chehablab/DBCM

DUKE-BREAST-CANCER-MRI — 2D Slice-Based Multisequence Breast MRI Dataset with Lesion Segmentation

This dataset is derived from the Duke Breast Cancer MRI collection, a large-scale breast MRI dataset with pathologically confirmed lesions, hosted on The Cancer Imaging Archive (TCIA). The original collection provides multi-sequence 3D breast MRI volumes with bounding-box lesion annotations and comprehensive clinical, pathological, and molecular metadata. Here, we provide a 2D slice-based version, extracted from two of the available MRI sequences per patient — pre-contrast T1-weighted (pre) and post-contrast T1-weighted (post1) — paired slice-for-slice with the corresponding lesion segmentation mask and patient-level clinical/pathological metadata. The dataset is designed for multimodal breast lesion segmentation, classification, and prognostic modeling on breast MRI.

📦 Dataset Structure

The dataset has a single train split, with each row corresponding to a single 2D axial slice from a patient's coregistered pre / post1 volumes:

Field	Description
volume_id	Unique patient/case identifier (e.g., Breast_MRI_001)
slice_id	Index of the axial slice within the volume
pre	2D pre-contrast T1-weighted MRI slice
post1	2D post-contrast T1-weighted MRI slice (first post-contrast acquisition)
pre_series	Series UID identifier for the pre-contrast sequence
post1_series	Series UID identifier for the post1-contrast sequence
mask	2D binary lesion segmentation mask (bounding box annotation)
is_tumorous	Boolean — whether a lesion is present on this slice
menopause	Menopausal status: 0 (pre-menopausal), 1 (post-menopausal)
race	Patient race/ethnicity (encoded categorical)
er	Estrogen receptor status: 0 (negative), 1 (positive)
pr	Progesterone receptor status: 0 (negative), 1 (positive)
her2	HER2 status: 0 (negative), 1 (positive), 2 (equivocal)
tubule_tumor_grade	Tubule formation grade (Nottingham): 1, 2, or 3
nuclear_tumor_grade	Nuclear pleomorphism grade (Nottingham): 1, 2, or 3
mitotic_tumor_grade	Mitotic count grade (Nottingham): 1, 2, or 3
nottingham_grade	Combined Nottingham histologic grade: 1, 2, or 3
breast_density	Breast tissue density (BI-RADS): 1, 2, 3, or 4
recurrence	Binary recurrence outcome: 0 (no recurrence), 1 (recurrence)

menopause, race, er, pr, her2, tubule_tumor_grade, nuclear_tumor_grade, mitotic_tumor_grade, nottingham_grade, breast_density, and recurrence are patient-level attributes and are therefore identical across all slices belonging to the same volume_id.

⚙️ Preprocessing

• For each patient, preprocessed pre-contrast T1 and post1-contrast T1 DICOM series were extracted and converted to image format
• Volumes were sliced into 2D axial slices; the bounding-box lesion segmentation mask was created per slice from annotated lesion regions
• Patient-level clinical and pathological metadata (menopause, race, er, pr, her2, tumor grades, breast_density, recurrence) were broadcast from the Duke Breast Cancer MRI clinical database to every slice belonging to that patient
• All slices (lesion-containing and background) were exported; use is_tumorous to filter or balance
• The following patient cases were excluded during processing: Breast_MRI_065, Breast_MRI_120, Breast_MRI_279, Breast_MRI_596, Breast_MRI_700, Breast_MRI_433, Breast_MRI_627

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🚀 Usage

from datasets import load_dataset
import matplotlib.pyplot as plt
import numpy as np

ds = load_dataset("YOUR_USERNAME/DUKE_BREAST_CANCER_MRI", split="train")

# Grab a lesion-containing slice and view both sequences plus the mask overlay
sample = next(s for s in ds if s["is_tumorous"])

fig, axes = plt.subplots(1, 3, figsize=(14, 4))

# Pre-contrast with mask
axes[0].imshow(sample["pre"], cmap="gray")
axes[0].imshow(np.array(sample["mask"]), cmap="jet", alpha=0.4)
axes[0].set_title("Pre-Contrast + Mask")
axes[0].axis("off")

# Post1-contrast with mask
axes[1].imshow(sample["post1"], cmap="gray")
axes[1].imshow(np.array(sample["mask"]), cmap="jet", alpha=0.4)
axes[1].set_title("Post1-Contrast + Mask")
axes[1].axis("off")

# Enhancement map (post1 - pre)
enhancement = np.array(sample["post1"]) - np.array(sample["pre"])
axes[2].imshow(enhancement, cmap="RdBu_r")
axes[2].set_title("Enhancement (Post1 - Pre)")
axes[2].axis("off")

# Clinical and pathological metadata
info_text = (
    f"{sample['volume_id']} | Slice {sample['slice_id']}\n"
    f"ER: {sample['er']}, PR: {sample['pr']}, HER2: {sample['her2']}\n"
    f"Nottingham Grade: {sample['nottingham_grade']} | "
    f"Breast Density: {sample['breast_density']}\n"
    f"Recurrence: {sample['recurrence']}"
)
fig.suptitle(info_text, fontsize=10)
plt.tight_layout()
plt.show()

---

🏷️ Feature Descriptions

Receptor Status

Molecular markers predictive of treatment response and prognosis:

• ER (Estrogen Receptor): 0 = Negative, 1 = Positive
• PR (Progesterone Receptor): 0 = Negative, 1 = Positive
• HER2 (Human Epidermal Growth Factor Receptor 2): 0 = Negative, 1 = Positive, 2 = Equivocal

Nottingham Histologic Grading System

Combined assessment of tumor differentiation and proliferation. Comprises three components, each graded 1–3:

• Tubule Formation (tubule_tumor_grade): Percentage of tumor forming tubular structures
• Nuclear Pleomorphism (nuclear_tumor_grade): Degree of variation in nuclear size and shape
• Mitotic Count (mitotic_tumor_grade): Number of mitotic figures per high-power field

Combined Grade (nottingham_grade): Sum of components; 1–3 (favorable), 4–5 (intermediate), 6–9 (unfavorable)

Breast Density

BI-RADS density classification (tissue composition affecting lesion detectability):

• 1 = Almost entirely fat
• 2 = Scattered fibroglandular densities
• 3 = Heterogeneously dense
• 4 = Extremely dense

Menopausal Status

• 0 = Pre-menopausal
• 1 = Post-menopausal

Recurrence Outcome

• 0 = No recurrence during follow-up
• 1 = Recurrence during follow-up

🧪 Use Cases

• Multimodal (pre / post1-contrast T1) breast lesion segmentation and detection
• Breast cancer receptor status (ER/PR/HER2) classification from imaging
• Pathologic grade prediction from MRI
• Prognostic modeling: predicting recurrence risk from imaging and clinical features
• Enhancement kinetics analysis: examining lesion enhancement patterns across pre/post acquisitions
• Multi-task learning combining segmentation with receptor status, grade, and recurrence prediction
• Representation learning and self-supervised pretraining for breast cancer MRI
• Breast density characterization and tissue composition analysis

⚠️ Important Notes

• Class imbalance: most slices, particularly toward the superior/inferior extremes of a volume, contain no lesion. Use is_tumorous to filter or apply weighted sampling.
• Volume-level splits: always split train/val/test at the volume_id level, never at the slice level, to avoid data leakage between sets.
• Lesion annotations: lesion regions are provided as bounding-box masks; voxels within the bounding box may include surrounding tissue rather than tumor proper.
• Mask-image alignment: mask is in the same 2D slice space as pre and post1 for a given row, so no additional registration is needed to overlay it.
• Missing data: a small number of cases may have incomplete clinical or pathological information; refer to the original TCIA collection for metadata details.
• Patient exclusions: seven patient cases were excluded from this release.

📚 Citation

This dataset is derived from data made available on The Cancer Imaging Archive (TCIA) under a CC BY-NC 4.0 license. If you use this dataset, please acknowledge [chehablab.com](Chehab Lab) and cite the original Duke collection:

@misc{Whissel2022,
  author = {Whissel, C. M. and Georgiade, G. S. and Sinha, S. and Sinha, U. P.},
  title  = {Duke Breast Cancer MRI [dataset]},
  year   = {2022},
  publisher = {The Cancer Imaging Archive},
  doi    = {10.7937/tcia.d8xv-ke92}
}

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📜 License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, consistent with the source data on TCIA.

You may copy, modify, distribute, and use the data for non-commercial purposes only, provided that appropriate credit is given to the original authors, TCIA, and the Duke Breast Cancer MRI collection.

Chehab Lab @ 2026