| --- |
| 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} |
| } |
| ``` |
|
|
