Modality = 'CT' LIMIT 10")

# Fetch and query additional indices
client.fetch_index("collections_index")
collections = client.sql_query("SELECT collection_id, CancerTypes, TumorLocations FROM collections_index")

client.fetch_index("analysis_results_index")
analysis = client.sql_query("SELECT * FROM analysis_results_index LIMIT 5")

As pandas DataFrames (direct access):

# Primary index (always available after client initialization)
df = client.index

# Fetch and access on-demand indices
client.fetch_index("sm_index")
sm_df = client.sm_index

Discovering Table Schemas (Essential for Query Writing)

The indices_overview dictionary contains complete schema information for all tables. Always consult this when writing queries or exploring data structure.

DICOM attribute mapping: Many columns are populated directly from DICOM attributes in the source files. The column description in the schema indicates when a column corresponds to a DICOM attribute (e.g., "DICOM Modality attribute" or references a DICOM tag). This allows leveraging DICOM knowledge when querying — standard DICOM attribute names like PatientID, StudyInstanceUID, Modality, BodyPartExamined work as expected.

from idc_index import IDCClient
client = IDCClient()

# List all available indices with descriptions
for name, info in client.indices_overview.items():
    print(f"\n{name}:")
    print(f"  Installed: {info['installed']}")
    print(f"  Description: {info['description']}")

# Get complete schema for a specific index (columns, types, descriptions)
schema = client.indices_overview["index"]["schema"]
print(f"\nTable: {schema['table_description']}")
print("\nColumns:")
for col in schema['columns']:
    desc = col.get('description', 'No description')
    # Description indicates if column is from DICOM attribute
    print(f"  {col['name']} ({col['type']}): {desc}")

# Find columns that are DICOM attributes (check description for "DICOM" reference)
dicom_cols = [c['name'] for c in schema['columns'] if 'DICOM' in c.get('description', '').upper()]
print(f"\nDICOM-sourced columns: {dicom_cols}")

Alternative: use get_index_schema() method:

schema = client.get_index_schema("index")
# Returns same schema dict: {'table_description': ..., 'columns': [...]}

Key Columns in Primary `index` Table

Most common columns for queries (use indices_overview for complete list and descriptions):

Column	Type	DICOM	Description
`collection_id`	STRING	No	IDC collection identifier
`analysis_result_id`	STRING	No	If applicable, indicates what analysis results collection given series is part of
`source_DOI`	STRING	No	DOI linking to dataset details; use for learning more about the content and for attribution (see citations below)
`PatientID`	STRING	Yes	Patient identifier
`StudyInstanceUID`	STRING	Yes	DICOM Study UID
`SeriesInstanceUID`	STRING	Yes	DICOM Series UID — use for downloads/viewing
`Modality`	STRING	Yes	Imaging modality (CT, MR, PT, SM, etc.)
`BodyPartExamined`	STRING	Yes	Anatomical region
`SeriesDescription`	STRING	Yes	Description of the series
`Manufacturer`	STRING	Yes	Equipment manufacturer
`StudyDate`	STRING	Yes	Date study was performed
`PatientSex`	STRING	Yes	Patient sex
`PatientAge`	STRING	Yes	Patient age at time of study
`license_short_name`	STRING	No	License type (CC BY 4.0, CC BY-NC 4.0, etc.)
`series_size_MB`	FLOAT	No	Size of series in megabytes
`instanceCount`	INTEGER	No	Number of DICOM instances in series

DICOM = Yes: Column value extracted from the DICOM attribute with the same name. Refer to the DICOM standard for numeric tag mappings. Use standard DICOM knowledge for expected values and formats.

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")

Data Access Options

Method	Auth Required	Best For
`idc-index`	No	Key queries and downloads (recommended)
IDC Portal	No	Interactive exploration, manual selection, browser-based download
BigQuery	Yes (GCP account)	Complex queries, full DICOM metadata
DICOMweb proxy	No	Tool integration via DICOMweb API

DICOMweb access

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

Endpoint	Auth	Use Case
Public proxy	No	Testing, moderate queries, daily quota
Google Healthcare	Yes (GCP)	Production use, higher quotas

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

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.

Tested with: idc-index 0.11.7 (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

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:

Option	Description
`--download-dir`	Output directory (default: current directory)
`--dir-template`	Directory hierarchy template (default: `%collection_id/%PatientID/%StudyInstanceUID/%Modality_%SeriesInstanceUID`)
`--log-level`	Verbosity: debug, info, warning, error, critical

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 = '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, or clinical data tables, 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)

See references/bigquery_guide.md for setup, table schemas, query patterns, and cost optimization.

8. Tool Selection Guide

Task	Tool	Reference
Programmatic queries & downloads	`idc-index`	This document
Interactive exploration	IDC Portal	https://portal.imaging.datacommons.cancer.gov/
Complex metadata queries	BigQuery	`references/bigquery_guide.md`
3D visualization & analysis	SlicerIDCBrowser	https://github.com/ImagingDataCommons/SlicerIDCBrowser

Default choice: Use idc-index for most tasks (no auth, easy API, batch downloads).

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

Use Case 1: Find and Download Lung CT Scans for Deep Learning

Objective: Build training dataset of lung CT scans from NLST collection

Steps:

from idc_index import IDCClient

client = IDCClient()

# 1. Query for lung CT scans with specific criteria
query = """
SELECT
  PatientID,
  SeriesInstanceUID,
  SeriesDescription
FROM index
WHERE collection_id = 'nlst'
  AND Modality = 'CT'
  AND BodyPartExamined = 'CHEST'
  AND license_short_name = 'CC BY 4.0'
ORDER BY PatientID
LIMIT 100
"""

results = client.sql_query(query)
print(f"Found {len(results)} series from {results['PatientID'].nunique()} patients")

# 2. Download data organized by patient
client.download_from_selection(
    seriesInstanceUID=list(results['SeriesInstanceUID'].values),
    downloadDir="./training_data",
    dirTemplate="%collection_id/%PatientID/%SeriesInstanceUID"
)

# 3. Save manifest for reproducibility
results.to_csv('training_manifest.csv', index=False)

Use Case 2: Query Brain MRI by Manufacturer for Quality Study

Objective: Compare image quality across different MRI scanner manufacturers

Steps:

from idc_index import IDCClient
import pandas as pd

client = IDCClient()

# Query for brain MRI grouped by manufacturer
query = """
SELECT
  Manufacturer,
  ManufacturerModelName,
  COUNT(DISTINCT SeriesInstanceUID) as num_series,
  COUNT(DISTINCT PatientID) as num_patients
FROM index
WHERE Modality = 'MR'
  AND BodyPartExamined LIKE '%BRAIN%'
GROUP BY Manufacturer, ManufacturerModelName
HAVING num_series >= 10
ORDER BY num_series DESC
"""

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

# Download sample from each manufacturer for comparison
for _, row in manufacturers.head(3).iterrows():
    mfr = row['Manufacturer']
    model = row['ManufacturerModelName']

    query = f"""
    SELECT SeriesInstanceUID
    FROM index
    WHERE Manufacturer = '{mfr}'
      AND ManufacturerModelName = '{model}'
      AND Modality = 'MR'
      AND BodyPartExamined LIKE '%BRAIN%'
    LIMIT 5
    """

    series = client.sql_query(query)
    client.download_from_selection(
        seriesInstanceUID=list(series['SeriesInstanceUID'].values),
        downloadDir=f"./quality_study/{mfr.replace(' ', '_')}"
    )

Use Case 3: Visualize Series Without Downloading

Objective: Preview imaging data before committing to download

from idc_index import IDCClient
import webbrowser

client = IDCClient()

series_list = client.sql_query("""
    SELECT SeriesInstanceUID, PatientID, SeriesDescription
    FROM index
    WHERE collection_id = 'acrin_nsclc_fdg_pet' AND Modality = 'PT'
    LIMIT 10
""")

# Preview each in browser
for _, row in series_list.iterrows():
    viewer_url = client.get_viewer_URL(seriesInstanceUID=row['SeriesInstanceUID'])
    print(f"Patient {row['PatientID']}: {row['SeriesDescription']}")
    print(f"  View at: {viewer_url}")
    # webbrowser.open(viewer_url)  # Uncomment to open automatically

For additional visualization options, see the IDC Portal getting started guide or SlicerIDCBrowser for 3D Slicer integration.

Use Case 4: License-Aware Batch Download for Commercial Use

Objective: Download only CC-BY licensed data suitable for commercial applications

Steps:

from idc_index import IDCClient

client = IDCClient()

# Query ONLY for CC BY licensed data (allows commercial use with attribution)
query = """
SELECT
  SeriesInstanceUID,
  collection_id,
  PatientID,
  Modality
FROM index
WHERE license_short_name LIKE 'CC BY%'
  AND license_short_name NOT LIKE '%NC%'
  AND Modality IN ('CT', 'MR')
  AND BodyPartExamined IN ('CHEST', 'BRAIN', 'ABDOMEN')
LIMIT 200
"""

cc_by_data = client.sql_query(query)

print(f"Found {len(cc_by_data)} CC BY licensed series")
print(f"Collections: {cc_by_data['collection_id'].unique()}")

# Download with license verification
client.download_from_selection(
    seriesInstanceUID=list(cc_by_data['SeriesInstanceUID'].values),
    downloadDir="./commercial_dataset",
    dirTemplate="%collection_id/%Modality/%PatientID/%SeriesInstanceUID"
)

# Save license information
cc_by_data.to_csv('commercial_dataset_manifest_CC-BY_ONLY.csv', index=False)

Best Practices

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 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

Quick reference for common queries. For detailed examples with context, see the Core Capabilities section above.

Discover available filter values

# What modalities exist?
client.sql_query("SELECT DISTINCT Modality FROM index")

# What body parts for a specific modality?
client.sql_query("""
    SELECT DISTINCT BodyPartExamined, COUNT(*) as n
    FROM index WHERE Modality = 'CT' AND BodyPartExamined IS NOT NULL
    GROUP BY BodyPartExamined ORDER BY n DESC
""")

# What manufacturers for MR?
client.sql_query("""
    SELECT DISTINCT Manufacturer, COUNT(*) as n
    FROM index WHERE Modality = 'MR'
    GROUP BY Manufacturer ORDER BY n DESC
""")

Find annotations and segmentations

Note: Not all image-derived objects belong to analysis result collections. Some annotations are deposited alongside original images. Use DICOM Modality or SOPClassUID to find all derived objects regardless of collection type.

# Find ALL segmentations and structure sets by DICOM Modality
# SEG = DICOM Segmentation, RTSTRUCT = Radiotherapy Structure Set
client.sql_query("""
    SELECT collection_id, Modality, COUNT(*) as series_count
    FROM index
    WHERE Modality IN ('SEG', 'RTSTRUCT')
    GROUP BY collection_id, Modality
    ORDER BY series_count DESC
""")

# Find segmentations for a specific collection (includes non-analysis-result items)
client.sql_query("""
    SELECT SeriesInstanceUID, SeriesDescription, analysis_result_id
    FROM index
    WHERE collection_id = 'tcga_luad' AND Modality = 'SEG'
""")

# List analysis result collections (curated derived datasets)
client.fetch_index("analysis_results_index")
client.sql_query("""
    SELECT analysis_result_id, analysis_result_title, Collections, Modalities
    FROM analysis_results_index
""")

# Find analysis results for a specific source collection
client.sql_query("""
    SELECT analysis_result_id, analysis_result_title
    FROM analysis_results_index
    WHERE Collections LIKE '%tcga_luad%'
""")

# Use seg_index for detailed DICOM Segmentation metadata
client.fetch_index("seg_index")

# Get segmentation statistics by algorithm
client.sql_query("""
    SELECT AlgorithmName, AlgorithmType, COUNT(*) as seg_count
    FROM seg_index
    WHERE AlgorithmName IS NOT NULL
    GROUP BY AlgorithmName, AlgorithmType
    ORDER BY seg_count DESC
    LIMIT 10
""")

# Find segmentations for specific source images (e.g., chest CT)
client.sql_query("""
    SELECT
        s.SeriesInstanceUID as seg_series,
        s.AlgorithmName,
        s.total_segments,
        s.segmented_SeriesInstanceUID as source_series
    FROM seg_index s
    JOIN index src ON s.segmented_SeriesInstanceUID = src.SeriesInstanceUID
    WHERE src.Modality = 'CT' AND src.BodyPartExamined = 'CHEST'
    LIMIT 10
""")

# Find TotalSegmentator results with source image context
client.sql_query("""
    SELECT
        seg_info.collection_id,
        COUNT(DISTINCT s.SeriesInstanceUID) as seg_count,
        SUM(s.total_segments) as total_segments
    FROM seg_index s
    JOIN index seg_info ON s.SeriesInstanceUID = seg_info.SeriesInstanceUID
    WHERE s.AlgorithmName LIKE '%TotalSegmentator%'
    GROUP BY seg_info.collection_id
    ORDER BY seg_count DESC
""")

Query slide microscopy data

# sm_index has detailed metadata; join with index for collection_id
client.fetch_index("sm_index")
client.sql_query("""
    SELECT i.collection_id, COUNT(*) as slides,
           MIN(s.min_PixelSpacing_2sf) as min_resolution
    FROM sm_index s
    JOIN index i ON s.SeriesInstanceUID = i.SeriesInstanceUID
    GROUP BY i.collection_id
    ORDER BY slides DESC
""")

Estimate download size

# Size for specific criteria
client.sql_query("""
    SELECT SUM(series_size_MB) as total_mb, COUNT(*) as series_count
    FROM index
    WHERE collection_id = 'nlst' AND Modality = 'CT'
""")

Link to clinical data

client.fetch_index("clinical_index")

# Find collections with clinical data and their tables
client.sql_query("""
    SELECT collection_id, table_name, COUNT(DISTINCT column_label) as columns
    FROM clinical_index
    GROUP BY collection_id, table_name
    ORDER BY collection_id
""")

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

histolab - Lightweight tile extraction and preprocessing for whole slide images. Use for basic slide processing, tissue detection, and dataset preparation from IDC slide microscopy data.
pathml - Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed imaging, nucleus segmentation, and ML model training on pathology data downloaded from IDC.

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

bigquery_guide.md - Advanced BigQuery usage guide for complex metadata queries
dicomweb_guide.md - DICOMweb endpoint URLs, code examples, and Google Healthcare API implementation details
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

41 KiB Raw Blame History

Imaging Data Commons

Overview

When to Use This Skill

IDC Data Model

Index Tables

Available Tables

Joining Tables

Accessing Index Tables

Discovering Table Schemas (Essential for Query Writing)

Key Columns in Primary index Table

Clinical Data Access

Data Access Options

Installation and Setup

Core Capabilities

1. Data Discovery and Exploration

2. Querying Metadata with SQL

3. Downloading DICOM Files

Command-Line Download

4. Visualizing IDC Images

5. Understanding and Checking Licenses

Generating Citations for Attribution

6. Batch Processing and Filtering

7. Advanced Queries with BigQuery

8. Tool Selection Guide

9. Integration with Analysis Pipelines

Common Use Cases

Use Case 1: Find and Download Lung CT Scans for Deep Learning

Use Case 2: Query Brain MRI by Manufacturer for Quality Study

Use Case 3: Visualize Series Without Downloading

Use Case 4: License-Aware Batch Download for Commercial Use

Best Practices

Troubleshooting

Common SQL Query Patterns

Discover available filter values

Find annotations and segmentations

Query slide microscopy data

Estimate download size

Link to clinical data

Related Skills

DICOM Processing

Pathology and Slide Microscopy

Metadata Visualization

Data Exploration

Resources

Schema Reference (Primary Source)

Reference Documentation

External Links

41 KiB

Raw Blame History

Key Columns in Primary `index` Table