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metadata
license: cc-by-nc-4.0
pretty_name: 2025 Dr. Robert Gillies ML Workshop - Cancer Survival Prediction
tags:
  - medical
  - cancer
  - glioblastoma
  - pathology
  - clinical-data
  - machine-learning
  - competition
  - survival-prediction
  - medical-imaging
  - whole-slide-imaging
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: train/train.csv
      - split: test
        path: test/test.csv

2025 Dr. Robert Gillies Machine Learning Workshop in Cancer - Hackathon Dataset

Dataset Description

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:

  1. C-Index (Concordance Index) - 40%
  2. AUROC (Area Under ROC Curve) - 30%
  3. Accuracy - 20%
  4. 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:

  1. Loading CSV files directly using pandas with hf:// protocol
  2. Downloading specific images on-demand using hf_hub_download()
  3. Using streaming=True if iterating through the dataset
  4. 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

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