# 104 Bigquery Guide Bea3f426

> BigQuery Guide for IDC

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- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/104-bigquery-guide-bea3f426

---

# BigQuery Guide for IDC

**Tested with:** IDC data version v23

For most queries and downloads, use `idc-index` (see main SKILL.md). This guide covers BigQuery for advanced use cases requiring full DICOM metadata or complex joins.

## Prerequisites

**Requirements:**
1. Google account
2. Google Cloud project with billing enabled (first 1 TB/month free)
3. `google-cloud-bigquery` Python package or BigQuery console access

**Authentication setup:**
```bash
# Install Google Cloud SDK, then:
gcloud auth application-default login
```

## When to Use BigQuery

Use BigQuery instead of `idc-index` when you need:
- Full DICOM metadata (all 4000+ tags, not just the ~50 in idc-index)
- Complex joins across clinical data tables
- DICOM sequence attributes (nested structures)
- Queries on fields not in the idc-index mini-index

## Accessing IDC in BigQuery

### Dataset Structure

All IDC tables are in the `bigquery-public-data` BigQuery project.

**Current version (recommended for exploration):**
- `bigquery-public-data.idc_current.*`
- `bigquery-public-data.idc_current_clinical.*`

**Versioned datasets (recommended for reproducibility):**

- `bigquery-public-data.idc_v{IDC version}.*`
- `bigquery-public-data.idc_v{IDC version}_clinical.*`

Always use versioned datasets for reproducible research!

## Key Tables

### dicom_all
Primary table joining complete DICOM metadata with IDC-specific columns (collection_id, gcs_url, license). Contains all DICOM tags from `dicom_metadata` plus collection and administrative metadata. See [dicom_all.sql](https://github.com/ImagingDataCommons/etl_flow/blob/master/bq/generate_tables_and_views/derived_tables/BQ_Table_Building/derived_data_views/sql/dicom_all.sql) for the exact derivation.

```sql
SELECT 
  collection_id,
  PatientID,
  StudyInstanceUID, 
  SeriesInstanceUID,
  Modality,
  BodyPartExamined,
  SeriesDescription,
  gcs_url,
  license_short_name
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE Modality = 'CT'
  AND BodyPartExamined = 'CHEST'
LIMIT 10
```

### Derived Tables

**segmentations** - DICOM Segmentation objects
```sql
SELECT *
FROM `bigquery-public-data.idc_current.segmentations`
LIMIT 10
```

**measurement_groups** - SR TID1500 measurement groups
**qualitative_measurements** - Coded evaluations
**quantitative_measurements** - Numeric measurements

### Collection Metadata

**original_collections_metadata** - Collection-level descriptions

```sql
SELECT
  collection_id,
  CancerTypes,
  TumorLocations,
  Subjects,
  src.source_doi,
  src.ImageTypes,
  src.license.license_short_name
FROM `bigquery-public-data.idc_current.original_collections_metadata`,
UNNEST(Sources) AS src
WHERE CancerTypes LIKE '%Lung%'
```

## Common Query Patterns

### Find Collections by Criteria

```sql
SELECT 
  collection_id,
  COUNT(DISTINCT PatientID) as patient_count,
  COUNT(DISTINCT SeriesInstanceUID) as series_count,
  ARRAY_AGG(DISTINCT Modality) as modalities
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE BodyPartExamined LIKE '%BRAIN%'
GROUP BY collection_id
HAVING patient_count > 50
ORDER BY patient_count DESC
```

### Get Download URLs

```sql
SELECT
  SeriesInstanceUID,
  gcs_url
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE collection_id = 'rider_pilot'
  AND Modality = 'CT'
```

### Find Studies with Multiple Modalities

```sql
SELECT
  StudyInstanceUID,
  ARRAY_AGG(DISTINCT Modality) as modalities,
  COUNT(DISTINCT SeriesInstanceUID) as series_count
FROM `bigquery-public-data.idc_current.dicom_all`
GROUP BY StudyInstanceUID
HAVING ARRAY_LENGTH(ARRAY_AGG(DISTINCT Modality)) > 1
LIMIT 100
```

### License Filtering

```sql
SELECT
  collection_id,
  license_short_name,
  COUNT(*) as instance_count
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE license_short_name = 'CC BY 4.0'
GROUP BY collection_id, license_short_name
```

### Find Segmentations with Source Images

```sql
SELECT
  src.collection_id,
  seg.SeriesInstanceUID as seg_series,
  seg.SegmentedPropertyType,
  src.SeriesInstanceUID as source_series,
  src.Modality as source_modality
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN `bigquery-public-data.idc_current.dicom_all` src
  ON seg.segmented_SeriesInstanceUID = src.SeriesInstanceUID
WHERE src.collection_id = 'qin_prostate_repeatability'
LIMIT 10
```

## Using Query Results with idc-index

Combine BigQuery for complex queries with idc-index for downloads (no GCP auth needed for downloads):

```python
from google.cloud import bigquery
from idc_index import IDCClient

# Initialize BigQuery client
# Requires: pip install google-cloud-bigquery
# Auth: gcloud auth application-default login
# Project: needed for billing even on public datasets (free tier applies)
bq_client = bigquery.Client(project="your-gcp-project-id")

# Query for series with specific criteria
query = """
SELECT DISTINCT SeriesInstanceUID
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE collection_id = 'tcga_luad'
  AND Modality = 'CT'
  AND Manufacturer = 'GE MEDICAL SYSTEMS'
LIMIT 100
"""

df = bq_client.query(query).to_dataframe()
print(f"Found {len(df)} GE CT series")

# Download with idc-index (no GCP auth required)
idc_client = IDCClient()
idc_client.download_from_selection(
    seriesInstanceUID=list(df['SeriesInstanceUID'].values),
    downloadDir="./tcga_luad_thin_ct"
)
```

## Cost and Optimization

**Pricing:** $5 per TB scanned (first 1 TB/month free). Most users stay within free tier.

**Minimize data scanned:**
- Select only needed columns (not `SELECT *`)
- Filter early with `WHERE` clauses
- Use `LIMIT` when testing
- Use `dicom_all` instead of `dicom_metadata` when possible (smaller)
- Preview queries in BQ console (free, shows bytes to scan)

**Check cost before running:**
```python
query_job = client.query(query, job_config=bigquery.QueryJobConfig(dry_run=True))
print(f"Query will scan {query_job.total_bytes_processed / 1e9:.2f} GB")
```

**Use materialized tables:** IDC provides both views (`table_name_view`) and materialized tables (`table_name`). Always use the materialized tables (faster, lower cost).

## Clinical Data

Clinical data is in separate datasets with collection-specific tables. Not all collections have clinical data (started in IDC v11).

**List available clinical tables:**
```sql
SELECT table_name
FROM `bigquery-public-data.idc_current_clinical.INFORMATION_SCHEMA.TABLES`
```

**Query clinical data for a collection:**
```sql
-- Example: TCGA-LUAD clinical data
SELECT *
FROM `bigquery-public-data.idc_current_clinical.tcga_luad_clinical`
LIMIT 10
```

**Join clinical with imaging data:**
```sql
SELECT
  d.PatientID,
  d.SeriesInstanceUID,
  d.Modality,
  c.age_at_diagnosis,
  c.pathologic_stage
FROM `bigquery-public-data.idc_current.dicom_all` d
JOIN `bigquery-public-data.idc_current_clinical.tcga_luad_clinical` c
  ON d.PatientID = c.dicom_patient_id
WHERE d.collection_id = 'tcga_luad'
  AND d.Modality = 'CT'
LIMIT 20
```

**Note:** Clinical table schemas vary by collection. Check column names with `INFORMATION_SCHEMA.COLUMNS` before querying.

## Important Notes

- Tables are read-only (public dataset)
- Schema changes between IDC versions
- Use versioned datasets for reproducibility
- Some DICOM sequences >15 levels deep are not extracted
- Very large sequences (>1MB) may be truncated
- Always check data license before use

## Common Errors

**Issue: Billing must be enabled**
- Cause: BigQuery requires a billing-enabled GCP project
- Solution: Enable billing in Google Cloud Console or use idc-index mini-index instead

**Issue: Query exceeds resource limits**
- Cause: Query scans too much data or is too complex
- Solution: Add more specific WHERE filters, use LIMIT, break into smaller queries

**Issue: Column not found**
- Cause: Field name typo or not in selected table
- Solution: Check table schema first with `INFORMATION_SCHEMA.COLUMNS`

**Issue: Permission denied**
- Cause: Not authenticated to Google Cloud
- Solution: Run `gcloud auth application-default login` or set GOOGLE_APPLICATION_CREDENTIALS

## Resources

- [Understanding the BigQuery DICOM schema](https://docs.cloud.google.com/healthcare-api/docs/how-tos/dicom-bigquery-schema)
- [BigQuery Query Syntax](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax)
- [Kaggle Intro to SQL](https://www.kaggle.com/learn/intro-to-sql)
- [Sample BigQuery queries of IDC data](https://github.com/ImagingDataCommons/idc-bigquery-cookbook)