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- """
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- Simple example of using Sybil for lung cancer risk prediction
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- """
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-
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- import sys
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- import os
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-
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- # Install required packages if needed
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- # !pip install torch torchvision transformers pydicom torchio sybil
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-
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- # Import the model
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- from modeling_sybil_wrapper import SybilHFWrapper
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- from configuration_sybil import SybilConfig
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-
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- def predict_cancer_risk(dicom_paths):
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- """
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- Predict lung cancer risk from DICOM files.
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-
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- Args:
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- dicom_paths: List of paths to DICOM files from a CT scan
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-
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- Returns:
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- Risk scores for years 1-6
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- """
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- # Load model
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- print("Loading Sybil model...")
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- config = SybilConfig()
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- model = SybilHFWrapper.from_pretrained("Lab-Rasool/sybil")
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-
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- # Run prediction
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- print(f"Processing {len(dicom_paths)} DICOM files...")
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- output = model(dicom_paths=dicom_paths, return_attentions=False)
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-
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- # Extract risk scores
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- risk_scores = output.risk_scores.numpy()
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-
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- return risk_scores
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-
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-
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- def main():
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- # Example usage with demo data
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- # In practice, replace with actual DICOM file paths
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- demo_dicom_paths = [
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- "path/to/slice001.dcm",
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- "path/to/slice002.dcm",
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- # ... more slices
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- ]
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-
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- # For testing, you can download demo data:
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- # Download from: https://github.com/reginabarzilaygroup/Sybil
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-
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- print("=" * 50)
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- print("Sybil Lung Cancer Risk Prediction")
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- print("=" * 50)
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-
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- # Uncomment when you have actual DICOM files:
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- # risk_scores = predict_cancer_risk(demo_dicom_paths)
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-
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- # Print results
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- # print("\nLung Cancer Risk Predictions:")
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- # print("-" * 30)
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- # for year, score in enumerate(risk_scores, 1):
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- # risk_pct = score * 100
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- # print(f"Year {year}: {risk_pct:.2f}% risk")
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-
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- print("\nNote: This example requires actual DICOM files.")
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- print("Please provide paths to LDCT scan DICOM files.")
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- print("\nFor more information:")
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- print("- Original paper: https://doi.org/10.1200/JCO.22.01345")
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- print("- GitHub: https://github.com/reginabarzilaygroup/Sybil")
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-
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-
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- if __name__ == "__main__":
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- main()