README.md

---
license: cc-by-3.0
task_categories:
- image-segmentation
tags:
- medical
- mri
- dce-mri
- breast
- breast-cancer
- tumor-segmentation
- tcia
- tcga
pretty_name: TCGA-Breast-Radiogenomics
size_categories:
- n<1K
configs:
- config_name: preview
  data_files:
  - split: train
    path: preview/train-*
dataset_info:
  config_name: preview
  features:
  - name: patient_id
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: overlay
    dtype: image
  - name: split
    dtype: string
  - name: slice_index
    dtype: int32
  - name: num_slices
    dtype: int32
  - name: rows
    dtype: int32
  - name: cols
    dtype: int32
  - name: tumor_voxels
    dtype: int32
  - name: tumor_voxels_in_slice
    dtype: int32
  - name: series_description
    dtype: string
  - name: n_phases
    dtype: int32
  - name: breast_side
    dtype: string
  - name: tissue_source_site
    dtype: string
  - name: in_bcss
    dtype: bool
  - name: series_reassigned
    dtype: bool
  splits:
  - name: train
    num_bytes: 19932079
    num_examples: 91
  download_size: 19936139
  dataset_size: 19932079
---

# TCGA-Breast-Radiogenomics

Whole-lesion **breast tumour** segmentation on dynamic contrast-enhanced (DCE)
MRI. 91 patients from the TCGA-BRCA cohort, each with one binary mask of the
primary invasive carcinoma, paired with its post-contrast source volume.

The upstream TCIA product is an *analysis result*: a 105 KB archive of masks in an
undocumented `.les` format with **no image reference of any kind**, plus a set of
spreadsheets. This mirror decodes those masks, resolves each one to its owning
DICOM series, reconstructs the spatial volume, and ships both as voxel-aligned
NIfTI — so none of that has to be repeated downstream.

## Dataset Details

| Field | Value |
|---|---|
| Modality | MRI, breast DCE (T1 post-contrast; GE VIBRANT / SAG 3D / BRAVA) |
| Body part | Breast |
| Task | Binary 3D segmentation (primary tumour) |
| Patients | 91 (one study, one series, one lesion each) |
| Classes | 1 + background |
| In-plane grid | 512x512 (46 cases), 256x256 (45) |
| Slices per volume | 41-172 (median 94) |
| In-plane spacing | 0.55-0.94 mm |
| Field strength | 1.5 T (GE Medical Systems, 90/91) |
| Format | NIfTI (`.nii.gz`), converted from DICOM + `.les` |
| License | CC BY 3.0 Unported (commercial use permitted) |

## Labels

| Value | Structure |
|---|---|
| 0 | background |
| 1 | primary invasive breast carcinoma (whole lesion) |

One lesion per patient — the masks are binary, with no multi-focal or multi-class
structure. Lesion size ranges from **311 to 54,188 voxels** (median 2,428), spread
over 4-30 slices. This is a small-target segmentation problem: the median lesion
occupies well under 0.1% of its volume.

## Layout

```
volumes/TCGA-AO-A03M/image.nii.gz   # post-contrast DCE volume
volumes/TCGA-AO-A03M/mask.nii.gz    # uint8 binary mask, identical geometry
...
metadata.jsonl                      # one record per patient
label_map.json                      # label -> name
```

`mask.nii.gz` carries the same affine as its `image.nii.gz`, so the pair is
voxel-aligned with no resampling.

Each `metadata.jsonl` record holds `patient_id`, `image`, `mask`, `split`,
`num_slices`, `rows`, `cols`, `spacing_xyz`, `tumor_voxels`, `tumor_slices`,
`series_description`, `series_uid`, `n_phases`, `phase_index`, acquisition
parameters, the BI-RADS consensus reads (`breast_side`, `birads_shape`,
`birads_margin`, `birads_internal_enhancement`, `birads_fibroglandular`,
`birads_background_enhancement`), `tissue_source_site`, and the two provenance
flags described below (`in_bcss`, `series_reassigned`).

## Splits

**There is no official train/val/test split.** All 91 cases are published as a
flat pool and are labelled `train` in `metadata.jsonl`. Any split is your own
construction — group on `patient_id`.

## How this mirror was built (and why you want it)

The upstream release cannot be consumed directly. Three steps were required, each
verified against all 91 cases.

### 1. `.les` is Fortran-ordered — silently

Each `.les` file is a 12-byte header (`6 x uint16 LE` = an *inclusive* bounding box
`y_start, x_start, z_start, y_end, x_end, z_end`) followed by
`(dy)(dx)(dz)` uint8 voxels in `{0, 1}`. The body is **column-major**, because
TCIA's reference reader is MATLAB.

A C-order `reshape` does not raise — it just returns a shredded mask:

| reshape order | mean 3-D connected components | masks that are a single blob |
|---|---|---|
| C-order (numpy default) | 182.0 | 0 / 91 |
| **Fortran order** | **1.04** | **87 / 91** |

### 2. The mask names no series — a separate spreadsheet does

`.les` files carry no SeriesInstanceUID, affine, spacing or frame of reference.
Matching on slice count alone is hopeless: **0 of 91 patients have a unique
candidate series** (median 9 candidates each). The owning series is recoverable
only from the `SERIES_UID` column of `tcga-breast-radiologist-reads.xls`, which the
segmenting team used to pick "the sequence that corresponded to the one on which
the radiologists annotated the truth". Across the 3 reviewers per patient: 68
unanimous, 19 resolved by 2-of-3 majority, 4 three-way ties.

### 3. DCE series are temporally interleaved

The designated series are multiphase acquisitions with up to **7 temporal phases
in a single series** (e.g. 410 files over 82 distinct slice positions = 5 phases).
The spatial volume is recovered by grouping on `ImagePositionPatient`; only then
does the mask's `z` index address the right slice. This mirror ships the **first
post-contrast phase** (`phase_index` / `n_phases` record which was taken).

### Voxel convention, resolved empirically

TCIA never documents whether `.les` axis 0 is the DICOM row or column, nor the
slice direction. All 16 transpose/flip combinations were scored by whether they
place the mask on *enhancing* voxels — a breast tumour on post-contrast DCE is
bright relative to its immediate surroundings. The result is unambiguous:

| convention | median lesion-vs-surround contrast |
|---|---|
| **axis0=row, axis1=col, axis2=slice ascending, no flips** | **+0.895** |
| next-best of the other 15 | +0.073 |

The winner is positive in **25/25** probed patients; every alternative is
indistinguishable from noise. Independently confirmed by laterality: the mask
centroid falls in the breast recorded by the radiologists in **89/89** patients
with a recorded side (2 patients are `Not Applicable`), at 64-114 mm off midline.

### Four patients were reassigned to a different series

For 4 patients the reads-designated series is demonstrably wrong — the mask lands
on *negative* contrast and/or in the wrong breast. All 4 were 2-of-3 majority
votes, and 3 of them designate a 512x512 `BRAVA` while the patient also has a
256x256 `VIBRANT` DCE series that the mask fits perfectly. Every plausible
alternative series was scored with the same test, excluding subtraction series
(whose post-minus-pre construction makes any lesion trivially bright and would not
be comparable to the other 87) and non-post-contrast sequences:

| patient | designated (rejected) | contrast | reassigned to | contrast |
|---|---|---|---|---|
| TCGA-AO-A03V | T1 Axial post fat sat | -0.145 | T1 Sagittal post fat sat | +0.617 |
| TCGA-AR-A1AX | brava (1 Min.) | -0.284 | VIBRANT | +0.932 |
| TCGA-AR-A24S | BRAVA isotropic | +0.048 | vibrant | +1.203 |
| TCGA-AR-A24X | brava (1 Min.) | -0.029 | VIBRANT | +0.700 |

These carry `series_reassigned: true`. The 4 *three-way ties* are unaffected — all
4 validated cleanly on their majority pick.

After reassignment: **91/91 cases have positive lesion contrast** (median +0.955,
minimum +0.337) and 89/89 agree on laterality.

## Provenance and integrity notes

**Official source, counts verified.** Downloaded from TCIA directly (masks and
spreadsheets over plain HTTPS; images via the unauthenticated NBIA REST API). No
registration and no re-host is involved. All TCIA-stated figures reproduce exactly
from the NBIA digest.

**This is a 91-patient subset of TCGA-BRCA**, which has 139 imaging patients; the
other 48 have no segmentation. The 91 mask barcodes are a strict subset (91 inside,
0 outside). Note also that the associated papers analyse **84** cases (after a
gene-expression filter) while TCIA released **91** masks.

**"Radiogenomics" names the study, not the contents.** The genomic data lives at the
NCI GDC and is *not* included here; this package is imaging + masks. Join on the
TCGA patient barcode to recover it.

**Only the mask-bearing series are mirrored.** The analysis result spans 1,129
series (36 GB) across these 91 patients, but only one series per patient carries a
lesion. The other sequences (T2, pre-contrast, subtraction, DWI) are available from
TCIA under the same barcode.

### ⚠️ Patient overlap with TCGA histopathology datasets

The cross-reference key is the **TCGA patient barcode** (`TCGA-XX-XXXX`), which is
also the DICOM `PatientID` and the GDC `submitter_id`, and is preserved verbatim as
`patient_id` / `tcga_patient_barcode`.

**10 of these 91 patients also appear in BCSS**, and the same slide pool feeds
**NuCLS** and **Pan-Cancer-Nuclei-Seg**:

`TCGA-AO-A12F`, `TCGA-AR-A1AQ`, `TCGA-BH-A0B3`, `TCGA-BH-A0BG`, `TCGA-BH-A0E0`,
`TCGA-BH-A0RX`, `TCGA-E2-A150`, `TCGA-E2-A159`, `TCGA-E2-A1B6`, `TCGA-E2-A1L7`

These carry `in_bcss: true`. No pixels are shared — that is H&E histopathology and
this is MRI — so it is not a mask conflict, but the same humans appear on both
sides of any multimodal split. Group on the barcode.

**No overlap** with Duke-Breast-Cancer-MRI (922 patients, `Breast_MRI_###` IDs, zero
TCGA barcodes), I-SPY1/I-SPY2, ACRIN-6698, QIN-Breast, Breast-MRI-NACT-Pilot,
BreastDM, RIDER, or the Medical Segmentation Decathlon (which has no breast task).

**⚠️ MAMA-MIA is not a zero-shot baseline here.** MAMA-MIA excludes TCGA-BRCA from
its data, but its segmentation model was *trained* on 331 cases including 80
sagittal TCGA-BRCA cases. Those masks were never released, so MAMA-MIA is also not
an alternative mask source for this cohort.

## Citation

```bibtex
@misc{morris2014tcgabreastradiogenomics,
  title     = {Using Computer-extracted Image Phenotypes from Tumors on Breast
               {MRI} to Predict Stage},
  author    = {Morris, Elizabeth and Burnside, Elizabeth and Whitman, Gary and
               Zuley, Margarita and Bonaccio, Ermelinda and Ganott, Marie and
               Sutton, Elizabeth and Net, Jose and Brandt, Kathleen and
               Li, Hui and Drukker, Karen and Perou, Charles and Giger, Maryellen L.},
  year      = {2014},
  publisher = {The Cancer Imaging Archive},
  doi       = {10.7937/K9/TCIA.2014.8SIPIY6G}
}

@article{burnside2016usingcomputer,
  title   = {Using computer-extracted image phenotypes from tumors on breast
             magnetic resonance imaging to predict breast cancer pathologic stage},
  author  = {Burnside, Elizabeth S. and Drukker, Karen and Li, Hui and
             Bonaccio, Ermelinda and Zuley, Margarita and Ganott, Marie and
             Net, Jose M. and Sutton, Elizabeth J. and Brandt, Kathleen R. and
             Whitman, Gary J. and Conzen, Suzanne D. and Lan, Li and
             Ljung, Britt-Marie and Morris, Elizabeth A. and Perou, Charles M. and
             Giger, Maryellen L.},
  journal = {Cancer},
  volume  = {122},
  number  = {5},
  pages   = {748--757},
  year    = {2016},
  doi     = {10.1002/cncr.29791}
}

@misc{lingle2016tcgabrca,
  title     = {The Cancer Genome Atlas Breast Invasive Carcinoma Collection
               ({TCGA-BRCA})},
  author    = {Lingle, W. and Erickson, B. J. and Zuley, M. L. and Jarosz, R. and
               Bonaccio, E. and Filippini, J. and Net, J. M. and Levi, L. and
               Morris, E. A. and Figler, G. G. and Elnajjar, P. and Kirk, S. and
               Lee, Y. and Giger, M. and Gruszauskas, N.},
  year      = {2016},
  publisher = {The Cancer Imaging Archive},
  doi       = {10.7937/K9/TCIA.2016.AB2NAZRP}
}

@article{clark2013tcia,
  title   = {The Cancer Imaging Archive ({TCIA}): Maintaining and Operating a
             Public Information Repository},
  author  = {Clark, Kenneth and Vendt, Bruce and Smith, Kirk and others},
  journal = {Journal of Digital Imaging},
  volume  = {26},
  number  = {6},
  pages   = {1045--1057},
  year    = {2013},
  doi     = {10.1007/s10278-013-9622-7}
}
```