imaging-data-commons – skill Dữ liệu & Khoa học cho agent AI

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 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):

| 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 |

| licensing_and_citation.md | Commercial-use questions, mixed-license cohorts, citation formats |

| 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) |

| mcp_guide.md | Hosted IDC MCP server: tool inventory, identification, handoff to idc-index |

| rest_api_guide.md | Hosted IDC REST API: endpoints, filter syntax, SQL over HTTP, manifests |

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:

| 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. 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.

| Family | Tables | Granularity |

|--------|--------|-------------|

| Core | index (primary metadata for all current data), collections_index, analysis_results_index | series / collection / analysis result |

| Modality acquisition pa

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