v1.1 to v1.2

---

name: imaging-data-commons

description: 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.

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:

source-skill-version: 1.4.0

skill-author: Andrey Fedorov, @fedorov

idc-index: 0.11.14

idc-data-version: v23

repository: https://github.com/ImagingDataCommons/idc-claude-skill

---

# 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__

def _parts(version):

# Compare numerically: "0.9.0" < "0.11.14" is False as a string comparison.

return tuple(int(p) if p.isdigit() else 0 for p in version.split(".")[:3])

if _parts(installed) < _parts(REQUIRED_VERSION):

print(f"Upgrading idc-index from {installed} to {REQUIRED_VERSION}...")

import subprocess

# Pin to the tested version — an unpinned upgrade installs whatever is

# newest on PyPI, into system packages, and may still not satisfy the check.

subprocess.run(

["pip3", "install", "--break-system-packages", f"idc-index=={REQUIRED_VERSION}"],

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(DISTINCT StudyInstanceUID) as studies,

COUNT(DISTINCT SeriesInstanceUID) as series,

SUM(instanceCount) as instances,

SUM(series_size_MB)/1000000 as size_TB

FROM index

""")

print(stats)

```

**Core workflow:**

1. Query metadata → `client.sql_query()`

2. Download DICOM files → `client.download_from_selection()`

3. Visualize in browser → `client.get_viewer_URL(seriesInstanceUID=...)`

## 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

**Core Sections (inline):**

- IDC Data Model - Collection and analysis result hierarchy

- Index Tables - Available tables and joining patterns

- Installation - Package setup and version verification

- Core Capabilities - Essential API patterns (query, download, visualize, license, citations, batch)

- Best Practices - Usage guidelines

- Troubleshooting - Common issues and solutions

**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 workflow examples (training datasets, batch downloads) |

| `sql_patterns.md` | Quick SQL patterns for filter discovery, annotations, size estimation |

| `clinical_data_guide.md` | Clinical/tabular data, imaging+clinical joins, value mapping |

| `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) |

## 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`). A patient belongs to exactly one collection.

- **analysis_result_id**: Identifies derived objects (segmentations, annotations, radiomics features) across one or more original collections.

Use `collection_id` to find original imaging data, may include annotations deposited along with the images; use `analysis_result_id` to find AI-generated or expert 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.

**Complete index table documentation:** Use https://idc-index.readthedocs.io/en/latest/indices_reference.html for quick check of available tables and columns without executing any code.

**Important:** Use `client.indices_overview` to get current table descriptions and column schemas. This is the authoritative source for available columns and their types — always query it when writing SQL or exploring data structure.

### Available Tables

| Table | Row Granularity | Loaded | Description |

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

| `index` | 1 row = 1 DICOM series | Auto | Primary metadata for all current IDC data |

| `prior_versions_index` | 1 row = 1 DICOM series | Auto | Series from previous IDC releases; for downloading deprecated data |

| `collections_index` | 1 row = 1 collection | fetch_index() | Collection-level metadata and descriptions |

| `analysis_results_index` | 1 row = 1 analysis result collection | fetch_index() | Metadata about derived datasets (annotations, segmentations) |

| `clinical_index` | 1 row = 1 clinical data column | fetch_index() | Dictionary mapping clinical table columns to collections |

| `sm_index` | 1 row = 1 slide microscopy series | fetch_index() | Slide Microscopy (pathology) series metadata |

| `sm_instance_index` | 1 row = 1 slide microscopy instance | fetch_index() | Instance-level (SOPInstanceUID) metadata for slide microscopy |

| `seg_index` | 1 row = 1 DICOM Segmentation series | fetch_index() | Segmentation metadata: algorithm, segment count, reference to source image series |

| `ann_index` | 1 row = 1 DICOM ANN series | fetch_index() | Microscopy Bulk Simple Annotations series metadata; references annotated image series |

| `ann_group_index` | 1 row = 1 annotation group | fetch_index() | Detailed annotation group metadata: graphic type, annotation count, property codes, algorithm |

| `contrast_index` | 1 row = 1 series with contrast info | fetch_index() | Contrast agent metadata: agent name, ingredient, administration route (CT, MR, PT, XA, RF) |

| `volume_geometry_index` | 1 row = 1 CT/MR/PT series | fetch_index() | 3D volume geometry validation for single-frame CT, MR, and PT series; boolean checks for orientation, spacing, dimensions, and slice positions; composite `regularly_spaced_3d_volume` flag |

| `rtstruct_index` | 1 row = 1 RTSTRUCT series | fetch_index() | RT Structure Set metadata: total ROI count, ROI names, generation algorithms, interpreted types, and the referenced image series UID |

**Auto** = loaded automatically when `IDCClient()` is instantiated

**fetch_index()** = requires `client.fetch_index("table_name")` to load

### Joining Tables

**Key columns are not explicitly labeled, the following is a subset that can be used in joins.**

| Join Column | Tables | Use Case |

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

| `collection_id` | index, prior_versions_index, collections_index, clinical_index | Link series to collection metadata or clinical data |

| `SeriesInstanceUID` | index, prior_versions_index, sm_index, sm_instance_index | Link series across tables; connect to slide microscopy details |

| `StudyInstanceUID` | index, prior_versions_index | Link studies across current and historical data |

| `PatientID` | index, prior_versions_index | Link patients across current and historical data |

| `analysis_result_id` | index, analysis_results_index | Link series to analysis result metadata (annotations, segmentations) |

| `source_DOI` | index, analysis_results_index | Link by publication DOI |

| `crdc_series_uuid` | index, prior_versions_index | Link by CRDC unique identifier |

| `Modality` | index, prior_versions_index | Filter by imaging modality |

| `SeriesInstanceUID` | index, seg_index, ann_index, ann_group_index, contrast_index | Link segmentation/annotation/contrast series to its index metadata |

| `segmented_SeriesInstanceUID` | seg_index → index | Link segmentation to its source image series (join seg_index.segmented_SeriesInstanceUID = index.SeriesInstanceUID) |

| `referenced_SeriesInstanceUID` | ann_index → index | Link annotation to its source image series (join ann_index.referenced_SeriesInstanceUID = index.SeriesInstanceUID) |

| `SeriesInstanceUID` | index, volume_geometry_index | Link series to its 3D geometry validation result (join index.SeriesInstanceUID = volume_geometry_index.SeriesInstanceUID) |

| `SeriesInstanceUID` / `referenced_SeriesInstanceUID` | index, rtstruct_index | Join RTSTRUCT series to its metadata (index.SeriesInstanceUID = rtstruct_index.SeriesInstanceUID); use rtstruct_index.referenced_SeriesInstanceUID to find the source image series |

**Note:** `Subjects`, `Updated`, and `Description` appear in multiple tables but have different meanings (counts vs identifiers, different update contexts).

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

### Clinical Data Access

```python

# Fetch clinical index (also downloads clinical data tables)

client.fetch_index("clinical_index")

# Query clinical index to find available tables and their columns

tables = client.sql_query("SELECT DISTINCT table_name, column_label FROM clinical_index")

# Load a specific clinical table as DataFrame

clinical_df = client.get_clinical_table("table_name")

```

See `references/clinical_data_guide.md` for detailed workflows including value mapping patterns and joining clinical data with imaging.

## Data Access Options

| Method | Auth Required | Best For |

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

| `idc-index` | No | Key queries and downloads (recommended) |

| Direct Parquet (GCS) | No | Quick queries without installing idc-index; always uses latest data |

| IDC Portal | No | Interactive exploration, manual selection, browser-based download |

| BigQuery | Yes (GCP account) | Complex queries, full DICOM metadata |

| DICOMweb proxy | No | Tool integration via DICOMweb API |

| Cloud storage (S3/GCS) | No | Direct file access, bulk downloads, custom pipelines |

**Cloud storage organization**

IDC maintains all DICOM files in public cloud storage buckets mirrored between AWS S3 and Google Cloud Storage. Files are organized by CRDC UUIDs (not DICOM UIDs) to support versioning.

| Bucket (AWS / GCS) | License | Content |

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

| `idc-open-data` / `idc-open-data` | No commercial restriction | >90% of IDC data |

| `idc-open-data-two` / `idc-open-idc1` | No commercial restriction | Collections with potential head scans |

| `idc-open-data-cr` / `idc-open-cr` | Commercial use restricted (CC BY-NC) | ~4% of data |

Files are stored as `<crdc_series_uuid>/<crdc_instance_uuid>.dcm`. Access is free (no egress fees) via AWS CLI, gsutil, or s5cmd with anonymous access. Use `series_aws_url` column from the index for S3 URLs; GCS uses the same path structure.

See `references/cloud_storage_guide.md` for bucket details, access commands, UUID mapping, and versioning.

**DICOMweb access**

IDC data is available via DICOMweb interface (Google Cloud Healthcare API implementation) for integration with PACS systems and DICOMweb-compatible tools.

| Endpoint | Auth | Use Case |

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

| Public proxy | No | Testing, moderate queries, daily quota |

| Google Healthcare | Yes (GCP) | Production use, higher quotas |

See `references/dicomweb_guide.md` for endpoint URLs, code examples, supported operations, and implementation details.

**Direct Parquet access**

All idc-index metadata tables are published as Parquet files to a public GCS bucket (`idc-index-data-artifacts`) with unrestricted CORS. This enables DuckDB or pandas queries without installing idc-index, including cross-table joins and queries against `volume_geometry_index` and `rtstruct_index`.

See `references/parquet_access_guide.md` for URL patterns, available files, and DuckDB query examples.

## Installation and Setup

**Required (for basic access):**

```bash

pip install --upgrade idc-index

```

**Important:** New IDC data release will always trigger a new version of `idc-index`. Always use `--upgrade` flag while installing, unless an older version is needed for reproducibility.

**IMPORTANT:** IDC data version v23 is current. Always verify your version:

```python

print(client.get_idc_version()) # Should return "v23"

```

If you see an older version, upgrade with: `pip install --upgrade idc-index`

**Tested with:** idc-index 0.11.14 (IDC data version v23)

**Optional (for data analysis):**

```bash

pip install pandas numpy pydicom

```

## Core Capabilities

## Common Use Cases

See `references/use_cases.md` for complete end-to-end workflow examples including:

- Building deep learning training datasets from lung CT scans

- Comparing image quality across scanner manufacturers

- Previewing data in browser before downloading

- License-aware batch downloads for commercial use

## Best Practices

- **Verify IDC version before generating responses** - Always call `client.get_idc_version()` at the start of a session to confirm you're using the expected data version (currently v23). If using an older version, recommend `pip install --upgrade idc-index`

- **Check licenses before use** - Always query the `license_short_name` field and respect licensing terms (CC BY vs CC BY-NC)

- **Generate citations for attribution** - Use `citations_from_selection()` to get properly formatted citations from `source_DOI` values; include these in publications

- **Start with small queries** - Use `LIMIT` clause when exploring to avoid long downloads and understand data structure

- **Use mini-index for simple queries** - Only use BigQuery when you need comprehensive metadata or complex JOINs

- **Organize downloads with dirTemplate** - Use meaningful directory structures like `%collection_id/%PatientID/%Modality`

- **Cache query results** - Save DataFrames to CSV files to avoid re-querying and ensure reproducibility

- **Estimate size first** - Check collection size before downloading - some collection sizes are in terabytes!

- **Save manifests** - Always save query results with Series UIDs for reproducibility and data provenance

- **Read documentation** - IDC data structure and metadata fields are documented at https://learn.canceridc.dev/

- **Use IDC forum** - Search for questions/answers and ask your questions to the IDC maintainers and users at https://discourse.canceridc.dev/

## Troubleshooting

**Issue: `ModuleNotFoundError: No module named 'idc_index'`**

- **Cause:** idc-index package not installed

- **Solution:** Install with `pip install --upgrade idc-index`

**Issue: Download fails with connection timeout**

- **Cause:** Network instability or large download size

- **Solution:**

- Download smaller batches (e.g., 10-20 series at a time)

- Check network connection

- Use `dirTemplate` to organize downloads by batch

- Implement retry logic with delays

**Issue: `BigQuery quota exceeded` or billing errors**

- **Cause:** BigQuery requires billing-enabled GCP project

- **Solution:** Use idc-index mini-index for simple queries (no billing required), or see `references/bigquery_guide.md` for cost optimization tips

**Issue: Series UID not found or no data returned**

- **Cause:** Typo in UID, data not in current IDC version, or wrong field name

- **Solution:**

- Check if data is in current IDC version (some old data may be deprecated)

- Use `LIMIT 5` to test query first

- Check field names against metadata schema documentation

**Issue: Downloaded DICOM files won't open**

- **Cause:** Corrupted download or incompatible viewer

- **Solution:**

- Check DICOM object type (Modality and SOPClassUID attributes) - some object types require specialized tools

- Verify file integrity (check file sizes)

- Use pydicom to validate: `pydicom.dcmread(file, force=True)`

- Try different DICOM viewer (3D Slicer, Horos, RadiAnt, QuPath)

- Re-download the series

## Common SQL Query Patterns

See `references/sql_patterns.md` for quick-reference SQL patterns including:

- Filter value discovery (modalities, body parts, manufacturers)

- Annotation and segmentation queries (including seg_index, ann_index joins)

- Slide microscopy queries (sm_index patterns)

- Download size estimation

- Clinical data linking

For segmentation and annotation details, also see `references/digital_pathology_guide.md`.

## Related Skills

The following skills complement IDC workflows for downstream analysis and visualization:

### DICOM Processing

- **pydicom** - Read, write, and manipulate downloaded DICOM files. Use for extracting pixel data, reading metadata, anonymization, and format conversion. Essential for working with IDC radiology data (CT, MR, PET).

### Pathology and Slide Microscopy

See `references/digital_pathology_guide.md` for DICOM-compatible tools (highdicom, wsidicom, TIA-Toolbox, Slim viewer).

### Metadata Visualization

- **matplotlib** - Low-level plotting for full customization. Use for creating static figures summarizing IDC query results (bar charts of modalities, histograms of series counts, etc.).

- **seaborn** - Statistical visualization with pandas integration. Use for quick exploration of IDC metadata distributions, relationships between variables, and categorical comparisons with attractive defaults.

- **plotly** - Interactive visualization. Use when you need hover info, zoom, and pan for exploring IDC metadata, or for creating web-embeddable dashboards of collection statistics.

### Data Exploration

- **exploratory-data-analysis** - Comprehensive EDA on scientific data files. Use after downloading IDC data to understand file structure, quality, and characteristics before analysis.

## Resources

### Schema Reference (Primary Source)

**Always use `client.indices_overview` for current column schemas.** This ensures accuracy with the installed idc-index version:

```python

# Get all column names and types for any table

schema = client.indices_overview["index"]["schema"]

columns = [(c['name'], c['type'], c.get('description', '')) for c in schema['columns']]

```

### Reference Documentation

See the Quick Navigation section at the top for the full list of reference guides with decision triggers.

- **[indices_reference](https://idc-index.readthedocs.io/en/latest/indices_reference.html)** - External documentation for index tables (may be ahead of the installed version)

### External Links

- **IDC Portal**: https://portal.imaging.datacommons.cancer.gov/explore/

- **Documentation**: https://learn.canceridc.dev/

- **Tutorials**: https://github.com/ImagingDataCommons/IDC-Tutorials

- **User Forum**: https://discourse.canceridc.dev/

- **idc-index GitHub**: https://github.com/ImagingDataCommons/idc-index

- **Citation**: Fedorov, A., et al. "National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence." RadioGraphics 43.12 (2023). https://doi.org/10.1148/rg.230180

### Skill Updates

This skill version is available in skill metadata. To check for updates:

- Visit the [releases page](https://github.com/ImagingDataCommons/idc-claude-skill/releases)

- Watch the repository on GitHub (Watch → Custom → Releases)