DICOMweb Guide for IDC

IDC provides DICOMweb access through Google Cloud Healthcare API DICOM stores. This guide covers the implementation specifics and usage patterns.

When to Use DICOMweb

Use DICOMweb when you need:

Integration with PACS systems or DICOMweb-compatible tools
Streaming metadata without downloading full files
Building custom viewers or web applications
Using existing DICOMweb client libraries (OHIF, dicomweb-client, etc.)

For most use cases, idc-index is simpler and recommended. Use DICOMweb when you specifically need the DICOMweb protocol.

Endpoints

Public Proxy (No Authentication)

https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb

100% data coverage - Contains all IDC data from all storage buckets
Points to the latest IDC version automatically
Updates immediately on new IDC releases
Per-IP daily quota (suitable for testing and moderate use)
No authentication required
Read-only access
Note: "viewer-only-no-downloads" in URL is legacy naming with no functional meaning

Google Healthcare API (Requires Authentication)

https://healthcare.googleapis.com/v1/projects/nci-idc-data/locations/us-central1/datasets/idc/dicomStores/idc-store-v{VERSION}/dicomWeb

Replace {VERSION} with the IDC release number. To find the current version:

python

from idc_index import IDCClient
client = IDCClient()
print(client.get_idc_version())  # e.g., "23" for v23

~96% data coverage - Only replicates data from idc-open-data bucket (missing ~4% from other buckets)
Updates 1-2 weeks after IDC releases
Requires authentication and provides higher quotas
Better performance (no proxy routing)
Each release gets a new versioned store

See Content Coverage Differences and Authentication sections below.

Content Coverage Differences

Important: The two DICOMweb endpoints have different data coverage. The IDC public proxy contains MORE data than the authenticated Google Healthcare endpoint.

Coverage Summary

Endpoint	Coverage	Missing Data
IDC Public Proxy	100%	None
Google Healthcare API	~96%	~4% (two buckets not replicated)

What's Missing from Google Healthcare?

The Google Healthcare DICOM store only replicates data from the idc-open-data S3 bucket. It does not include data from two additional buckets:

idc-open-data-cr
idc-open-data-two

These missing buckets typically contain several thousand series each, representing approximately 4% of total IDC data. The exact counts vary by IDC version.

See cloud_storage_guide.md for details on bucket organization, file structure, and direct access methods.

Update Timing

IDC Public Proxy: Updates immediately when new IDC versions are released
Google Healthcare: Updates 1-2 weeks after each new IDC version release

Between releases, both endpoints remain current. The 1-2 week delay only occurs during the transition period after a new IDC version is published.

Warning from IDC documentation: "Google-hosted DICOM store may not contain the latest version of IDC data!" - Check during the weeks following a new release.

Choosing the Right Endpoint

Use IDC Public Proxy when:

You need complete data coverage (100%)
You need the absolute latest data immediately after a new version release
You don't want to set up GCP authentication
Your usage fits within per-IP quotas (can request increases via [email protected])
You're accessing slide microscopy images frame-by-frame

Use Google Healthcare API when:

The ~4% missing data doesn't affect your use case
You need higher quotas for heavy usage
You want better performance (direct access, no proxy routing)

Checking Your Data Availability

Before choosing an endpoint, verify whether your data might be in the missing buckets:

python

from idc_index import IDCClient

client = IDCClient()

# Check which buckets contain your collection's data
results = client.sql_query("""
    SELECT series_aws_url, COUNT(*) as series_count
    FROM index
    WHERE collection_id = 'your_collection_id'
    GROUP BY series_aws_url
""")

print(results)

# Look for URLs containing 'idc-open-data-cr' or 'idc-open-data-two'
# If present, that data won't be available in Google Healthcare endpoint

Implementation Details

IDC DICOMweb is provided through Google Cloud Healthcare API DICOM stores. The implementation follows DICOM PS3.18 Web Services with specific characteristics documented in the Google Healthcare DICOM conformance statement.

Supported Operations

Service	Description	Supported
QIDO-RS	Search for DICOM objects	Yes
WADO-RS	Retrieve DICOM objects and metadata	Yes
STOW-RS	Store DICOM objects	No (IDC is read-only)

Not supported: URI Service, Worklist Service, Non-Patient Instance Service, Capabilities Transactions

Searchable DICOM Tags (QIDO-RS)

The implementation supports a limited set of searchable tags:

Level	Searchable Tags
Study	StudyInstanceUID, PatientName, PatientID, AccessionNumber, ReferringPhysicianName, StudyDate
Series	All study tags + SeriesInstanceUID, Modality
Instance	All series tags + SOPInstanceUID

Important: Only exact matching is supported, except for:

StudyDate: supports range queries
PatientName: supports fuzzy matching

Query Limitations

Maximum results: 5,000 for studies/series searches; 50,000 for instances
Maximum offset: 1,000,000
DICOM sequence tags larger than ~1 MB are not returned in metadata (BulkDataURI provided instead)

Code Examples

All examples use the public proxy endpoint. For authenticated access to Google Healthcare, see the authentication section.

Finding UIDs with idc-index

Use idc-index to discover data, then use DICOMweb for metadata access:

python

from idc_index import IDCClient

client = IDCClient()

# Find studies of interest
results = client.sql_query("""
    SELECT StudyInstanceUID, SeriesInstanceUID, PatientID, Modality
    FROM index
    WHERE collection_id = 'tcga_luad' AND Modality = 'CT'
    LIMIT 5
""")

# Use these UIDs with DICOMweb
study_uid = results.iloc[0]['StudyInstanceUID']
series_uid = results.iloc[0]['SeriesInstanceUID']
print(f"Study: {study_uid}")
print(f"Series: {series_uid}")

QIDO-RS: Search by UID

python

import requests

base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb"

# Search for a specific study
study_uid = "1.3.6.1.4.1.14519.5.2.1.6450.9002.307623500513044641407722230440"
response = requests.get(
    f"{base_url}/studies",
    params={"StudyInstanceUID": study_uid},
    headers={"Accept": "application/dicom+json"}
)

if response.status_code == 200:
    studies = response.json()
    print(f"Found {len(studies)} study")

QIDO-RS: List Series in a Study

python

import requests

base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb"
study_uid = "1.3.6.1.4.1.14519.5.2.1.6450.9002.307623500513044641407722230440"

response = requests.get(
    f"{base_url}/studies/{study_uid}/series",
    headers={"Accept": "application/dicom+json"}
)

if response.status_code == 200:
    series_list = response.json()
    for series in series_list:
        # DICOM tags are returned as hex codes
        series_uid = series.get("0020000E", {}).get("Value", [None])[0]
        modality = series.get("00080060", {}).get("Value", [None])[0]
        description = series.get("0008103E", {}).get("Value", [""])[0]
        print(f"{modality}: {description}")

QIDO-RS: List Instances in a Series

python

import requests

base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb"
study_uid = "1.3.6.1.4.1.14519.5.2.1.6450.9002.307623500513044641407722230440"
series_uid = "1.3.6.1.4.1.14519.5.2.1.6450.9002.217441095430480124587725641302"

response = requests.get(
    f"{base_url}/studies/{study_uid}/series/{series_uid}/instances",
    params={"limit": 10},
    headers={"Accept": "application/dicom+json"}
)

if response.status_code == 200:
    instances = response.json()
    print(f"Found {len(instances)} instances")
    for inst in instances[:3]:
        sop_uid = inst.get("00080018", {}).get("Value", [None])[0]
        print(f"  SOPInstanceUID: {sop_uid}")

WADO-RS: Retrieve Series Metadata

python

import requests

base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb"
study_uid = "1.3.6.1.4.1.14519.5.2.1.6450.9002.307623500513044641407722230440"
series_uid = "1.3.6.1.4.1.14519.5.2.1.6450.9002.217441095430480124587725641302"

response = requests.get(
    f"{base_url}/studies/{study_uid}/series/{series_uid}/metadata",
    headers={"Accept": "application/dicom+json"}
)

if response.status_code == 200:
    instances = response.json()
    print(f"Retrieved metadata for {len(instances)} instances")

    # Extract image dimensions from first instance
    if instances:
        inst = instances[0]
        rows = inst.get("00280010", {}).get("Value", [None])[0]
        cols = inst.get("00280011", {}).get("Value", [None])[0]
        print(f"Image dimensions: {rows} x {cols}")

Combined Workflow: idc-index Discovery + DICOMweb Metadata

python

from idc_index import IDCClient
import requests

# Use idc-index for efficient discovery
idc = IDCClient()
results = idc.sql_query("""
    SELECT StudyInstanceUID, SeriesInstanceUID, Modality, SeriesDescription
    FROM index
    WHERE collection_id = 'nlst' AND Modality = 'CT'
    LIMIT 1
""")

study_uid = results.iloc[0]['StudyInstanceUID']
series_uid = results.iloc[0]['SeriesInstanceUID']
print(f"Found: {results.iloc[0]['SeriesDescription']}")

# Use DICOMweb to stream metadata without downloading files
base_url = "https://proxy.imaging.datacommons.cancer.gov/current/viewer-only-no-downloads-see-tinyurl-dot-com-slash-3j3d9jyp/dicomWeb"

response = requests.get(
    f"{base_url}/studies/{study_uid}/series/{series_uid}/metadata",
    headers={"Accept": "application/dicom+json"}
)

if response.status_code == 200:
    metadata = response.json()
    print(f"Retrieved metadata for {len(metadata)} instances without downloading files")

Common DICOM Tags Reference

DICOMweb returns tags as hexadecimal codes. Common tags:

Tag	Name	Description
00080018	SOPInstanceUID	Unique instance identifier
00080020	StudyDate	Date study was performed
00080060	Modality	Imaging modality (CT, MR, PT, etc.)
0008103E	SeriesDescription	Description of series
00100020	PatientID	Patient identifier
0020000D	StudyInstanceUID	Unique study identifier
0020000E	SeriesInstanceUID	Unique series identifier
00280010	Rows	Image height in pixels
00280011	Columns	Image width in pixels

Authentication for Google Healthcare API

To use the Google Healthcare endpoint with higher quotas:

python

from google.auth import default
from google.auth.transport.requests import Request
import requests

# Get credentials (requires gcloud auth)
credentials, project = default()
credentials.refresh(Request())

# Build authenticated request
base_url = "https://healthcare.googleapis.com/v1/projects/nci-idc-data/locations/us-central1/datasets/idc/dicomStores/idc-store-v23/dicomWeb"

response = requests.get(
    f"{base_url}/studies",
    params={"limit": 5},
    headers={
        "Authorization": f"Bearer {credentials.token}",
        "Accept": "application/dicom+json"
    }
)

Prerequisites:

Google Cloud SDK installed (gcloud)
Authenticated: gcloud auth application-default login
Account has access to public Google Cloud datasets

Troubleshooting

Issue: 400 Bad Request on search queries

Cause: Using unsupported search parameters. The implementation only supports specific DICOM tags for filtering.
Solution: Use UID-based queries (StudyInstanceUID, SeriesInstanceUID). For filtering by Modality or other attributes, use idc-index to discover UIDs first, then query DICOMweb with specific UIDs.

Issue: 403 Forbidden on Google Healthcare endpoint

Cause: Missing authentication or insufficient permissions
Solution: Run gcloud auth application-default login and ensure your account has access

Issue: 429 Too Many Requests

Cause: Rate limit exceeded
Solution: Add delays between requests, reduce limit values, or use authenticated endpoint for higher quotas

Issue: 204 No Content for valid UIDs

Cause: UID may be from an older IDC version not in current data, or data is in buckets not replicated by Google Healthcare
Solution:
- Verify UID exists using idc-index query first
- Check if data is in idc-open-data-cr or idc-open-data-two buckets (not available in Google Healthcare endpoint)
- Switch to IDC public proxy for 100% coverage
- During new version releases, Google Healthcare may lag 1-2 weeks behind

Issue: Large metadata responses slow to parse

Cause: Series with many instances returns large JSON
Solution: Use limit parameter on instance queries, or query specific instances by SOPInstanceUID

Issue: Response missing expected attributes

Cause: DICOM sequences larger than ~1 MB are excluded from metadata responses
Solution: Retrieve the full DICOM instance using WADO-RS instance retrieval if you need all attributes

Resources

IDC Documentation:

IDC DICOM Stores - Data coverage and bucket details
IDC DICOMweb Access - Endpoint usage and differences
IDC Proxy Policy - Quota policies and usage restrictions
IDC User Guide - Complete documentation

DICOMweb Standards and Tools:

Related Guides:

cloud_storage_guide.md - Direct bucket access, file organization, CRDC UUIDs, and versioning
bigquery_guide.md - Advanced metadata queries with full DICOM attributes