This dataset is part of the 2025 Dr. Robert Gillies Machine Learning Workshop at Moffitt Cancer Center. It contains de-identified medical imaging, pathology reports, and clinical data for glioblastoma patients, designed for an overall survival prediction challenge.

Dataset Splits

Training Set: 600 patients with complete survival outcomes
Test Set: 265 patients with withheld survival outcomes

Training Dataset

Total Patients: 600
Pathology Images: 600 whole-slide tissue images (.tif format)
Clinical Records: 600 patient records with comprehensive clinical and pathology data
Total Size: ~481GB

Test Dataset

Total Patients: 265
Pathology Images: 265 whole-slide tissue images (.tif format)
Clinical Records: 265 patient records with predictive features only (survival outcomes withheld)
Total Size: ~213GB
Purpose: Final evaluation with ground truth outcomes held by organizers

Dataset Structure

train/
├── train.csv                           # Clinical and pathology report data (with outcomes)
└── images/                             # Pathology whole-slide images
    ├── metadata.csv                    # Links image files to patient IDs
    ├── P0002.tif                       # Patient pathology slides (~100-174MB each)
    ├── P0003.tif
    └── ... (600 patients)

test/
├── test.csv                            # Clinical features WITHOUT survival outcomes
└── images/                             # Pathology whole-slide images
    ├── metadata.csv                    # Links image files to patient IDs
    ├── P0001.tif                       # Patient pathology slides (~100-174MB each)
    ├── P0004.tif
    └── ... (265 patients)

Clinical Data Schema (Training Set)

The train.csv file in the training set contains the following fields:

Demographics:

patient_id: Unique de-identified patient identifier
age_at_diagnosis: Patient age at diagnosis
gender: Patient gender
race: Patient race
ethnicity: Patient ethnicity

Tumor Characteristics:

primary_diagnosis: Primary cancer diagnosis
tumor_grade: Tumor grade classification
ajcc_m: AJCC M stage (metastasis)
ajcc_n: AJCC N stage (lymph nodes)
classification_of_tumor: Tumor classification (primary/recurrence/progression)
morphology: ICD-O-3 morphology code
tissue_origin: Anatomical site of origin
laterality: Side of body affected
prior_malignancy: History of prior cancer
synchronous_malignancy: Concurrent cancer diagnosis

Clinical Outcomes:

vital_status: Patient vital status (Alive/Dead)
overall_survival_days: Overall survival time in days
overall_survival_event: Survival event indicator (0/1)
days_to_death: Time to death event
days_to_last_followup: Time to last follow-up
cause_of_death: Cause of death
days_to_progression: Time to disease progression
days_to_recurrence: Time to disease recurrence
progression_or_recurrence: Indicator of disease progression/recurrence
disease_response: Response to treatment

Treatment Information:

treatment_types: Types of treatments received
therapeutic_agents: Specific drugs/agents used
days_to_treatment: Time to treatment initiation
treatment_outcome: Outcome of treatment

Pathology:

pathology_report: Complete de-identified pathology report text

Test Set Features

The test.csv file in the test set contains 21 predictive features only. The following 9 survival outcome columns have been removed to prevent participants from calculating C-index:

Withheld Columns:

vital_status (Alive/Dead status)
overall_survival_days (survival time - required for C-index)
overall_survival_event (event indicator - required for C-index)
days_to_death
days_to_last_followup
cause_of_death
days_to_progression
days_to_recurrence
progression_or_recurrence

Available Test Features (21 columns):

All demographic fields (patient_id, age_at_diagnosis, gender, race, ethnicity)
All tumor characteristics (primary_diagnosis, tumor_grade, ajcc_m, ajcc_n, classification_of_tumor, morphology, tissue_origin, laterality, prior_malignancy, synchronous_malignancy)
Treatment information (treatment_types, therapeutic_agents, days_to_treatment, treatment_outcome, disease_response)
Pathology report (pathology_report)

Submission Format

For the overall survival prediction challenge, submit predictions as a CSV file with the following format:

patient_id,outcome,risk_score,confidence
P0001,1,0.742,0.89
P0004,0,0.234,0.76
P0007,1,0.891,0.92

Column Definitions:

patient_id: Patient identifier (must match test set IDs)
outcome: Predicted binary outcome (0 = censored/alive, 1 = event/death)
risk_score: Predicted risk score (higher = higher risk of event)
confidence: Model confidence in prediction (0-1 scale)

Evaluation Metrics:

C-Index (Concordance Index) - 40%
AUROC (Area Under ROC Curve) - 30%
Accuracy - 20%
Calibration - 10%

Usage

Important Notes

This is a multimodal dataset combining:

Large pathology images (~100-174MB each, TIFF format, ~700GB total)
Clinical/tabular data (CSV format with patient metadata)

Due to the large image file sizes, we recommend:

Loading CSV files directly using pandas with hf:// protocol
Downloading specific images on-demand using hf_hub_download()
Using streaming=True if iterating through the dataset
Only downloading the full dataset if you have sufficient storage (~700GB)

Loading the Dataset

from datasets import load_dataset
import pandas as pd

# Load the training set (with outcomes)
train_dataset = load_dataset("Lab-Rasool/hackathon-2025", split="train")
train_data = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/train/train.csv")

# Load the test set (without outcomes)
test_dataset = load_dataset("Lab-Rasool/hackathon-2025", split="test")
test_data = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/test/test.csv")

# Access images with metadata
from huggingface_hub import hf_hub_download
from PIL import Image
import tifffile

# Download and open a specific image
train_img_path = hf_hub_download(
    repo_id="Lab-Rasool/hackathon-2025",
    filename="train/images/P0002.tif",
    repo_type="dataset"
)
train_image = tifffile.imread(train_img_path)

# Or using datasets library with streaming
train_ds = load_dataset("Lab-Rasool/hackathon-2025", split="train", streaming=True)
# Note: For large image files, consider downloading specific files as needed

Working with Images and Clinical Data

This dataset combines large pathology images (TIFF files) with clinical/tabular data. Here's how to efficiently work with both:

import pandas as pd
import tifffile
from huggingface_hub import hf_hub_download, snapshot_download

# Option 1: Download entire dataset (requires ~700GB storage)
# local_dir = snapshot_download(repo_id="Lab-Rasool/hackathon-2025", repo_type="dataset")

# Option 2: Download only CSV files and images on-demand (recommended)
# Load clinical data
train_csv = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/train/train.csv")
test_csv = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/test/test.csv")

# Load image metadata to link images with patient IDs
train_metadata = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/train/images/metadata.csv")
test_metadata = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/test/images/metadata.csv")

# Download specific images as needed
def load_patient_image(patient_id, split="train"):
    """Load pathology image for a specific patient"""
    img_path = hf_hub_download(
        repo_id="Lab-Rasool/hackathon-2025",
        filename=f"{split}/images/{patient_id}.tif",
        repo_type="dataset"
    )
    return tifffile.imread(img_path)

# Example: Load image and clinical data for patient P0002
patient_id = "P0002"
patient_image = load_patient_image(patient_id, split="train")
patient_clinical = train_csv[train_csv['patient_id'] == patient_id]

print(f"Image shape: {patient_image.shape}")
print(f"Clinical data:\n{patient_clinical}")

Example Use Cases

Image Analysis:

# Load pathology image with clinical data
import tifffile
import pandas as pd
from huggingface_hub import hf_hub_download

# Download clinical data
train_data = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/train/train.csv")

# Download specific image
img_path = hf_hub_download(
    repo_id="Lab-Rasool/hackathon-2025",
    filename="train/images/P0002.tif",
    repo_type="dataset"
)
image = tifffile.imread(img_path)

# Get corresponding clinical data
clinical_info = train_data[train_data['patient_id'] == 'P0002']

NLP on Pathology Reports:

import pandas as pd

# Load clinical data with pathology reports
train_data = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/train/train.csv")

# Extract pathology reports
reports = train_data[['patient_id', 'pathology_report']]

Survival Analysis (Training Set Only):

# Analyze survival outcomes (only available in training set)
survival_data = train_data[[
    'patient_id',
    'overall_survival_days',
    'overall_survival_event',
    'vital_status'
]]

Making Predictions on Test Set:

import tifffile
import pandas as pd
from huggingface_hub import hf_hub_download

# Load test features
test_features = pd.read_csv("hf://datasets/Lab-Rasool/hackathon-2025/test/test.csv")

# Download and load a test image
test_img_path = hf_hub_download(
    repo_id="Lab-Rasool/hackathon-2025",
    filename="test/images/P0001.tif",
    repo_type="dataset"
)
test_image = tifffile.imread(test_img_path)

# Extract features for a patient
patient = test_features[test_features['patient_id'] == 'P0001']

# Make predictions with your model
# risk_score = your_model.predict(patient_features)

# Create submission
submission = pd.DataFrame({
    'patient_id': test_features['patient_id'],
    'outcome': predicted_outcomes,
    'risk_score': predicted_risk_scores,
    'confidence': prediction_confidences
})

submission.to_csv('my_submission.csv', index=False)

License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

✅ Academic and research use permitted
✅ Educational use permitted
❌ Commercial use prohibited
✅ Derivative works permitted with attribution

Important Notes

Training Set: Use for model development and training. Contains complete survival outcomes.
Test Set: Use for final predictions. Survival outcomes are withheld for evaluation.
No Self-Evaluation: Ground truth outcomes for the test set are held by competition organizers.
Fair Use: Do not attempt to obtain test set ground truth labels through external sources.
Submission Required: All 265 test patients must be included in submissions with matching patient IDs.

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