CPTAC-UCEC

Written and checked by a reviewer, not generated from metadata.

No reviewed research questions yet. The measurements and field completeness above are machine-extracted and still useful for judging fit.

Written by a reviewer against the source, not inferred from what is missing.

Articles that analyzed these data, counted separately from articles that only cited the paper. Only the first counts as reuse.

Analyses someone ran on these data end to end, each with a receipt recording when it ran, against which packages and for how long.

Nobody has shared an analysis of this dataset yet. The examples on other datasets cover the common patterns and can be adapted - and if you adapt one, it belongs here.

Starter code generated from this record’s own identifiers, then the access steps.

from idc_index import IDCClient

COLLECTION = "cptac_ucec"
client = IDCClient()

series = client.index[client.index["collection_id"] == COLLECTION]   # one row per DICOM series
print(len(series), "series,", series["PatientID"].nunique(), "patients,",
      round(series["series_size_MB"].sum() / 1024, 1), "GB")
print(series["Modality"].value_counts().to_dict())

# Everything above is an index lookup and costs nothing. Uncomment to pull the pixels;
# the client refuses up front if the disk cannot hold them.
# client.download_from_selection(collection_id=COLLECTION, downloadDir="./cptac_ucec")

curl -s https://cd2s.vercel.app/data/datasets/idc-cptac-ucec.json | python3 -c '
import json, sys
r = json.load(sys.stdin)
for l in r["limitations"]:
    print(l["severity"].upper(), "-", l["statement"][:120])
print("survival endpoint:", r["longitudinal"]["has_survival_endpoint"], "| treatment response:", r["longitudinal"]["has_treatment_response"])'

Access steps

1. 1

   Open the collection in the IDC portalFor people5 minutes

   Browse the series before downloading; imaging collections can be very large.

   https://portal.imaging.datacommons.cancer.gov/explore/?collection_id=cptac_ucec

   Generated from repository policy. Check the linked policy before downloading.

2. 2

   Read the collection's own licenceFor people5 minutes

   Per-collection license, typically CC BY 3.0/4.0 or CC BY-NC. IDC licences differ by collection.

   https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/

   Generated from repository policy. Check the linked policy before downloading.

3. 3

   Download with idc-indexFor peoplehours for a full collection

   The package resolves the collection to its buckets and pulls selected data.

   pip install idc-index
   python - <<'PY'
   from idc_index import IDCClient
   IDCClient().download_from_selection(collection_id="cptac_ucec", downloadDir=".")
   PY

   https://github.com/ImagingDataCommons/idc-index

   Generated from repository policy. Check the linked policy before downloading.

4. 4

   List the series before pulling pixelsFor agents

   List modality and size first so an agent can choose what to fetch.

   python - <<'PY'
   from idc_index import IDCClient
   i = IDCClient().index
   s = i[i["collection_id"] == "cptac_ucec"]
   print(len(s), "series", s["PatientID"].nunique(), "patients",
         round(s["series_size_MB"].sum() / 1024, 1), "GB")
   print(s["Modality"].value_counts().to_dict())
   PY

   https://learn.canceridc.dev/

   Generated from repository policy. Check the linked policy before downloading.

All of it, and more, is in this record’s JSON.

Same disease area, favoring cohorts that add a measurement this one lacks.