Index Tables Guide for IDC

Tested with: idc-index 0.12.5 (IDC data version v24)

This guide covers the structure and access patterns for IDC index tables: programmatic schema discovery, DataFrame access, and join column references. For the overview of available tables and their purposes, see the "Index Tables" section in the main SKILL.md.

Complete index table documentation: https://idc-index.readthedocs.io/en/latest/indices_reference.html

When to Use This Guide

Load this guide when you need to:

• Discover table schemas and column types programmatically
• Access index tables as pandas DataFrames (not via SQL)
• Understand key columns and join relationships between tables

For SQL query examples (filter discovery, finding annotations, size estimation), see references/sql_patterns.md.

Prerequisites

Needs idc-index installed — run python scripts/check_version.py, which reports the installed version and prints the install command for the interpreter you are running.

Available Tables

SKILL.md carries a compact map of the table families. This is the full inventory with row granularity and contents. Always call client.fetch_index("table_name") before querying any of them — it is safe and idempotent for all tables, including those loaded automatically at startup.

Accessing Index Tables

Via SQL (recommended for filtering/aggregation)

from idc_index import IDCClient
client = IDCClient()

# Query the primary index (always available)
results = client.sql_query("SELECT * FROM index WHERE Modality = 'CT' LIMIT 10")

# Fetch and query additional indices
client.fetch_index("collections_index")
collections = client.sql_query("SELECT collection_id, cancer_types, tumor_locations FROM collections_index")

client.fetch_index("analysis_results_index")
analysis = client.sql_query("SELECT * FROM analysis_results_index LIMIT 5")

As pandas DataFrames (direct access)

# Primary index (always available after client initialization)
df = client.index

# Fetch and access on-demand indices
client.fetch_index("sm_index")
sm_df = client.sm_index

Discovering Table Schemas

The indices_overview dictionary contains complete schema information for all tables. Always consult this when writing queries or exploring data structure.

DICOM attribute mapping: Many columns are populated directly from DICOM attributes in the source files. The column description in the schema indicates when a column corresponds to a DICOM attribute (e.g., "DICOM Modality attribute" or references a DICOM tag). This allows leveraging DICOM knowledge when querying — standard DICOM attribute names like PatientID, StudyInstanceUID, Modality, BodyPartExamined work as expected.

from idc_index import IDCClient
client = IDCClient()

# List all available indices with descriptions
for name, info in client.indices_overview.items():
    print(f"\n{name}:")
    print(f"  Installed: {info['installed']}")
    print(f"  Description: {info['description']}")

# Get complete schema for a specific index (columns, types, descriptions)
schema = client.indices_overview["index"]["schema"]
print(f"\nTable: {schema['table_description']}")
print("\nColumns:")
for col in schema['columns']:
    desc = col.get('description', 'No description')
    # Description indicates if column is from DICOM attribute
    print(f"  {col['name']} ({col['type']}): {desc}")

# Find columns that are DICOM attributes (check description for "DICOM" reference)
dicom_cols = [c['name'] for c in schema['columns'] if 'DICOM' in c.get('description', '').upper()]
print(f"\nDICOM-sourced columns: {dicom_cols}")

Alternative: use get_index_schema() method:

schema = client.get_index_schema("index")
# Returns same schema dict: {'table_description': ..., 'columns': [...]}

Finding which table contains a column

The most common schema question is "where does SliceThickness live?" — the primary index holds series-level metadata only, so modality-specific acquisition parameters are in dedicated tables. Search the overview rather than guessing; neither call fetches anything:

# Find which table(s) contain a specific column (no fetch required)
target = "SliceThickness"
for table_name, info in client.indices_overview.items():
    if any(c["name"] == target for c in info["schema"]["columns"]):
        print(f"'{target}' is in: {table_name}")
# → 'SliceThickness' is in: ct_index

# List all columns in a table from the schema (no fetch required)
ct_cols = [c["name"] for c in client.indices_overview["ct_index"]["schema"]["columns"]]
print("ct_index columns:", ct_cols)
# → ['SeriesInstanceUID', 'PixelSpacing_row_mm', 'PixelSpacing_col_mm', 'Rows',
#    'Columns', 'SliceThickness', 'KVP', 'ConvolutionKernel', ...]

Then client.fetch_index("ct_index") and join to index on SeriesInstanceUID.

Key Columns Reference

Most common columns in the primary index table (use indices_overview for complete list and descriptions):

DICOM = Yes: Column value extracted from the DICOM attribute with the same name. Refer to the DICOM standard for numeric tag mappings. Use standard DICOM knowledge for expected values and formats.

Join Column Reference

Use this table to identify join columns between index tables. Always call client.fetch_index("table_name") before using a table in SQL.

For complete query examples using these joins, see references/sql_patterns.md.

Troubleshooting

Issue: Column not found in table

• Cause: Column name misspelled or doesn't exist in that table
• Solution: Use client.indices_overview["table_name"]["schema"]["columns"] to list available columns

Issue: DataFrame access returns None

• Cause: Index not fetched or property name incorrect
• Solution: Fetch first with client.fetch_index(), then access via property matching the index name

Resources

• Complete index table documentation: https://idc-index.readthedocs.io/en/latest/indices_reference.html
• references/sql_patterns.md for query examples using these tables
• references/clinical_data_guide.md for clinical data workflows
• references/digital_pathology_guide.md for pathology-specific indices