PDXNet
A pan-cancer repository of 5,420 DICOM Slide Microscopy instances from 919 patient-derived xenograft (PDX) and paired parental tumor H&E whole slide images (870 mouse PDX, 49 human progenitor) curated from the NCI PDXNet Consortium. Images have associated genomic, transcriptomic, and clinical metadata, as well as pathologic assessments and, in some cases, detailed pathologic annotations. The original TIFF and SVS images were converted to DICOM SM format. Please see the PDXNet Zenodo page to learn more about the images and to obtain any supporting metadata for this collection.
Can it answer your research question?
0 of 6 supportedOverall survival
Not measuredClinical field completeness has not been measured for this record.
Progression or recurrence
Not measuredClinical field completeness has not been measured for this record.
Treatment response
Not measuredClinical field completeness has not been measured for this record.
Which drug was given
Not measuredClinical field completeness has not been measured for this record.
Stage-adjusted modelling
Not measuredClinical field completeness has not been measured for this record.
Analysis by race or ethnicity
Not measuredClinical field completeness has not been measured for this record.
At a glance
Every percentage is the repository’s own record of what is filled inSubjectCALC
As filed by the repository
Who is in the cohort
The repository’s harmonized records carry no demographics for this dataset.
What was measured, and on how many
- DICOM medical imaging919 of 919 · 100%
- Clinical (accompanying data, per IDC supporting_data)
- Genomics (accompanying data, per IDC supporting_data)
Access
Public DICOM download from the IDC portal, its API, or the idc-index Python package; no account required for public collections.
- License
- Per-collection license, typically CC BY 3.0/4.0 or CC BY-NC
- Citation requirement
- Cite the collection DOI and the IDC/TCIA data-citation guidance.
What it is good for
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.
What it cannot tell you
Written by a reviewer against the source, not inferred from what is missing.
IDC serves no clinical table for this collection, so nothing about outcome, stage, treatment or demographics can be read from the repository. Any such variable would have to come from the originating trial or publication. The collection's own supporting_data field nevertheless lists clinical data, so the two disagree.
Affects: survival; treatment response; stage-adjusted modelling; analysis by race or ethnicity
Who has used it
Articles that analyzed these data, counted separately from articles that only cited the paper. Only the first counts as reuse.
- Not assessed: this dataset has no accession specific enough for citation-based reuse tracing, so a shortfall cannot be measured.
Articles that analyzed the data
Not searchable. See the note above.
Community examples
Share your exampleAnalyses 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.
Start here
Starter code generated from this record’s own identifiers, then the access steps.
Get the data in code
IDC: pull the DICOM collection with idc-indexpython
from idc_index import IDCClient
COLLECTION = "pdxnet"
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="./pdxnet")This page as data: limitations firstbash
curl -s https://cd2s.vercel.app/data/datasets/idc-pdxnet.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
- Open the collection in the IDC portalFor people
Browse the series before downloading; imaging collections can be very large.
https://portal.imaging.datacommons.cancer.gov/explore/?collection_id=pdxnet - Read the collection's own licenceFor people
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/ - Download with idc-indexFor people
The package resolves the collection to its buckets and pulls selected data.
https://github.com/ImagingDataCommons/idc-indexpip install idc-index python - <<'PY' from idc_index import IDCClient IDCClient().download_from_selection(collection_id="pdxnet", downloadDir=".") PY - List the series before pulling pixelsFor agents
List modality and size first so an agent can choose what to fetch.
https://learn.canceridc.dev/python - <<'PY' from idc_index import IDCClient i = IDCClient().index s = i[i["collection_id"] == "pdxnet"] 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
Provenance
All of it, and more, is in this record’s JSON.