k-dense-ai/scientific-agent-skills/imaging-data-commons

Imaging Data Commons

Overview

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 metadata is reachable three ways — a bundled local DuckDB index (offline after installation; additional indices are fetched from GitHub and clinical tables from S3), or the hosted IDC service over MCP or REST (api.imaging.datacommons.cancer.gov, no authentication). 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. Citation resolution contacts DOI services. Public IDC routes require no credentials; optional Google clients use Application Default Credentials.

Reviewed 2026-09-30: idc-index 0.12.5, idc-index-data 24.2.2, IDC v24; hosted API 3.0.0b3. Recheck at use time.

Choose the access path first. There is no single default: the cheapest correct path depends on the session and the task.

1. Session already has the IDC MCP server? Route discovery and metadata there — see *IDC

MCP Server*.

1. Otherwise, is `idc-index` installed and current? Run python scripts/check_version.py. If it passes,

use idc-index for everything.

1. Not installed, and the task is read-only metadata — counts, attribute values, collection

lookups, SQL under 10 000 rows, licenses, citations, viewer URLs? Use the REST API over `curl`; do not install anything. Installing costs ~77 MB of packaged index data plus pandas, pyarrow, and duckdb, which a metadata question does not need. See Data Access Options.

1. Not installed, and the task needs more than metadata — downloading files, pandas or

plotting, pydicom/SimpleITK, pathology tiling, results past 10 000 rows, or a version-pinned script the user re-runs? Install idc-index: check_version.py exits non-zero and prints the exact install command for the running interpreter. Prefer a virtual environment, then restart Python.

idc-index (GitHub) is still the most capable Python path, with query and download helpers in one client. check_version.py never installs anything itself — it also flags a newer idc-index or skill release when one exists.

Setup for the `idc-index` path: use the intended interpreter for scripts/check_version.py and confirm its meets pinned minimum message before continuing. A launch failure is not a pass.

from idc_index import IDCClient
client = IDCClient()
# Verify IDC data version (should be "v24")
print(f"IDC data version: {client.get_idc_version()}")

Download and image-processing examples are illustrative unless noted; see the reference review notes for verification scope.

Core workflow: query metadata with client.sql_query() → download with client.download_from_selection() → visualize with client.get_viewer_URL(). Python examples below assume this client; Data Access Options has the REST equivalents. For current data scale, run the summary query in references/sql_patterns.md or GET /v3/stats.

IDC MCP Server

IDC operates a hosted MCP server at https://api.imaging.datacommons.cancer.gov/mcp (streamable HTTP, no authentication). Where it is available it complements — it does not replace — the idc-index workflow below.

Identify it by the MCP resource idc://guide, or by three or more of the tool names build_cohort, get_cohort_urls, list_analysis_results, and get_idc_version. Generic names such as run_sql are not evidence on their own. If identification is ambiguous, use idc-index.

If this session has the server, treat it as authoritative for discovery and metadata — IDC version, counts, attribute values, cohort building, metadata SQL — and follow the server's own instructions rather than re-deriving them from this file. Its data version is whatever the server reports: call get_idc_version instead of relying on the version pinned in this file.

Return here for what the server does not do: downloading files, local pandas/notebook analysis, DICOMweb, BigQuery, digital pathology tiling, and reproducible scripts. Hand off by passing SeriesInstanceUIDs from the server to client.download_from_selection(...), and run scripts/check_version.py at that point.

If it is not available, the identical service is reachable with no configuration as a REST API at https://api.imaging.datacommons.cancer.gov/v3 — use it for read-only metadata rather than installing idc-index, per the routing gate in Overview. Suggest connecting the MCP server at most once, only for repeated interactive discovery, and never change the user's configuration yourself.

See references/mcp_guide.md for the tool inventory, handoff patterns, and per-host notes.

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

Inline below: the MCP/REST routing rules, the IDC data model, the index tables and how they join, the core API patterns (query, download, visualize, license, cite), best practices, and troubleshooting.

Reference Guides (load on demand):

IDC Data Model

IDC adds two grouping levels above the standard DICOM hierarchy (Patient → Study → Series → Instance):

• collection_id: Groups patients by disease, modality, or research focus (e.g., tcga_luad, nlst). Treat (collection_id, PatientID) as the patient key; do not assume PatientID is globally unique.
• analysis_result_id: Identifies derived objects (segmentations, annotations, radiomics features) across one or more original collections. Use it to find AI-generated or expert annotations, while collection_id finds original imaging data (which may itself include deposited annotations).

Key identifiers for queries:

Index Tables

The idc-index package provides multiple metadata index tables, accessible via SQL or as pandas DataFrames. The REST API exposes the same tables through GET /tables and POST /sql.

Important: client.indices_overview is the authoritative source for current table descriptions, available columns, and their types — query it when writing SQL or exploring data structure. It also answers "which table contains column X"; see references/index_tables_guide.md for that search pattern and full schema discovery.

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.

references/index_tables_guide.md has the full inventory with each table's columns and contents — load it when you need to know what a specialized table actually holds.

`prior_versions_index` contains historical series versions, including revised versions whose DICOM SeriesInstanceUID still occurs in index. Pin crdc_series_uuid for historical content. For "what's new" in the current release use series_init_idc_version / series_revised_idc_version in the main index table, which are not equivalent to this table's min_idc_version / max_idc_version.

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.

Note: subjects, updated, and description appear in multiple tables but have different meanings (counts vs identifiers, different update contexts). A UID-only join to prior_versions_index can match several historical revisions; use CRDC UUIDs to distinguish them.

For detailed join examples, schema discovery patterns, key columns reference, and DataFrame access, see references/index_tables_guide.md.

Clinical Data Access

Clinical (non-imaging) attributes — staging, demographics, therapy — live in per-collection tables. client.fetch_index("clinical_index") loads the dictionary mapping columns to collections; client.get_clinical_table(name) returns one table as a DataFrame.

See references/clinical_data_guide.md for the discovery workflow, coded-value mapping, and joining clinical data with imaging.

Data Access Options