PET-CT-Radiomics-Classifier

A comprehensive pipeline for multi-model classification of PET/CT radiomics data, including individual classifiers and decision-level fusion via soft voting. ROC curves are used to evaluate performance on both individual models and the ensemble.

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📌 Project Overview

This project performs binary classification (progression and death prediction) based on radiomics features extracted from PET and CT scans. We compare 9 classic machine learning models, 4 advanced tabular models, and a fusion ensemble. The pipeline includes:

• Feature selection
• Train/test split
• Model training and ROC evaluation
• Soft-voting fusion
• Visualization with ROC curves and SHAP values

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🧬 Radiomics Feature Extraction

We extract 3D radiomics features from CT and PET scans using PyRadiomics. Each scan is paired with a patient-specific binary or labeled segmentation mask.

🗂 Input Folders

• /H/ROI/ — contains patient masks in .nii format
• /H/converted_nii/ — contains CT and PET images in NIfTI format (suffix _CT, _PET)
• tumor.yaml — PyRadiomics parameter configuration file

⚙️ Process Overview

For each patient:

• Extract patient name from the folder
• Match corresponding CT and PET images using the name fragment
• Load images using SimpleITK (fallback to nibabel if needed)
• Resample CT and PET images to align with the mask resolution
• Extract features using PyRadiomics with YAML configuration
• Save features separately for CT and PET

📁 Output Files

File	Description
radiomics_features_all.xlsx	Excel file with CT and PET feature sheets
failed_cases_all.csv	Log of patients with missing or invalid data

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🧪 Classification Models Used

• Random Forest
• Logistic Regression
• SVM (RBF Kernel)
• Decision Tree
• K-Nearest Neighbors
• Gaussian Naive Bayes
• Gradient Boosting
• AdaBoost
• XGBoost
• Fusion Model (Soft Voting)
• MotherNet
• TabPFNv2
• GAMformer
• Tabflex

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📊 Tasks

Four classification tasks are implemented:

• PET — Progression Prediction
• PET — Death Prediction
• CT — Progression Prediction
• CT — Death Prediction

Each task uses 10 selected radiomics features.

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📈 Visualizations

• ROC curves per task (train set)
• Annotated AUC values per model
• Fusion model included in each plot
• Optional SHAP value plots (coming soon)

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📐 Index Calculation

• Precision:
  Precision = TP / (TP + FP)

• Recall:
  Recall = TP / (TP + FN)

• F1-Score:
  F1 = 2 * (Precision * Recall) / (Precision + Recall)

• Accuracy:
  Accuracy = (TP + TN) / (TP + TN + FP + FN)

• AUC (Area Under the Curve):
  AUC is calculated from the ROC curve, which plots the True Positive Rate (TPR) against the False Positive Rate (FPR).

  • TPR (Recall) = TP / (TP + FN)
  • FPR = FP / (FP + TN)

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🎯 Prediction Output

After training on all valid patient samples for each task, the fusion model (soft-voting VotingClassifier) outputs individual prediction probabilities.

📄 Output File

File	Description
fusion_patient_probabilities.xlsx	Predicted probabilities per patient using the fusion model

📑 Columns in Output Excel

Column	Description
patient	Patient ID extracted from the Excel sheet
dataset	PET or CT
label	Target label: progression or death
true_label	Ground truth (0 or 1)
fusion_probability	Predicted probability of the positive class (label = 1)

Example (NOT THE REAL ONE):

patient	dataset	label	true_label	fusion_probability
P001	PET	progression	1	0.84
P001	PET	death	0	0.21
P001	CT	progression	1	0.75
P001	CT	death	0	0.32

This result can be used for patient-level risk stratification and decision support.

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📚 References

• TICL (Tabular In-Context Learning) - Microsoft
• TabPFN - PriorLabs
• PyRadiomics - AIM-Harvard

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🛠 Requirements

pip install pandas matplotlib scikit-learn xgboost openpyxl
pip install pyradiomics SimpleITK nibabel numpy tqdm