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Estrella-231
Estrella-231COMMUNITY
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imaging-data-commons

Query and download cancer imaging datasets from NCI Imaging Data Commons using idc-index identifiers.

Updated May 24, 2026
Search Tags:#license compliance#dicom#metadata query#dataset download#cancer imaging#python idc-index#viewer url
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill imaging-data-commons-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: imaging-data-commons
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/imaging-data-commons
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill imaging-data-commons-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the friction of locating, filtering, and downloading large public cancer imaging datasets by automating metadata queries, license checks, and DICOM downloads from NCI Imaging Data Commons (IDC).

Core Features & Use Cases

  • Dataset discovery by metadata: Query IDC for CT/MR/PET radiology series or digital pathology (slide microscopy) using idc-index with the collection_id/patient/study/series hierarchy.
  • Efficient downloads for selected cohorts: Download DICOM data by collection_id, by specific SeriesInstanceUID lists, or in batches/organized directory templates.
  • Browser visualization without special tooling: Generate viewer URLs for quick inspection of results before committing to downloads.
  • License-aware usage: Retrieve and surface license information (via IDC metadata) so you can respect CC-BY vs CC-NC terms.
  • Covers core modalities and derived objects: Supports indexing for segmentations (SEG), annotations (ANN), clinical tables, and geometry validation (volume_geometry_index) via on-demand index tables and references.

Quick Start

Use the imaging-data-commons skill to query and download a small subset of IDC CT CHEST series for a research experiment using the currently documented idc-index workflow and then open one downloaded series in the browser viewer to confirm correctness.

Frequently Asked Questions about imaging-data-commons

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I download public cancer imaging datasets from NCI Imaging Data Commons?▼

You can discover CT, MR, PET radiology, and digital pathology series by using the idc-index package to run SQL queries via IDCClient. This allows cohort discovery by metadata using the collection_id, patient, study, and series hierarchy.

Can I preview DICOM series in a browser before downloading large cohorts?▼

Yes, you can preview DICOM series in a browser before downloading by generating viewer URLs. This allows quick browser-based visualization for QA without requiring special tooling or committing to large dataset downloads.

How do I check license terms for cancer imaging datasets before use?▼

Yes, idc-index supports downloading DICOM data in batches or organized directory templates by specific SeriesInstanceUID lists or collection_id. This enables efficient downloads for selected cohorts tailored to radiology and digital pathology research workflows.

Does the imaging-data-commons skill support digital pathology and segmentation objects?▼

Yes, the skill supports indexing for digital pathology slide microscopy, segmentations (SEG), annotations (ANN), and clinical tables. It handles these derived objects through on-demand index tables and references including geometry validation.

Do I need a specific idc-index version to query NCI Imaging Data Commons metadata?▼

Yes, you need an idc-index version that matches the skill metadata to query NCI Imaging Data Commons metadata properly. This ensures deterministic cohort retrieval and compatibility with download_from_selection workflows.

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