imaging-data-commons

name	imaging-data-commons
description	Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Invoke for any question about IDC collections, cancer imaging datasets, DICOM data access, radiology (CT, MR, PET) or pathology AI training sets, metadata queries, visualization, or license checks — even when the user doesn't explicitly mention "IDC". No authentication required.
license	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.
metadata	{"version":"1.6.5","skill-author":"Andrey Fedorov, @fedorov","idc-index":"0.12.3","idc-data-version":"v24","repository":"https://github.com/ImagingDataCommons/imaging-data-commons-skill"}

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.

Expected network access: idc-index queries a local DuckDB index (no network for metadata). File downloads use public GCS (storage.googleapis.com) and AWS S3 (s3.amazonaws.com) — no authentication required. DICOMweb access uses either the public IDC proxy (proxy.imaging.datacommons.cancer.gov, no auth) or the Google Cloud Healthcare API (healthcare.googleapis.com, requires GCP authentication). Optional BigQuery queries (bigquery.googleapis.com) also require GCP authentication. No credentials or environment variables are accessed by this skill.

Current IDC Data Version: v24 (always verify with IDCClient().get_idc_version())

Primary tool: idc-index (GitHub)

CRITICAL - run this FIRST, before any IDC query:

python scripts/check_version.py

It installs the pinned idc-index minimum if needed and prints a notice when a newer idc-index release (which may carry a newer IDC data version) or a newer skill version is available, along with the link to update. If it reports an upgrade, restart Python before continuing.

Verify IDC data version and check current data scale:

from idc_index import IDCClient
client = IDCClient()

# Verify IDC data version (should be "v24")
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
• Core Capabilities - Essential API patterns (query, download, visualize, license, citations)
• 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 workflows: training datasets, batch downloads, DICOM reading with pydicom/SimpleITK, pipeline integration
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.

from idc_index import IDCClient

client = IDCClient()

# Find which table(s) contain a specific column (no fetch required)
target = "SliceThickness"
for table_name, info in client.indices_overview.items():
    if any(c["name"] == target for c in info["schema"]["columns"]):
        print(f"'{target}' is in: {table_name}")
# → 'SliceThickness' is in: ct_index

# List all columns in a table from the schema (no fetch required)
ct_cols = [c["name"] for c in client.indices_overview["ct_index"]["schema"]["columns"]]
print("ct_index columns:", ct_cols)
# → ['SeriesInstanceUID', 'PixelSpacing_row_mm', 'PixelSpacing_col_mm', 'Rows',
#    'Columns', 'SliceThickness', 'KVP', 'ConvolutionKernel', ...]

Available Tables

Always call client.fetch_index("table_name") before querying any index table — it is safe and idempotent for all tables, including those loaded automatically at startup.

Table	Row Granularity	Description
index	1 row = 1 DICOM series	Primary metadata for all current IDC data
version_metadata_index	1 row = 1 IDC release version	IDC version release timestamps; join on idc_version to correlate series with their release date
collections_index	1 row = 1 collection	Collection-level metadata and descriptions
analysis_results_index	1 row = 1 analysis result collection	Metadata about derived datasets (annotations, segmentations)
clinical_index	1 row = 1 (collection, table, column) triple	Dictionary mapping clinical data table columns to collections
sm_index	1 row = 1 slide microscopy series	Slide Microscopy (pathology) series metadata
sm_instance_index	1 row = 1 slide microscopy instance	Instance-level (SOPInstanceUID) metadata for slide microscopy
seg_index	1 row = 1 DICOM Segmentation series	Segmentation metadata: algorithm, segment count, reference to source image series
ann_index	1 row = 1 DICOM ANN series	Microscopy Bulk Simple Annotations series metadata; references annotated image series
ann_group_index	1 row = 1 annotation group	Detailed annotation group metadata: graphic type, annotation count, property codes, algorithm
contrast_index	1 row = 1 series with contrast info	Contrast agent metadata: agent name, ingredient, administration route (CT, MR, PT, XA, RF)
volume_geometry_index	1 row = 1 CT/MR/PT series	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	RT Structure Set metadata: total ROI count, ROI names, generation algorithms, interpreted types, and the referenced image series UID
ct_index	1 row = 1 CT series	CT acquisition/reconstruction parameters: pixel spacing, slice thickness, kVp, convolution kernel, tube current (min/max for dose-modulated), exposure, spiral pitch, scan options
mr_index	1 row = 1 MR series	MR acquisition/sequence parameters: field strength, scanning sequence, TE (array for multi-echo), TR, flip angle, DiffusionBValue (array for DWI), pixel bandwidth, receive coil, number of temporal positions
pt_index	1 row = 1 PET series	PET acquisition/reconstruction/radiopharmaceutical parameters: series type, units, decay/scatter/attenuation correction, reconstruction method, radionuclide, injected dose, frame duration (array for dynamic PET)
prior_versions_index	1 row = 1 DICOM series	Reproducibility only. Contains series permanently removed from IDC (all max_idc_version < current version; zero overlap with index). Use ONLY when a user explicitly needs to reproduce work from a prior IDC version using data no longer in the current release. Do NOT use for version history or "what's new" questions — those use series_init_idc_version/series_revised_idc_version in the main index table. Column names min_idc_version/max_idc_version here are NOT equivalent to series_init_idc_version/series_revised_idc_version in index.

Joining Tables

SeriesInstanceUID is the universal join key for all series-level specialized tables: sm_index, sm_instance_index, seg_index, ann_index, ann_group_index, contrast_index, volume_geometry_index, rtstruct_index, ct_index, mr_index, pt_index. Always join these to index on SeriesInstanceUID. The exceptions below use different column names.

Join Column	Tables	Use Case
collection_id	index, prior_versions_index, collections_index, clinical_index	Link series to collection metadata or clinical 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
segmented_SeriesInstanceUID	seg_index → index	Link segmentation to its source image series (seg_index.segmented_SeriesInstanceUID = index.SeriesInstanceUID)
referenced_SeriesInstanceUID	ann_index → index, rtstruct_index → index	Link annotation or RTSTRUCT to its source image series

Note: subjects, updated, and description appear in multiple tables but have different meanings (counts vs identifiers, different update contexts).

Note on prior_versions_index: Joining prior_versions_index with index on SeriesInstanceUID always returns zero rows — there is no overlap. This table is for historical reproducibility only; never join it with index to answer questions about current data or version history.

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

Method	Auth Required	Best For
idc-index	No	Key queries and downloads (recommended)
Direct Parquet (GCS)	No	Quick queries without installing idc-index; always uses latest data
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
Cloud storage (S3/GCS)	No	Direct file access, bulk downloads, custom pipelines

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.

Bucket (AWS / GCS)	License	Content
idc-open-data / idc-open-data	No commercial restriction	>90% of IDC data
idc-open-data-two / idc-open-idc1	No commercial restriction	Collections with potential head scans
idc-open-data-cr / idc-open-cr	Commercial use restricted (CC BY-NC)	~4% of data

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.

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.

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.

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, cancer_types, tumor_locations, species, subjects, supporting_data
    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.cancer_types 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
• Versioning: series_init_idc_version (IDC version when series was first added), series_revised_idc_version (IDC version when series was last revised)

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

Version tracking — "what's new in IDC vX?"

Use series_init_idc_version and series_revised_idc_version in the main index table. Do NOT use prior_versions_index for this — it contains only removed series.

from idc_index import IDCClient
client = IDCClient()

VERSION = 24  # Replace with target version

# Series added for the first time in vVERSION
new_series = client.sql_query(f"""
    SELECT collection_id,
           COUNT(DISTINCT SeriesInstanceUID) as new_series,
           ROUND(SUM(series_size_MB)/1000, 2) as size_GB
    FROM index
    WHERE series_init_idc_version = {VERSION}
    GROUP BY collection_id
    ORDER BY new_series DESC
""")

# Series revised (updated content) in vVERSION but originally added earlier
revised_series = client.sql_query(f"""
    SELECT collection_id,
           COUNT(DISTINCT SeriesInstanceUID) as revised_series
    FROM index
    WHERE series_revised_idc_version = {VERSION}
      AND series_init_idc_version < {VERSION}
    GROUP BY collection_id
    ORDER BY revised_series DESC
""")

# When was each collection first added to IDC?
client.fetch_index("version_metadata_index")
first_appearance = client.sql_query("""
    WITH first_versions AS (
        SELECT collection_id, MIN(series_init_idc_version) as first_version
        FROM index
        GROUP BY collection_id
    )
    SELECT f.collection_id, f.first_version, v.version_timestamp as first_release_date
    FROM first_versions f
    JOIN version_metadata_index v ON f.first_version = v.idc_version
    ORDER BY f.first_version DESC
""")

To verify column names and descriptions before writing queries, use client.get_index_schema('index') or client.indices_overview — see Best Practices.

3. Downloading DICOM Files

Download imaging data efficiently from IDC's cloud storage.

IMPORTANT — two download methods with different signatures:

Method	First arg	Second arg	Use when
download_from_selection	downloadDir (required)	filter kwargs (optional)	Filtering by collection, patient, study, or series
download_dicom_series	seriesInstanceUID (required)	downloadDir (required)	Downloading specific series by UID only

download_from_selection takes filter keyword arguments, NOT a DataFrame. The name "from_selection" refers to filtering the IDC index by criteria — not accepting a pandas DataFrame. To download the results of a query, extract UIDs from the DataFrame and pass them as a list.

Download entire collection:

from idc_index import IDCClient

client = IDCClient()

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

Download specific series (from a query result):

# Step 1: 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
""")

# Step 2: Extract UIDs as a list from the DataFrame
uids = list(series_df['SeriesInstanceUID'].values)

# Step 3: Pass the list to download_from_selection (NOT the DataFrame itself)
client.download_from_selection(
    downloadDir="./data/lung_ct",
    seriesInstanceUID=uids       # list of strings, not a DataFrame
)

# Alternative: download_dicom_series has seriesInstanceUID as FIRST arg (different order!)
client.download_dicom_series(
    seriesInstanceUID=uids,      # FIRST arg here
    downloadDir="./data/lung_ct"
)

# Download from Google Storage instead of AWS
client.download_from_selection(
    downloadDir="./data/lung_ct",
    seriesInstanceUID=uids,
    source_bucket_location="gcs"
)

Custom directory structure:

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

# Simplified hierarchy (omit StudyInstanceUID level)
client.download_from_selection(
    downloadDir="./data",
    collection_id="tcga_luad",
    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(
    downloadDir="./data/flat",
    seriesInstanceUID=list(series_df['SeriesInstanceUID'].values),
    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

idc download is available after installing idc-index. Auto-detects input type: collection ID, series UID, or manifest file path.

idc download rider_pilot --download-dir ./data
idc download manifest.txt --download-dir ./data

See references/cli_guide.md for full options, idc download-from-manifest (resume support), and idc download-from-selection (filter-based).

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. 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 for setup, table schemas, query patterns, private element access, and cost optimization.

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 (segmentations), ann_index / ann_group_index (microscopy annotations), sm_index (slide microscopy), collections_index (collection metadata). Only use BigQuery if you need private DICOM elements or attributes not in any index.

Use cases that require BigQuery (no idc-index equivalent):

• Per-segment anatomy search — seg_index gives series-level SEG metadata, but the BigQuery segmentations table exposes each segment individually with its DICOM coded structure name (e.g., find all SEG series containing a "Liver" or "Neoplasm" segment)
• Quantitative measurements from SR — the quantitative_measurements BigQuery table contains pre-extracted radiomics features (volume, diameter, shape descriptors, texture, intensity statistics) from DICOM SR TID1500 objects; no idc-index equivalent
• Qualitative measurements from SR — the qualitative_measurements BigQuery table contains coded assessments (malignancy rating, calcification, texture, margin) from DICOM SR TID1500; no idc-index equivalent

See references/bigquery_guide.md for schemas, column descriptions, and query examples for these tables.

7. 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).

Best Practices

• Check schema before writing queries — Use client.get_index_schema('index') (reads cached metadata, no SQL executed) or client.indices_overview to see all available columns and their descriptions. The version-tracking columns series_init_idc_version and series_revised_idc_version in the main index table directly answer "what's new / when was this added" questions without touching prior_versions_index.
• Never use web search for IDC data content questions - Always query the idc-index directly using client.sql_query(). Web sources (release notes, blog posts, documentation pages) are frequently out of date and will produce incorrect answers. The local DuckDB index is the authoritative source; use it even when web search is available.
• 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 v24). 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
• 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

Troubleshooting

Issue: ModuleNotFoundError: No module named 'idc_index'

• Cause: idc-index package not installed
• Solution: Install with pip install --upgrade idc-index; for data analysis also install pip install pandas numpy pydicom (tested with pandas>=1.5, numpy>=1.23, pydicom>=2.3)

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: Column not found in index table (e.g., SliceThickness, PixelSpacing, KVP, EchoTime, InjectedDose)

• Cause: The index table contains series-level metadata only; modality-specific acquisition and reconstruction parameters live in dedicated tables (ct_index, mr_index, pt_index)
• Solution: Search client.indices_overview to find the right table, then fetch and join on SeriesInstanceUID:

  target = "SliceThickness"
  for table_name, info in client.indices_overview.items():
      if any(c["name"] == target for c in info["schema"]["columns"]):
          print(f"Found in: {table_name}")
  # → Found in: ct_index
  
  client.fetch_index("ct_index")
  result = client.sql_query("""
      SELECT i.SeriesInstanceUID, i.Modality, c.SliceThickness, c.KVP, c.PixelSpacing_row_mm
      FROM index i
      JOIN ct_index c USING (SeriesInstanceUID)
      WHERE i.collection_id = 'your_collection'
  """)
  

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 digital pathology related see references/digital_pathology_guide.md.

Resources

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)