/imaging-data-commons

name: imaging-data-commons
description: Queries and downloads public cancer imaging data from NCI Imaging Data Commons. Supports IDC collection discovery, DICOM access, radiology (CT, MR, PET) and pathology AI datasets, metadata SQL, visualization, licensing, and citations. Uses public metadata and download routes without authentication; optional BigQuery and Google Healthcare routes require Google credentials.
compatibility: Requires network access for hosted APIs, index fetching, citations, and downloads. Local Python workflows target idc-index 0.12.5; BigQuery and Google Healthcare require Google credentials.
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: "1.8"
  last-reviewed: "2026-09-30"
  source-skill-version: "1.8.1"
  skill-author: Andrey Fedorov, @fedorov
  idc-index: "0.12.5"
  idc-data-version: "v24"
  repository: https://github.com/ImagingDataCommons/imaging-data-commons-skill

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*. 2. **Otherwise, is `idc-index` installed and current?** Run `python scripts/check_version.py`. If it passes, use `idc-index` for everything. 3. **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*. 4. **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](https://github.com/imagingdatacommons/idc-index)) 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 fo