imaging-data-commons

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Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.

Data & Documents 26,817 stars 2774 forks Updated today MIT

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Skill Content

# 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. **Current IDC Data Version: v23** (always verify with `IDCClient().get_idc_version()`) **Primary tool:** `idc-index` ([GitHub](https://github.com/imagingdatacommons/idc-index)) **CRITICAL - Check package version and upgrade if needed (run this FIRST):** ```python import idc_index REQUIRED_VERSION = "0.11.14" # Must match metadata.idc-index in this file installed = idc_index.__version__ if installed < REQUIRED_VERSION: print(f"Upgrading idc-index from {installed} to {REQUIRED_VERSION}...") import subprocess subprocess.run(["pip3", "install", "--upgrade", "--break-system-packages", "idc-index"], check=True) print("Upgrade complete. Restart Python to use new version.") else: print(f"idc-index {installed} meets requirement ({REQUIRED_VERSION})") ``` **Verify IDC data version and check current data scale:** ```python from idc_index import IDCClient client = IDCClient() # Verify IDC data version (should be "v23") 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(DISTIN...

Details

Author
K-Dense-AI
Repository
K-Dense-AI/scientific-agent-skills
Created
7 months ago
Last Updated
today
Language
Python
License
MIT

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