--- 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} } ```