v1.4 to v1.5

+ description: Query and download public cancer imaging data from NCI Imaging Data Commons. Invoke for any question about IDC collections, cancer imaging datasets, DICOM data access, radiology (CT, MR, PET) or pathology AI training sets, metadata queries, visualization, or license checks — even when the user doesn't explicitly mention "IDC". No authentication required.

+ version: "1.5"

+ source-skill-version: 1.8.1

+ idc-index: "0.12.5"

+ idc-data-version: "v24"

+ repository: https://github.com/ImagingDataCommons/imaging-data-commons-skill

+ Query and download public cancer imaging data from the National Cancer Institute Imaging Data Commons (IDC). No authentication required for data access.

+ **Expected network access:** IDC metadata is reachable three ways — a local DuckDB index shipped with the `idc-index` Python package (no network), or the hosted IDC service over MCP or REST (`api.imaging.datacommons.cancer.gov`, no authentication). File downloads use public GCS (`storage.googleapis.com`) and AWS S3 (`s3.amazonaws.com`) — no authentication required. DICOMweb access uses either the public IDC proxy (`proxy.imaging.datacommons.cancer.gov`, no auth) or the Google Cloud Healthcare API (`healthcare.googleapis.com`, requires GCP authentication). Optional BigQuery queries (`bigquery.googleapis.com`) also require GCP authentication. No credentials or environment variables are accessed by this skill.

+ **Current IDC Data Version: v24** (always verify — see *Best Practices*)

+ **Choose the access path first.** There is no single default: the cheapest correct path depends

+ on the session and the task.

+ 1. **Session already has the IDC MCP server?** Route discovery and metadata there — see *IDC

+ MCP Server*.

+ 2. **Otherwise, is `idc-index` installed?** Run `python scripts/check_version.py`. If it passes,

+ use `idc-index` for everything.

+ 3. **Not installed, and the task is read-only metadata** — counts, attribute values, collection

+ lookups, SQL under 10 000 rows, licenses, citations, viewer URLs? **Use the REST API over

+ `curl`; do not install anything.** Installing costs ~77 MB of packaged index data plus

+ pandas, pyarrow, and duckdb, which a metadata question does not need. See *Data Access

+ Options*.

+ 4. **Not installed, and the task needs more than metadata** — downloading files, pandas or

+ plotting, pydicom/SimpleITK, pathology tiling, results past 10 000 rows, or a version-pinned

+ script the user re-runs? Install `idc-index`: `check_version.py` exits non-zero and prints

+ the exact install command for the running interpreter. Prefer a virtual environment, then

+ restart Python.

+ `idc-index` ([GitHub](https://github.com/imagingdatacommons/idc-index)) is still the most

+ capable path and the only one that moves image bytes; the rule is just not to pay for it before

+ the task calls for it. `check_version.py` never installs anything itself — it also flags a newer

+ `idc-index` or skill release when one exists.

+ **Setup for the `idc-index` path:**

+ ```python

+ from idc_index import IDCClient

+ client = IDCClient()

+ # Verify IDC data version (should be "v24")

+ print(f"IDC data version: {client.get_idc_version()}")

+ **Core workflow:** query metadata with `client.sql_query()` → download with

+ `client.download_from_selection()` → visualize with `client.get_viewer_URL()`. Python examples

+ below assume this `client`; *Data Access Options* has the REST equivalents. For current data

+ scale, run the summary query in `references/sql_patterns.md` or `GET /v3/stats`.

+ ## IDC MCP Server

+ IDC operates a hosted MCP server at `https://api.imaging.datacommons.cancer.gov/mcp`

+ (streamable HTTP, no authentication). Where it is available it complements — it does not

+ replace — the `idc-index` workflow below.

+ **Identify it** by the MCP resource `idc://guide`, or by three or more of the tool names

+ `build_cohort`, `get_cohort_urls`, `list_analysis_results`, and `get_idc_version`. Generic

+ names such as `run_sql` are not evidence on their own. If identification is ambiguous, use

+ `idc-index`.

+ **If this session has the server**, treat it as authoritative for discovery and metadata —

+ IDC version, counts, attribute values, cohort building, metadata SQL — and follow the

+ server's own instructions rather than re-deriving them from this file. Its data version is

+ whatever the server reports: call `get_idc_version` instead of relying on the version pinned

+ in this file.

+ Return here for what the server does not do: downloading files, local pandas/notebook

+ analysis, DICOMweb, BigQuery, digital pathology tiling, and reproducible scripts. Hand off by

+ passing SeriesInstanceUIDs from the server to `client.download_from_selection(...)`, and run

+ `scripts/check_version.py` at that point.

+ **If it is not available**, the identical service is reachable with no configuration as a REST

+ API at `https://api.imaging.datacommons.cancer.gov/v3` — use it for read-only metadata rather

+ than installing `idc-index`, per the routing gate in *Overview*. Suggest connecting the MCP

+ server at most once, only for repeated interactive discovery, and never change the user's

+ configuration yourself.

+

+ See `references/mcp_guide.md` for the tool inventory, handoff patterns, and per-host notes.

+

+ Inline below: the MCP/REST routing rules, the IDC data model, the index tables and how they

+ join, the core API patterns (query, download, visualize, license, cite), best practices, and

+ troubleshooting.

+ | `use_cases.md` | End-to-end workflows: training datasets, batch downloads, DICOM reading with pydicom/SimpleITK, pipeline integration |

+ | `licensing_and_citation.md` | Commercial-use questions, mixed-license cohorts, citation formats |

+ | `mcp_guide.md` | Hosted IDC MCP server: tool inventory, identification, handoff to `idc-index` |

+ | `rest_api_guide.md` | Hosted IDC REST API: endpoints, filter syntax, SQL over HTTP, manifests |

+ - **analysis_result_id**: Identifies derived objects (segmentations, annotations, radiomics features) across one or more original collections. Use it to find AI-generated or expert annotations, while `collection_id` finds original imaging data (which may itself include deposited annotations).

+ The `idc-index` package provides multiple metadata index tables, accessible via SQL or as pandas DataFrames. The REST API exposes the same tables through `GET /tables` and `POST /sql`.

+ **Important:** `client.indices_overview` is the authoritative source for current table descriptions, available columns, and their types — query it when writing SQL or exploring data structure. It also answers "which table contains column X"; see `references/index_tables_guide.md` for that search pattern and full schema discovery.

+ Always call `client.fetch_index("table_name")` before querying any index table — it is safe and idempotent for all tables, including those loaded automatically at startup.

+ | Family | Tables | Granularity |

+ |--------|--------|-------------|

+ | Core | `index` (primary metadata for all current data), `collections_index`, `analysis_results_index` | series / collection / analysis result |

+ | Modality acquisition parameters | `ct_index`, `mr_index`, `pt_index`, `contrast_index` | 1 row = 1 series of that modality |

+ | Derived objects | `seg_index`, `rtstruct_index`, `ann_index`, `ann_group_index` | 1 row = 1 series (or annotation group) |

+ | Microscopy | `sm_index`, `sm_instance_index` | 1 row = 1 SM series / instance |

+ | Geometry, clinical, history | `volume_geometry_index`, `clinical_index`, `version_metadata_index`, `prior_versions_index` | see guide |

+ `references/index_tables_guide.md` has the full inventory with each table's columns and

+ contents — load it when you need to know what a specialized table actually holds.

+

+ **`prior_versions_index` is for reproducibility only.** It contains series permanently *removed*

+ from IDC, with zero overlap with `index`. Use it only to reproduce work against a prior IDC

+ version. Do NOT use it for version history or "what's new" questions — those use

+ `series_init_idc_version` / `series_revised_idc_version` in the main `index` table, which are

+ not equivalent to this table's `min_idc_version` / `max_idc_version`.

+

+ **`SeriesInstanceUID` is the universal join key** for all series-level specialized tables: `sm_index`, `sm_instance_index`, `seg_index`, `ann_index`, `ann_group_index`, `contrast_index`, `volume_geometry_index`, `rtstruct_index`, `ct_index`, `mr_index`, `pt_index`. Always join these to `index` on `SeriesInstanceUID`. The exceptions below use different column names.

+ | `segmented_SeriesInstanceUID` | seg_index → index | Link segmentation to its source image series (`seg_index.segmented_SeriesInstanceUID = index.SeriesInstanceUID`) |

+ | `referenced_SeriesInstanceUID` | ann_index → index, rtstruct_index → index | Link annotation or RTSTRUCT to its source image series |

+ **Note:** `subjects`, `updated`, and `description` appear in multiple tables but have different meanings (counts vs identifiers, different update contexts). Joining `prior_versions_index` to `index` on `SeriesInstanceUID` always returns zero rows — see the warning above.

+ Clinical (non-imaging) attributes — staging, demographics, therapy — live in per-collection

+ tables. `client.fetch_index("clinical_index")` loads the dictionary mapping columns to

+ collections; `client.get_clinical_table(name)` returns one table as a DataFrame.

+ See `references/clinical_data_guide.md` for the discovery workflow, coded-value mapping, and

+ joining clinical data with imaging.

+ ## Data Access Options

+ | Method | Auth | Best For | Reference |

+ |--------|------|----------|-----------|

+ | `idc-index` | No | Downloads, pandas analysis, unbounded queries — the most capable path | This document |

+ | IDC MCP server | No | Discovery, cohort building, metadata when the session already has it | `mcp_guide.md` |

+ | IDC REST API | No | Metadata with no install, from any language or shell — the default when `idc-index` is absent | `rest_api_guide.md` |

+ | Direct Parquet (GCS) | No | Version-pinned queries, or results past the REST row cap | `parquet_access_guide.md` |

+ | Cloud storage (S3/GCS) | No | Direct file access, bulk transfer, custom pipelines | `cloud_storage_guide.md` |

+ | DICOMweb via IDC proxy | No | Tool and PACS integration; daily quota, so testing and moderate use | `dicomweb_guide.md` |

+ | DICOMweb via Google Healthcare | Yes (GCP) | The same DICOMweb API at production volume, without the proxy quota | `dicomweb_guide.md` |

+ | SlicerIDCBrowser | No | 3D visualization and analysis in 3D Slicer | https://github.com/ImagingDataCommons/SlicerIDCBrowser |

+ | BigQuery | Yes (GCP) | Full DICOM metadata, private elements, SR measurements — last resort | `bigquery_guide.md` |

+ **The IDC Portal (https://portal.imaging.datacommons.cancer.gov/) is interactive only** —

+ browser-based exploration, manual cohort selection, and download. Unlike every option above it

+ has no programmatic interface, so point a user there to browse or click through data

+ themselves; never use it as a step in a script or workflow.

+ **REST API — the no-install metadata path**

+ `https://api.imaging.datacommons.cancer.gov/v3`, no authentication: discovery, cohort counts and

+ manifests, read-only SQL, clinical tables, viewer URLs, licenses, citations. It is the same

+ service as the MCP server over plain HTTP, so it needs no configuration. It never moves image

+ bytes — switch to `idc-index` to download, to get a DataFrame, or for results past 10 000 rows.

+ ```bash

+ B=https://api.imaging.datacommons.cancer.gov/v3

+ curl -s $B/version # idc_version, idc_index_data_version, api_version

+ curl -s $B/stats # collections, patients, studies, series, instances, size_TB

+ curl -s "$B/attributes/Modality/values?limit=5" # real filter values, with counts

+ curl -s $B/sql -H 'content-type: application/json' \

+ -d '{"sql":"SELECT collection_id, COUNT(*) n FROM index GROUP BY 1 ORDER BY n DESC LIMIT 3"}'

+ curl -s $B/cohort/counts -H 'content-type: application/json' \

+ -d '{"filters":{"terms":{"collection_id":["rider_pilot"]}}}'

+ ```

+ **The filter object always goes under `filters`** — on `cohort/counts`, `cohort/manifest`,

+ `cohort/manifest.txt`, `licenses`, and `citations` alike. A bare filter or an unrecognized key is

+ a 422 naming the fix; an unfiltered series-enumerating request is a 400, not the whole archive.

+ Every filtered response echoes `filters_applied` and `warnings` — read them, because they name

+ any predicate the server dropped. A zero count with empty `warnings` therefore means the filter

+ matched nothing, not that a value was miscased; miscasing produces a warning that says so.

+ `POST /sql` takes one read-only `SELECT`/`WITH` over the tables `idc-index` exposes plus

+ `clinical.<table>`; `max_rows` defaults to 5 000, caps at 10 000, and `truncated` flags clipping.

+ `GET /attributes` lists the 19 filterable attributes — clinical values, segmented anatomy, and

+ acquisition parameters are not among them and need SQL. There is no rate limit or quota. **Use

+ v3 only:** V1 and V2 are superseded and scheduled for shutdown, so port any `/v1/`- or

+ `Modality_btw`-style example a user brings rather than extending it.

+ Both sides build on `idc-index-data`, so compare the API's `idc_index_data_version` against local

+ `idc_index_data.__version__` before mixing them: the **major is the IDC data release** (`24.x.y`

+ serves `v24`), so differing minor/patch means the series are identical. If the API is a whole

+ release ahead, `idc-index` **cannot download the extra series** — it silently skips what its own

+ index does not list — so either upgrade it (run `scripts/check_version.py` for the right command)

+ or transfer directly from the bucket with `s5cmd --no-sign-request`.

+ See `references/rest_api_guide.md` for the endpoint reference, filter grounding, limits, and the

+ manifest-based download flow.

+ **Cloud storage organization**

+ All DICOM files live in public buckets mirrored between AWS S3 and GCS, organized by CRDC UUIDs

+ (not DICOM UIDs) to support versioning, as `<crdc_series_uuid>/<crdc_instance_uuid>.dcm`. Access

+ is free (no egress fees) via AWS CLI, gsutil, or s5cmd with anonymous access; use the

+ `series_aws_url` column for S3 URLs. Note that `idc-open-data-cr` / `idc-open-cr` (~4% of data)

+ is commercial-use restricted (CC BY-NC). See `references/cloud_storage_guide.md` for the full

+ bucket list and UUID mapping.

+ **DICOMweb access**

+ IDC data is available via DICOMweb (Google Cloud Healthcare API) for PACS integration and

+ DICOMweb-compatible tools: a public proxy (no auth, daily quota) for testing and moderate

+ queries, or Google Healthcare (GCP auth) for production volumes. See

+ `references/dicomweb_guide.md`.

+

+ The idc-index metadata tables are also published as Parquet on a public GCS bucket

+ (`idc-index-data-artifacts`), queryable with DuckDB or pandas. This needs DuckDB installed

+ and cannot reach the per-collection clinical tables, so prefer REST `/sql` for ad-hoc metadata;

+ choose Parquet to pin a data version or for results past the REST row cap. See

+ `references/parquet_access_guide.md`.

+ ## Core Capabilities

+ The patterns below are the ones that go wrong when recalled from memory rather than checked.

+ Worked examples for each area live in the reference guides named inline.

+ ### 1. Discovery — enumerate values before filtering on them

+ Filtering on a guessed `Modality` or `BodyPartExamined` string is the most common cause of an

+ empty result set. Enumerate first:

+ modalities = client.sql_query("""

+ SELECT DISTINCT Modality, COUNT(*) as series_count

+ FROM index

+ GROUP BY Modality

+ ORDER BY series_count DESC

+ """)

+ print(modalities)

+ The same pattern works for any filter column, optionally narrowed by another —

+ `BodyPartExamined` within a `Modality`, `Manufacturer`, `collection_id`. On the REST path this

+ grounding is a single call — `GET /attributes/{attr}/values` returns values with counts — and the

+ cohort endpoints report a miscased value in `warnings` rather than as an empty result.

+ Two indices carry curated collection-level metadata the primary `index` does not, both

+ requiring `client.fetch_index(...)` first: `collections_index` (cancer types, tumor locations,

+ species, subject counts) and `analysis_results_index` (derived datasets — AI segmentations,

+ expert annotations, radiomics — with their source collections and modalities).

+

+ **Cancer type lives in `collections_index.cancer_types`, not in `index`** — filtering by

+ cancer type requires a join:

+

+ ```python

+ client.fetch_index("collections_index")

+ results = client.sql_query("""

+ SELECT i.collection_id, i.PatientID, i.SeriesInstanceUID, i.Modality

+ FROM index i

+ JOIN collections_index c ON i.collection_id = c.collection_id

+ WHERE c.cancer_types LIKE '%Breast%'

+ AND i.Modality = 'MR'

+ LIMIT 20

+ """)

+ `client.sql_query()` returns a pandas DataFrame. Confirm column names with

+ `client.get_index_schema('index')` or `client.indices_overview` before writing a query rather

+ than assuming them.

+ See `references/sql_patterns.md` for filter-value discovery, annotation and segmentation

+ queries, size estimation, clinical linking, and version tracking ("what's new in vX" — use

+ `series_init_idc_version` / `series_revised_idc_version` in `index`, never

+ `prior_versions_index`).

+ ### 2. Downloading DICOM files

+ **The two download methods take their first two arguments in opposite order.** This is the

+ most common source of broken IDC code — check it rather than recalling it:

+ | Method | First arg | Second arg | Use when |

+ |--------|-----------|------------|----------|

+ | `download_from_selection` | `downloadDir` (required) | filter kwargs (optional) | Filtering by collection, patient, study, or series |

+ | `download_dicom_series` | `seriesInstanceUID` (required) | `downloadDir` (required) | Downloading specific series by UID only |

+ **`download_from_selection` takes filter keyword arguments, NOT a DataFrame.** The name

+ "from_selection" refers to filtering the IDC index by criteria — not to accepting a pandas

+ DataFrame. To download query results, extract the UIDs into a list first:

+ ```python

+ # Step 1: Query for series UIDs

+ series_df = client.sql_query("""

+ SELECT SeriesInstanceUID

+ FROM index

+ WHERE Modality = 'CT'

+ AND BodyPartExamined = 'CHEST'

+ AND collection_id = 'nlst'

+ LIMIT 5

+ """)

+ # Step 2: Extract UIDs as a list from the DataFrame

+ uids = list(series_df['SeriesInstanceUID'].values)

+ # Step 3: Pass the list to download_from_selection (NOT the DataFrame itself)

+ client.download_from_selection(

+ downloadDir="./data/lung_ct",

+ seriesInstanceUID=uids # list of strings, not a DataFrame

+ )

+ # Alternative: download_dicom_series has seriesInstanceUID as FIRST arg (different order!)

+ client.download_dicom_series(

+ seriesInstanceUID=uids, # FIRST arg here

+ downloadDir="./data/lung_ct"

+ )

+ # Whole collection: downloadDir is still the FIRST positional argument

+ client.download_from_selection(downloadDir="./data/rider", collection_id="rider_pilot")

+ ```

+ Both methods default to AWS; pass `source_bucket_location="gcs"` to pull from Google Storage.

+ **Downloaded files are named `<crdc_instance_uuid>.dcm`, not by SOPInstanceUID.** The DICOM

+ UIDs are preserved inside the file metadata, not in the filename. Use the `crdc_instance_uuid`

+ column to map files back to the series they came from.

+ `idc download <collection|series-uid|manifest> --download-dir ./data` does the same from a

+ shell. See `references/cli_guide.md` for the `dirTemplate` hierarchy options (Python default:

+ `%collection_id/%PatientID/%StudyInstanceUID/%Modality_%SeriesInstanceUID`; `dirTemplate=""`

+ flattens), manifest downloads with resume, and dry-run size estimation.

+ ### 3. Visualizing IDC images

+ ```python

+ viewer_url = client.get_viewer_URL(seriesInstanceUID=uid) # one series

+ viewer_url = client.get_viewer_URL(studyInstanceUID=study_uid) # all series in a study

+ ```

+ Returns a browser URL — nothing is downloaded. The method selects OHIF v3 for radiology or

+ SLIM for slide microscopy automatically. Viewing by study is useful when a single DICOM Study

+ holds several Series (T1, T2, and DWI from one MRI session).

+ ### 4. Licenses and citations — obligations, not optional steps

+ IDC data carries license terms and attribution requirements that follow it into any downstream

+ publication or product, and neither is inferable from the pixel data. **Check the license

+ before use, and generate citations for whatever you download.**

+ ```python

+ # License breakdown for a selection

+ licenses = client.sql_query("""

+ SELECT DISTINCT collection_id, license_short_name,

+ COUNT(DISTINCT SeriesInstanceUID) as series_count

+ FROM index GROUP BY collection_id, license_short_name

+ """)

+ # Citations for the same selection you downloaded (APA by default)

+ for citation in client.citations_from_selection(collection_id="rider_pilot"):

+ print(citation)

+ ```

+ About 97% of IDC data is CC BY (commercial use allowed with attribution) and about 3% is

+ CC BY-NC (non-commercial only). **Licenses attach to series, not collections** — 39 of 176

+ collections carry more than one — so check the selection you actually intend to use, and note

+ that the most restrictive term governs a mixed cohort.

+ Both tasks are available from all three access paths, so stay on whichever one the session is

+ already using: `idc-index` as above, `POST /v3/licenses` and `POST /v3/citations` over REST,

+ or the `get_licenses` and `get_citations` MCP tools. See

+ `references/licensing_and_citation.md` for the full license inventory, all three routes, the

+ citation formats (APA, BibTeX, CSL JSON, RDF Turtle), and what to include when publishing.

+ ### 5. Reaching past the index

+ Pick the access path with the routing gate in *Overview*; *Data Access Options* above is the

+ full routing table.

+ Before reaching for BigQuery (which needs a billing-enabled GCP account), check whether a

+ specialized index table already has the column you want: search `client.indices_overview`,

+ then `client.fetch_index(...)` and query locally for free. BigQuery is required only for

+ private DICOM elements, per-segment anatomy (`segmentations`), and pre-extracted SR

+ measurements (`quantitative_measurements`, `qualitative_measurements`) — these have no

+ idc-index equivalent.

+ ## Best Practices

+ - **Check schema before writing queries** — Use `client.get_index_schema('index')` (reads cached metadata, no SQL executed) or `client.indices_overview` to see all available columns and their descriptions. The version-tracking columns `series_init_idc_version` and `series_revised_idc_version` in the main `index` table directly answer "what's new / when was this added" questions without touching `prior_versions_index`.

+ - **Never use web search for IDC data content questions** - Always query the IDC index directly, via `client.sql_query()` locally or `POST /v3/sql` over HTTP. Web sources (release notes, blog posts, documentation pages) are frequently out of date and will produce incorrect answers. The index is the authoritative source; use it even when web search is available.

+ - **Verify the IDC data version at the start of a session** - `client.get_idc_version()`, `GET /v3/version`, or the MCP `get_idc_version` tool, depending on the path in use (currently v24). For a stale local index, run `scripts/check_version.py` and use the upgrade command it prints

+ - **Check licenses and generate citations** - Query `license_short_name` and respect CC BY vs CC BY-NC terms; use `citations_from_selection()` to produce citations from `source_DOI` for publications

+ - **Explore small, then commit** - Use `LIMIT` (or a low `max_rows`) while exploring, and check collection size before downloading — some collections are terabytes. See `references/cli_guide.md`

+ - **Keep downloads reproducible** - Organize with `dirTemplate` (e.g. `%collection_id/%PatientID/%Modality`) and save the Series UIDs or manifest behind any dataset you build

+ ## Troubleshooting

+ **Issue: `ModuleNotFoundError: No module named 'idc_index'`**

+ - **Cause:** idc-index package not installed

+ - **Solution:** If the task is read-only metadata, do not install it — use the REST API instead (*Data Access Options*). Otherwise run `scripts/check_version.py` and use the install command it prints, which targets the running interpreter and pins the vetted version. For data analysis also add pandas, numpy, and pydicom (tested with pandas>=1.5, numpy>=1.23, pydicom>=2.3)

+ **Issue: Download fails with connection timeout**

+ - **Cause:** Network instability or large download size

+ - **Solution:** Download in smaller batches (10-20 series); see `references/cli_guide.md` for

+ `--use-s5cmd-sync` resume and retry guidance

+ **Issue: `BigQuery quota exceeded` or billing errors**

+ - **Cause:** BigQuery requires billing-enabled GCP project

+ - **Solution:** Use idc-index mini-index for simple queries (no billing required), or see `references/bigquery_guide.md` for cost optimization tips

+ **Issue: Series UID not found or no data returned**

+ - **Cause:** Typo in UID, data not in the current IDC version, or wrong field name

+ - **Solution:** Test with `LIMIT 5` first, check field names against `client.indices_overview`,

+ and confirm the series is in the current version (some old data is deprecated)

+ **Issue: Column not found in `index` table (e.g., `SliceThickness`, `PixelSpacing`, `KVP`, `EchoTime`, `InjectedDose`)**

+ - **Cause:** The `index` table contains series-level metadata only; modality-specific acquisition and reconstruction parameters live in dedicated tables (`ct_index`, `mr_index`, `pt_index`)

+ - **Solution:** Search `client.indices_overview` for the column to find its table — the loop is under *Finding which table contains a column* in `references/index_tables_guide.md` — then fetch and join on `SeriesInstanceUID`:

+ ```python

+ client.fetch_index("ct_index")

+ result = client.sql_query("""

+ SELECT i.SeriesInstanceUID, i.Modality, c.SliceThickness, c.KVP, c.PixelSpacing_row_mm

+ FROM index i

+ JOIN ct_index c USING (SeriesInstanceUID)

+ WHERE i.collection_id = 'your_collection'

+ """)

+ ```

+

+ **Issue: Downloaded DICOM files won't open**

+ - **Cause:** Corrupted download, or an object type the viewer does not handle — SEG, RTSTRUCT,

+ SR, and slide microscopy all need specialized tools

+ - **Solution:** Check `Modality` and `SOPClassUID` first, validate with

+ `pydicom.dcmread(file, force=True)`, try another viewer (3D Slicer, QuPath for pathology),

+ then re-download

+

+ ## Resources

+

+ Reference guides and their decision triggers are listed in *Quick Navigation* above.

+

+ - **IDC Portal**: https://portal.imaging.datacommons.cancer.gov/explore/

+ - **Documentation**: https://learn.canceridc.dev/ — **Tutorials**: https://github.com/ImagingDataCommons/IDC-Tutorials

+ - **User Forum**: https://discourse.canceridc.dev/ — **idc-index**: https://github.com/ImagingDataCommons/idc-index

+ - **[indices_reference](https://idc-index.readthedocs.io/en/latest/indices_reference.html)** — external index-table docs (may be ahead of the installed version)

+ - **Citation**: Fedorov, A., et al. "National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence." RadioGraphics 43.12 (2023). https://doi.org/10.1148/rg.230180

+ - **Skill updates**: [releases page](https://github.com/ImagingDataCommons/imaging-data-commons-skill/releases); watch the repository (Watch → Custom → Releases)