Datasets/pseudo_phi_dicom_data

Pseudo-PHI-DICOM-Data

OpenMachine-extracted

NCI Imaging Data Commons / The Cancer Imaging ArchiveAvailable from IDC

The process of image de-identification is time consuming, requires significant human resources, and is prone to human error. Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted. This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms. We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. Please see the Medical Imaging De-Identification Initiative (MIDI) wiki page to learn more about the images and to obtain any supporting metadata for this collection.

Can it answer my research question?Who has used it Get the data

Can it answer your research question?

0 of 6 supported

Overall survival
Not measured
Clinical field completeness has not been measured for this record.
Progression or recurrence
Not measured
Clinical field completeness has not been measured for this record.
Treatment response
Not measured
Clinical field completeness has not been measured for this record.
Which drug was given
Not measured
Clinical field completeness has not been measured for this record.
Stage-adjusted modelling
Not measured
Clinical field completeness has not been measured for this record.
Analysis by race or ethnicity
Not measured
Clinical field completeness has not been measured for this record.

Judged from the repository’s own field completeness (thresholds). “Not measured” means we have not probed that field for this record, not that the data lack it.

At a glance

Every percentage is the repository’s own record of what is filled in

CasesAPI

Measurement types

Follow-up

None recorded

no survival endpoint

Treatment response

Unknown

SubjectCALC

Pan-cancer

As filed by the repository

VariousVarious

Who is in the cohort

The repository’s harmonized records carry no demographics for this dataset.

What was measured, and on how many

1 measurement type, 1 covering the whole cohort.

DICOM medical imaging42 of 42 · 100%

Platform, files and data levels

Assay	Platform	Files	Levels
DICOM medical imagingAPI	-	-	DICOM series

Access

OpenDownload without an account.API

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

1 blocking

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

BlockingMissing dataAPI

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.

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.

Analyzed the dataCALC

could not be measured for this dataset

Declared using it

named in a data availability statement

Cited the paper

attention to the publication, not reuse of the data

Against expectation

Not assessed

Reuse cannot be traced for this dataset

No accession is specific enough to search the literature for, so we cannot tell how often these data have been reused. That is a limit of the measurement, not a finding about the dataset. It is also a barrier in itself: a researcher cannot find prior work to build on.

Reuse assessment

Not assessed: this dataset has no accession specific enough for citation-based reuse tracing, so a shortfall cannot be measured.

Compared against rgi-huber-v2. See Methods for the model.

Articles that analyzed the data

Not searchable. See the note above.

Community examples

Share your example

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.

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

pip install idc-index. Images are public; the package uses s5cmd under the hood.

from idc_index import IDCClient

COLLECTION = "pseudo_phi_dicom_data"
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="./pseudo_phi_dicom_data")

This page as data: limitations firstbash

curl -s https://cd2s.vercel.app/data/datasets/idc-pseudo-phi-dicom-data.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
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=pseudo_phi_dicom_data
Generated from repository policy. Check the linked policy before downloading.
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
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="pseudo_phi_dicom_data", downloadDir=".")
PY
```
https://github.com/ImagingDataCommons/idc-index
Generated from repository policy. Check the linked policy before downloading.

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"] == "pseudo_phi_dicom_data"]
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.

Provenance

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

Identifiers

IDC collectionpseudo_phi_dicom_data API
TCIA collectionPseudo-PHI-DICOM-Data API
DOI10.7937/s17z-r072TEXT

Versions and verification

Metadata retrieved: 18 Sept 2026
Review status: Machine-extracted
Data generation funding: Role not established

For software record JSON