BigQuery Guide for IDC

Reviewed 2026-09-30: current IDC table documentation and Google BigQuery API/cost guidance. The GoogleSQL examples below are illustrative; no authenticated BigQuery jobs were run. Use a dry run against your pinned dataset before executing them.

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 BigQuery access (sandbox supports limited public queries without a billing account; billing applies beyond applicable limits)
3. google-cloud-bigquery[pandas] Python package for .to_dataframe(), or BigQuery console access

Authentication setup:

# Install Google Cloud SDK, then:
gcloud auth application-default login

When to Use BigQuery

Use BigQuery instead of idc-index when you need:

• Instance-level DICOM metadata beyond the compact series-level index; extraction has depth/size limits
• Hosted queries across large clinical/imaging tables (local DuckDB and REST also support clinical joins)
• DICOM sequence attributes (nested structures)
• Queries on fields not in the idc-index mini-index
• Private DICOM elements (vendor-specific tags in OtherElements column)
• Per-segment detail from DICOM Segmentation objects — idc-index seg_index gives series-level metadata, but not per-segment rows/number associations; use the segmentations table when those details are needed
• Quantitative measurements from DICOM SR — radiomics features (volume, diameter, shape descriptors) without downloading and parsing SR files; no idc-index equivalent
• Qualitative measurements from DICOM SR — coded evaluations (malignancy rating, texture, margin) without parsing SR files; no idc-index equivalent

Accessing IDC in BigQuery

The same metadata is also exported as public Parquet in idc-open-metadata, including clinical tables and SR measurements. For an unauthenticated alternative see references/parquet_access_guide.md; large instance-level joins may still favor 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 exported DICOM fields plus collection and administrative metadata, one row per instance. See dicom_all.sql for the exact derivation.

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

These tables are derived from DICOM objects (Segmentation and Structured Report) and have no equivalent in idc-index. Use them to query per-segment anatomy, radiomics features, and qualitative assessments without downloading DICOM files.

segmentations — one row per segment within a DICOM SEG object. Lets you search by anatomical structure name or DICOM coded concept. The idc-index seg_index gives series-level metadata; this table gives per-segment detail.

measurement_groups — one row per SR TID1500 measurement group. The parent grouping for quantitative and qualitative measurements; links measurements to segmentations and source images.

quantitative_measurements — one row per numeric measurement within an SR TID1500 group. Contains radiomics features (volume, diameter, shape descriptors, texture) extracted from DICOM SR without downloading or parsing SR files.

qualitative_measurements — one row per coded evaluation within an SR TID1500 group. Contains assessed findings (malignancy likelihood, texture, margin type) using coded concept values.

See the Derived Tables: Detailed Documentation section below for schemas, column descriptions, and query examples.

Collection Metadata

original_collections_metadata - Collection-level descriptions

SELECT
  collection_id,
  cancer_types,
  tumor_locations,
  subjects,
  src.source_doi,
  src.modalities,
  src.license.license_short_name
FROM `bigquery-public-data.idc_current.original_collections_metadata`,
UNNEST(sources) AS src
WHERE cancer_types LIKE '%Lung%'

Common Query Patterns

Find Collections by Criteria

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

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

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

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

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 (SELECT DISTINCT SeriesInstanceUID, collection_id, PatientID, Modality
      FROM `bigquery-public-data.idc_current.dicom_all`) src
  ON seg.segmented_SeriesInstanceUID = src.SeriesInstanceUID
WHERE src.collection_id = 'qin_prostate_repeatability'
LIMIT 10

Derived Tables: Detailed Documentation

dicom_all is instance-level. The examples below project distinct series metadata before joining it to segment/measurement rows, preventing one measurement from being duplicated for every image slice. Retain SOPInstanceUID plus measurementGroup_number as measurement keys, and inspect units and derivation modifiers before comparing quantities.

segmentations

One row per segment within a DICOM Segmentation (SEG) object. Unlike idc-index seg_index (one row per SEG series), this table exposes each labeled region individually so you can search by anatomical structure or finding type.

Key columns:

SegmentedPropertyCategory and SegmentedPropertyType are RECORD types with sub-fields CodeValue, CodingSchemeDesignator, and CodeMeaning. Use .CodeMeaning for human-readable filtering.

idc-index gap: seg_index in idc-index has total_segments, AlgorithmName, and aggregated codes, but does not expose individual segment anatomy per row. Use this table when segment-level details are required. For screening series that contain a structure, the aggregated SegmentedPropertyType_CodeMeanings array in seg_index can suffice.

Discover what structures are segmented across IDC:

SELECT
  SegmentedPropertyCategory.CodeMeaning AS category,
  SegmentedPropertyType.CodeMeaning AS structure,
  SegmentAlgorithmType,
  COUNT(DISTINCT SeriesInstanceUID) AS seg_series_count
FROM `bigquery-public-data.idc_current.segmentations`
GROUP BY 1, 2, 3
ORDER BY seg_series_count DESC
LIMIT 20

Find all SEG series containing a specific structure, with source image context:

SELECT
  seg.SeriesInstanceUID AS seg_series,
  seg.SegmentNumber,
  seg.SegmentedPropertyType.CodeMeaning AS structure,
  seg.SegmentAlgorithmType,
  seg.SegmentAlgorithmName,
  img.collection_id,
  img.PatientID,
  img.Modality,
  seg.viewer_url
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN (SELECT DISTINCT SeriesInstanceUID, collection_id, PatientID, Modality
      FROM `bigquery-public-data.idc_current.dicom_all`) img
  ON seg.segmented_SeriesInstanceUID = img.SeriesInstanceUID
WHERE seg.SegmentedPropertyType.CodeMeaning = 'Liver'
  AND seg.SegmentAlgorithmType = 'AUTOMATIC'
LIMIT 20

Find all segment types present in a collection:

SELECT
  seg.SegmentedPropertyType.CodeMeaning AS structure,
  seg.SegmentAlgorithmType,
  COUNT(DISTINCT seg.SeriesInstanceUID) AS seg_series_count
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN (SELECT DISTINCT SeriesInstanceUID, collection_id, PatientID, Modality
      FROM `bigquery-public-data.idc_current.dicom_all`) img
  ON seg.segmented_SeriesInstanceUID = img.SeriesInstanceUID
WHERE img.collection_id = 'nlst'
GROUP BY 1, 2
ORDER BY seg_series_count DESC

Link segments to SR measurements using the referenced SEG instance and segment number:

-- Find segments that have corresponding SR measurements
SELECT
  seg.SeriesInstanceUID AS seg_series,
  seg.SegmentNumber,
  seg.SegmentedPropertyType.CodeMeaning AS structure,
  qm.Quantity.CodeMeaning AS measurement,
  ROUND(CAST(qm.Value AS FLOAT64), 2) AS value,
  qm.Units.CodeMeaning AS units
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN `bigquery-public-data.idc_current.quantitative_measurements` qm
  ON seg.SOPInstanceUID = qm.segmentationInstanceUID
  AND seg.SegmentNumber = qm.segmentationSegmentNumber
WHERE seg.SegmentedPropertyType.CodeMeaning = 'Neoplasm'
  AND qm.Quantity.CodeMeaning = 'Volume from Voxel Summation'
LIMIT 10

quantitative_measurements

One row per numeric measurement in a DICOM SR TID1500 Measurement Report. Contains radiomics features (shape, intensity, texture) and clinical measurements (volume, diameter, SUV). These measurements are pre-extracted from SR — no download or DICOM parsing needed.

No compact idc-index equivalent. Available in BigQuery and its public Parquet export.

Key columns:

Discover available measurement types:

SELECT
  Quantity.CodeMeaning AS measurement,
  Units.CodeMeaning AS units,
  COUNT(*) AS measurement_count,
  COUNT(DISTINCT SeriesInstanceUID) AS sr_series_count
FROM `bigquery-public-data.idc_current.quantitative_measurements`
GROUP BY 1, 2
ORDER BY measurement_count DESC
LIMIT 20

Query measurements for a specific structure (e.g., liver volume across collections):

SELECT
  qm.PatientID,
  ROUND(CAST(qm.Value AS FLOAT64) / 1000, 1) AS volume_cm3,
  img.collection_id,
  qm.segmentationSeriesUID
FROM `bigquery-public-data.idc_current.quantitative_measurements` qm
JOIN (SELECT DISTINCT SeriesInstanceUID, collection_id, PatientID, Modality
      FROM `bigquery-public-data.idc_current.dicom_all`) img
  ON qm.sourceSegmentedSeriesUID = img.SeriesInstanceUID
WHERE qm.Quantity.CodeMeaning = 'Volume from Voxel Summation'
  AND qm.findingSite.CodeMeaning = 'Liver'
  AND qm.Units.CodingSchemeDesignator = 'UCUM'
  AND qm.Units.CodeValue = 'mm3'
ORDER BY volume_cm3 DESC
LIMIT 20

Retrieve all measurements for a specific patient and finding:

SELECT
  qm.measurementGroup_number,
  qm.finding.CodeMeaning AS finding,
  qm.findingSite.CodeMeaning AS finding_site,
  qm.lateralityModifier.CodeMeaning AS laterality,
  qm.Quantity.CodeMeaning AS feature,
  ROUND(CAST(qm.Value AS FLOAT64), 3) AS value,
  qm.Units.CodeMeaning AS units
FROM `bigquery-public-data.idc_current.quantitative_measurements` qm
WHERE qm.PatientID = 'LIDC-IDRI-0001'
  AND qm.finding.CodeMeaning = 'Nodule'
ORDER BY qm.measurementGroup_number, qm.Quantity.CodeMeaning

qualitative_measurements

One row per coded evaluation in a DICOM SR TID1500 Measurement Report. Instead of numeric values, these record assessed characteristics using coded concept pairs (e.g., Quantity="Malignancy", Value="4 out of 5 (Moderately Suspicious for Cancer)").

No compact idc-index equivalent. Available in BigQuery and its public Parquet export.

Key columns:

Discover available qualitative features and their values:

SELECT
  Quantity.CodeMeaning AS feature,
  Value.CodeMeaning AS assessed_value,
  finding.CodeMeaning AS finding,
  COUNT(*) AS count
FROM `bigquery-public-data.idc_current.qualitative_measurements`
GROUP BY 1, 2, 3
ORDER BY count DESC
LIMIT 20

Find all nodules with a specific malignancy rating:

SELECT
  qm.PatientID,
  qm.trackingIdentifier AS nodule_id,
  qm.Value.CodeMeaning AS malignancy_rating,
  img.collection_id
FROM `bigquery-public-data.idc_current.qualitative_measurements` qm
JOIN (SELECT DISTINCT SeriesInstanceUID, collection_id, PatientID, Modality
      FROM `bigquery-public-data.idc_current.dicom_all`) img
  ON qm.SeriesInstanceUID = img.SeriesInstanceUID
WHERE qm.Quantity.CodeMeaning = 'Malignancy'
  AND qm.Value.CodeMeaning LIKE '%Suspicious%'
ORDER BY qm.PatientID
LIMIT 20

measurement_groups

The parent table for TID1500 measurement groups. Each row represents one measurement group within an SR, with references to the segmentation and source image but without the individual measurement values. Use this table when you need to enumerate groups or check what was tracked, without pulling all measurement values.

Key columns: SOPInstanceUID, SeriesInstanceUID, PatientID, measurementGroup_number, trackingIdentifier, trackingUniqueIdentifier, finding, findingSite, segmentationInstanceUID, segmentationSeriesUID, segmentationSegmentNumber, sourceSegmentedSeriesUID, contentSequence (raw SR content sequence).

In most workflows, join quantitative_measurements and qualitative_measurements directly using SOPInstanceUID + measurementGroup_number rather than going through measurement_groups.

Combining quantitative and qualitative measurements

The primary use case requiring both tables: correlate numeric features (volume, diameter) with coded assessments (malignancy, texture) for the same finding. Join on SOPInstanceUID + measurementGroup_number.

Example: LIDC-IDRI lung nodule analysis — malignancy rating with volume and diameter:

SELECT
  qual.PatientID,
  qual.trackingIdentifier AS nodule_id,
  qual.Value.CodeMeaning AS malignancy_rating,
  ROUND(CAST(vol.Value AS FLOAT64), 1) AS volume_mm3,
  ROUND(CAST(diam.Value AS FLOAT64), 1) AS diameter_mm
FROM `bigquery-public-data.idc_current.qualitative_measurements` qual
JOIN `bigquery-public-data.idc_current.quantitative_measurements` vol
  ON qual.SOPInstanceUID = vol.SOPInstanceUID
  AND qual.measurementGroup_number = vol.measurementGroup_number
JOIN `bigquery-public-data.idc_current.quantitative_measurements` diam
  ON qual.SOPInstanceUID = diam.SOPInstanceUID
  AND qual.measurementGroup_number = diam.measurementGroup_number
WHERE qual.Quantity.CodeMeaning = 'Malignancy'
  AND vol.Quantity.CodeMeaning = 'Volume'
  AND diam.Quantity.CodeMeaning = 'Diameter'
ORDER BY qual.PatientID, qual.trackingIdentifier
LIMIT 20

Joining all three derived tables to get full segment context:

SELECT
  seg.SegmentedPropertyType.CodeMeaning AS structure,
  qual.Quantity.CodeMeaning AS qualitative_feature,
  qual.Value.CodeMeaning AS qualitative_value,
  qm.Quantity.CodeMeaning AS quantitative_feature,
  ROUND(CAST(qm.Value AS FLOAT64), 3) AS numeric_value,
  qm.Units.CodeMeaning AS units,
  img.collection_id
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN `bigquery-public-data.idc_current.qualitative_measurements` qual
  ON seg.SOPInstanceUID = qual.segmentationInstanceUID
  AND seg.SegmentNumber = qual.segmentationSegmentNumber
JOIN `bigquery-public-data.idc_current.quantitative_measurements` qm
  ON qual.SOPInstanceUID = qm.SOPInstanceUID
  AND qual.measurementGroup_number = qm.measurementGroup_number
JOIN (SELECT DISTINCT SeriesInstanceUID, collection_id, PatientID, Modality
      FROM `bigquery-public-data.idc_current.dicom_all`) img
  ON seg.segmented_SeriesInstanceUID = img.SeriesInstanceUID
WHERE seg.SegmentedPropertyType.CodeMeaning = 'Neoplasm'
LIMIT 10

Private DICOM Elements

Private DICOM elements are vendor-specific attributes not defined in the DICOM standard. They often contain essential acquisition parameters (like diffusion b-values, gradient directions, or scanner-specific settings) that are critical for image interpretation and analysis.

Understanding Private Elements

How private elements work:

• Private elements use odd-numbered group numbers (e.g., 0019, 0043, 2001)
• Each vendor reserves blocks of 256 elements using Private Creator identifiers at positions (gggg,0010-00FF)
• For example, GE uses Private Creator "GEMS_PARM_01" at (0043,0010) to reserve elements (0043,1000-10FF)

Standard vs. private tags: Some parameters exist in both forms:

Older scanners typically populate only private tags; newer scanners may use standard tags. Always check both.

Challenges with private elements:

• Require manufacturer DICOM Conformance Statements to interpret
• Tag meanings can change between software versions
• May be removed during de-identification for HIPAA compliance
• Value encoding varies (string vs. numeric, different units)

Accessing Private Elements in BigQuery

Private elements are stored in the OtherElements column of dicom_all as an array of structs with Tag and Data fields.

Tag notation: DICOM notation (0043,1039) becomes BigQuery format Tag_00431039.

Private Element Query Patterns

Discover Available Private Tags

List all non-empty private tags for a collection:

SELECT
  other_elements.Tag,
  COUNT(*) AS instance_count,
  ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)] IGNORE NULLS LIMIT 5) AS sample_values
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE collection_id = 'qin_prostate_repeatability'
  AND Modality = 'MR'
  AND ARRAY_LENGTH(other_elements.Data) > 0
  AND other_elements.Data[SAFE_OFFSET(0)] IS NOT NULL
  AND other_elements.Data[SAFE_OFFSET(0)] != ''
GROUP BY other_elements.Tag
ORDER BY instance_count DESC

For a specific series:

SELECT
  other_elements.Tag,
  ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)] IGNORE NULLS) AS values
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE SeriesInstanceUID = '1.3.6.1.4.1.14519.5.2.1.7311.5101.206828891270520544417996275680'
  AND ARRAY_LENGTH(other_elements.Data) > 0
  AND other_elements.Data[SAFE_OFFSET(0)] IS NOT NULL
  AND other_elements.Data[SAFE_OFFSET(0)] != ''
GROUP BY other_elements.Tag

To identify the Private Creator for a tag, look for the reservation element in the same group. For example, if you find Tag_00431039, the Private Creator is at Tag_00430010 (the tag that reserves block 10xx in group 0043).

Identify Equipment Manufacturer

Determine what equipment produced the data to find the correct DICOM Conformance Statement:

SELECT DISTINCT Manufacturer, ManufacturerModelName
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE collection_id = 'qin_prostate_repeatability'
  AND Modality = 'MR'

Access Private Element Values

Use UNNEST to access individual private elements:

SELECT
  SeriesInstanceUID,
  SeriesDescription,
  other_elements.Data[SAFE_OFFSET(0)] AS b_value
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE collection_id = 'qin_prostate_repeatability'
  AND other_elements.Tag = 'Tag_00431039'
LIMIT 10

Aggregate Values by Series

Collect all unique values across slices in a series:

SELECT
  SeriesInstanceUID,
  ANY_VALUE(SeriesDescription) AS SeriesDescription,
  ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)]) AS b_values
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE collection_id = 'qin_prostate_repeatability'
  AND other_elements.Tag = 'Tag_00431039'
GROUP BY SeriesInstanceUID

Combine Standard and Private Filters

Filter using both standard DICOM attributes and private element values:

SELECT
  PatientID,
  SeriesInstanceUID,
  ANY_VALUE(SeriesDescription) AS SeriesDescription,
  ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)]) AS b_values,
  COUNT(DISTINCT SOPInstanceUID) AS n_slices
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE collection_id = 'qin_prostate_repeatability'
  AND Modality = 'MR'
  AND other_elements.Tag = 'Tag_00431039'
  AND ImageType[SAFE_OFFSET(0)] = 'ORIGINAL'
  AND other_elements.Data[SAFE_OFFSET(0)] = '1400'
GROUP BY PatientID, SeriesInstanceUID
ORDER BY PatientID

Cross-Collection Analysis

Survey usage of a private tag across all IDC collections:

SELECT
  collection_id,
  ARRAY_TO_STRING(ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)] IGNORE NULLS), ', ') AS values_found,
  ARRAY_AGG(DISTINCT Manufacturer IGNORE NULLS) AS manufacturers
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE other_elements.Tag = 'Tag_00431039'
  AND other_elements.Data[SAFE_OFFSET(0)] IS NOT NULL
  AND other_elements.Data[SAFE_OFFSET(0)] != ''
GROUP BY collection_id
ORDER BY collection_id

Workflow: Finding and Using Private Tags

1. Discover available private tags in your collection using the discovery query above
2. Identify the manufacturer to know which conformance statement to consult
3. Find the DICOM Conformance Statement from the manufacturer's website (see Resources below)
4. Search the conformance statement for the parameter you need (e.g., "b_value", "gradient") to understand what each tag contains
5. Convert tag to BigQuery format: (gggg,eeee) → Tag_ggggeeee
6. Query and verify results visually in the IDC Viewer

Data Quality Notes

• Some private tags encode packed values (e.g., GE diffusion fields); values such as "1000000600" are not directly interpretable as b-values without the vendor convention
• IDC data is de-identified; private tags containing PHI may have been removed or modified
• The same tag may have different meanings across software versions
• Inspect source headers and vendor documentation to validate private-tag interpretation; the IDC Viewer provides complementary image QA

Private Element Resources

Manufacturer DICOM Conformance Statements:

• GE Healthcare MR
• Siemens MR
• Siemens CT

DICOM Standard:

• Part 5 Section 7.8 - Private Data Elements
• Part 15 Appendix E - De-identification Profiles

Community Resources:

• NAMIC Wiki: DWI/DTI DICOM - comprehensive vendor comparison for diffusion imaging (archived; original NA-MIC wiki retired)
• StandardizeBValue - tool to extract vendor b-values to standard tags

Using Query Results with idc-index

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

from google.cloud import bigquery
from idc_index import IDCClient

# Initialize BigQuery client
# Requires: the Python google-cloud-bigquery[pandas] package
# Auth: gcloud auth application-default login
# Project: executes the query and supplies its quota/billing context
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: On-demand analysis is priced per TiB scanned; the reviewed US rate is $6.25/TiB with the first 1 TiB/month free. Verify the region and current BigQuery pricing before estimating a job.

Minimize data scanned:

• Select only needed columns (not SELECT *)
• Use partition/cluster filters when the table layout supports them
• Use LIMIT to bound returned rows; it generally does not bound scanned bytes
• Do not assume dicom_all is smaller than dicom_metadata; compare dry-run estimates
• Preview queries in BQ console (free, shows bytes to scan)

Check cost before running:

query_job = bq_client.query(
    query, job_config=bigquery.QueryJobConfig(dry_run=True, use_query_cache=False)
)
print(f"Query will scan {query_job.total_bytes_processed / 1e9:.2f} GB")

For execution, set maximum_bytes_billed in QueryJobConfig to your budget in bytes; BigQuery refuses a job estimated to exceed it. See estimate and control costs.

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. All clinical data available via idc-index is also available in BigQuery, with the same content and structure. Use BigQuery when you need complex cross-collection queries or joins that aren't possible with the local idc-index tables.

Datasets:

• bigquery-public-data.idc_current_clinical - current release (for exploration)
• bigquery-public-data.idc_v{version}_clinical - versioned datasets (for reproducibility)

Discover the current clinical table inventory rather than relying on a fixed count. Not all collections have clinical data (started in IDC v11).

Clinical Table Naming

Most collections use a single table: <collection_id>_clinical

Exception: ACRIN collections use multiple tables for different data types (e.g., acrin_6698_A0, acrin_6698_A1, etc.).

Metadata Tables

Two metadata tables help navigate clinical data:

table_metadata - Collection-level information:

SELECT
  collection_id,
  table_name,
  table_description
FROM `bigquery-public-data.idc_current_clinical.table_metadata`
WHERE collection_id = 'nlst'

column_metadata - Attribute-level details with value mappings:

SELECT
  collection_id,
  table_name,
  column,
  column_label,
  data_type,
  values
FROM `bigquery-public-data.idc_current_clinical.column_metadata`
WHERE collection_id = 'nlst'
  AND column_label LIKE '%stage%'

The values field contains observed attribute values with their descriptions (same as in idc-index clinical_index).

Common Clinical Queries

List available clinical tables:

SELECT table_name
FROM `bigquery-public-data.idc_current_clinical.INFORMATION_SCHEMA.TABLES`
WHERE table_name NOT IN ('table_metadata', 'column_metadata')

Find collections with specific clinical attributes:

SELECT DISTINCT collection_id, table_name, column, column_label
FROM `bigquery-public-data.idc_current_clinical.column_metadata`
WHERE LOWER(column_label) LIKE '%chemotherapy%'

Query clinical data for a collection:

-- Example: NLST cancer staging data
SELECT
  dicom_patient_id,
  clinical_stag,
  path_stag,
  de_stag
FROM `bigquery-public-data.idc_current_clinical.nlst_canc`
WHERE clinical_stag IS NOT NULL
LIMIT 10

Join clinical with imaging data:

SELECT
  d.PatientID,
  d.StudyInstanceUID,
  d.Modality,
  c.clinical_stag,
  c.path_stag
FROM (SELECT DISTINCT collection_id, PatientID, StudyInstanceUID, Modality
      FROM `bigquery-public-data.idc_current.dicom_all`) d
JOIN `bigquery-public-data.idc_current_clinical.nlst_canc` c
  ON d.PatientID = c.dicom_patient_id
WHERE d.collection_id = 'nlst'
  AND d.Modality = 'CT'
  AND c.clinical_stag = '400'  -- Stage IV
LIMIT 20

Cross-collection clinical search:

-- Find all collections with staging information
SELECT
  cm.collection_id,
  cm.table_name,
  cm.column,
  cm.column_label
FROM `bigquery-public-data.idc_current_clinical.column_metadata` cm
WHERE LOWER(cm.column_label) LIKE '%stage%'
ORDER BY cm.collection_id

Key Column: dicom_patient_id

Every clinical table includes dicom_patient_id, which matches the DICOM PatientID attribute in imaging tables. This is the join key between clinical and imaging data.

Note: Clinical table schemas vary significantly by collection. Always check available columns first:

SELECT column_name, data_type
FROM `bigquery-public-data.idc_current_clinical.INFORMATION_SCHEMA.COLUMNS`
WHERE table_name = 'nlst_canc'

See references/clinical_data_guide.md for detailed workflows using idc-index, which provides the same clinical data without requiring BigQuery authentication.

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: The requested operation exceeds sandbox capabilities or needs billing configuration
• Solution: Check sandbox limits and project configuration, enable billing when needed, or use idc-index/public Parquet exports

Issue: Query exceeds resource limits

• Cause: Query scans too much data or is too complex
• Solution: Inspect dry-run bytes and the plan, use applicable partition/cluster filters, and break into smaller queries; LIMIT alone is not a scan-cost control

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
• BigQuery Query Syntax
• Kaggle Intro to SQL
• Sample BigQuery queries of IDC data