NSCLC Radiogenomics

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

1. 1. Do quantitative CT or PET features predict gene expression programs in lung adenocarcinoma?

   Needs another datasetn ≈ 26HUMAN

   This is the defining radiogenomics question and it needs paired data on the same patients, which this collection provides for the subset with matched microarrays. Imaging is available for all 211.

   Statistical note: The matched-expression subset described in the collection is around 26 patients, which supports hypothesis generation and feature-level correlation but not a validated predictive model. Confirm the exact overlap before designing.

   NeedsRadiology imagingBulk RNA sequencing

   A published example: PMID 30325352

2. 2. How reproducible are radiomic features across CT and PET acquisitions of the same tumor?

   Answerable as shippedn ≈ 211HUMAN

   Both modalities were acquired for the same patients pre-surgically, so feature stability across modality and acquisition can be assessed directly - a prerequisite for any radiomics claim and rarely testable.

   Statistical note: Reproducibility is estimable at this sample size; report intraclass correlation.

   NeedsRadiology imaging

3. 3. Can models trained on this cohort's segmentations generalize to other lung imaging collections in the same repository?

   Needs another datasetn ≈ 211HUMAN

   Expert segmentations and semantic annotations are provided, and the Imaging Data Commons holds several other lung collections that can serve as external validation within one access framework.

   NeedsRadiology imaging

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.

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

from idc_index import IDCClient

COLLECTION = "nsclc_radiogenomics"
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="./nsclc_radiogenomics")

curl -s https://cd2s.vercel.app/data/datasets/idc-nsclc-radiogenomics.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

   Explore the collection in the Imaging Data CommonsFor people30 minutes

   Fully open. No account, no data access request.

   https://portal.imaging.datacommons.cancer.gov/explore/?collection_id=nsclc_radiogenomics
2. 2

   Pull DICOM with the idc-index Python packageFor people1-2 hours

   pip install idc-index
   python -c "
   from idc_index import IDCClient
   c = IDCClient()
   s = c.index[c.index['collection_id'] == 'nsclc_radiogenomics']
   print(len(s), 'series', round(s['series_size_MB'].sum() / 1024, 1), 'GB')
   c.download_from_selection(collection_id='nsclc_radiogenomics', downloadDir='.')
   "
   

3. 3

   Retrieve the matched expression series from GEO and verify the identifier joinFor people2 hours

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28827
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"] == "nsclc_radiogenomics"]
   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.