BigQuery Guide for IDC

Tested with: idc-index 0.12.1 (IDC data version v24)

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:

Google account
Google Cloud project with billing enabled (first 1 TB/month free)
google-cloud-bigquery Python package 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:

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
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 individual segment anatomy codes; use segmentations BigQuery table to query by structure name
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

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 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,
  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

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 `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

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:

Column	Type	Description
`SeriesInstanceUID`	STRING	SEG series UID
`SOPInstanceUID`	STRING	SEG instance UID
`PatientID`	STRING	Patient identifier
`StudyInstanceUID`	STRING	Study UID
`SegmentNumber`	INTEGER	Segment index within the SEG (starting from 1)
`SegmentedPropertyCategory`	RECORD	Coded category (e.g., "Anatomical Structure", "Morphologically Altered Structure")
`SegmentedPropertyType`	RECORD	Specific structure (e.g., "Liver", "Kidney", "Neoplasm")
`AnatomicRegion`	RECORD	Optional anatomic region modifier
`SegmentAlgorithmType`	STRING	AUTOMATIC, SEMIAUTOMATIC, or MANUAL
`SegmentAlgorithmName`	STRING (REPEATED)	Algorithm name array (e.g., ["TotalSegmentator"])
`TrackingUID`	STRING	Links segment to SR measurements
`TrackingID`	STRING	Human-readable tracking label
`segmented_SeriesInstanceUID`	STRING	Source image series UID — join to `dicom_all` to get collection/modality
`viewer_url`	STRING	Direct IDC viewer link for the SEG

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 BigQuery table when you need to find SEG series that contain a specific structure (e.g., all series with a "Liver" segment).

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 `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 `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 TrackingUID:

-- 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.SeriesInstanceUID = qm.segmentationSeriesUID
  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 idc-index equivalent. This table is only accessible via BigQuery.

Key columns:

Column	Type	Description
`SOPInstanceUID`	STRING	SR instance UID
`SeriesInstanceUID`	STRING	SR series UID — join to `dicom_all` for collection/modality
`SeriesDescription`	STRING	SR series description (e.g., "TotalSegmentator(v1.5.6) shape Measurements")
`PatientID`	STRING	Patient identifier
`measurementGroup_number`	INTEGER	Group index within the SR (0-based); join key with `measurement_groups` and `qualitative_measurements`
`Quantity`	RECORD	What was measured — `CodeValue`, `CodingSchemeDesignator`, `CodeMeaning` (e.g., "Volume from Voxel Summation")
`Value`	NUMERIC	The numeric measurement value
`Units`	RECORD	Units — `CodeMeaning` (e.g., "cubic millimeter", "no units", "Hounsfield Unit")
`derivationModifier`	RECORD	How the value was derived (e.g., "Mean", "Minimum", "Maximum")
`lateralityModifier`	RECORD	Laterality qualifier
`finding`	RECORD	What finding was measured — `CodeMeaning` (e.g., "Nodule", "Organ", "Anatomical Structure")
`findingSite`	RECORD	Where the finding is — `CodeMeaning` (e.g., "Liver", "Esophagus", "Lung")
`trackingIdentifier`	STRING	Human-readable tracking label (e.g., "Nodule 1", "Measurements group 26")
`trackingUniqueIdentifier`	STRING	Tracking UID — links back to `segmentations.TrackingUID`
`segmentationInstanceUID`	STRING	SOPInstanceUID of the referenced SEG object
`segmentationSeriesUID`	STRING	SeriesInstanceUID of the referenced SEG object
`segmentationSegmentNumber`	INTEGER	Segment number within the SEG — join to `segmentations.SegmentNumber`
`sourceSegmentedSeriesUID`	STRING	Source image series — join to `dicom_all.SeriesInstanceUID`

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 `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'
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 idc-index equivalent. This table is only accessible via BigQuery.

Key columns:

Column	Type	Description
`SOPInstanceUID`	STRING	SR instance UID
`SeriesInstanceUID`	STRING	SR series UID — join to `dicom_all` for collection/modality
`PatientID`	STRING	Patient identifier
`measurementGroup_number`	INTEGER	Group index within the SR — join key with `quantitative_measurements`
`Quantity`	RECORD	What was assessed — `CodeMeaning` (e.g., "Malignancy", "Calcification", "Texture")
`Value`	RECORD	The coded answer — `CodeMeaning` (e.g., "4 out of 5 (Moderately Suspicious for Cancer)")
`finding`	RECORD	What finding was assessed — `CodeMeaning` (e.g., "Nodule")
`findingSite`	RECORD	Anatomic site — `CodeMeaning` (e.g., "Lung")
`trackingIdentifier`	STRING	Human-readable tracking label
`segmentationInstanceUID`	STRING	SOPInstanceUID of the referenced SEG object
`segmentationSeriesUID`	STRING	SeriesInstanceUID of the referenced SEG object
`segmentationSegmentNumber`	INTEGER	Segment number within the referenced SEG
`sourceSegmentedSeriesUID`	STRING	Source image series — join to `dicom_all.SeriesInstanceUID`

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 `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.SeriesInstanceUID = qual.segmentationSeriesUID
  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 `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:

Parameter	Standard Tag	GE	Siemens	Philips
Diffusion b-value	(0018,9087)	(0043,1039)	(0019,100C)	(2001,1003)
Private Creator	-	GEMS_PARM_01	SIEMENS CSA HEADER	Philips Imaging

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

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

Data Quality Notes

Some collections show unrealistic values (e.g., b-value "1000000600") indicating encoding issues or different conventions
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
Always verify query results visually using the IDC Viewer before large-scale analysis

Private Element Resources

Manufacturer DICOM Conformance Statements:

DICOM Standard:

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: 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:

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. 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)

Currently there are ~130 clinical tables representing ~70 collections. 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 `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: 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

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BigQuery Guide for IDC

Prerequisites

When to Use BigQuery

Accessing IDC in BigQuery

Dataset Structure

Key Tables

dicom_all

Derived Tables

Collection Metadata

Common Query Patterns

Find Collections by Criteria

Get Download URLs

Find Studies with Multiple Modalities

License Filtering

Find Segmentations with Source Images

Derived Tables: Detailed Documentation

segmentations

quantitative_measurements

qualitative_measurements

measurement_groups

Combining quantitative and qualitative measurements

Private DICOM Elements

Understanding Private Elements

Accessing Private Elements in BigQuery

Private Element Query Patterns

Discover Available Private Tags

Identify Equipment Manufacturer

Access Private Element Values

Aggregate Values by Series

Combine Standard and Private Filters

Cross-Collection Analysis

Workflow: Finding and Using Private Tags

Data Quality Notes

Private Element Resources

Using Query Results with idc-index

Cost and Optimization

Clinical Data

Clinical Table Naming

Metadata Tables

Common Clinical Queries

Key Column: dicom_patient_id

Important Notes

Common Errors

Resources