AI-Driven Radiomics for Precision Prostate Cancer Therapy

Authors: Tsz Him Chan, Annette Haworth, Alan Wang, Mahyar Osanlouy et al.
Publication: EJNMMI Research (2023)
Code: Available on Request

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Overview

This pioneering study developed an AI-powered radiomics pipeline to optimize biologically targeted radiation therapy (BiRT) for prostate cancer. By integrating PSMA PET/CT with multiparametric MRI (mpMRI), we created voxel-level predictions of tumor location and grade through advanced image registration and machine learning techniques.

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Key Innovations

1. Multi-modal fusion: First study combining PSMA PET radiomics with DCE MRI perfusion parameters
2. Voxel-wise prediction: Achieved 0.89 AUC for tumor detection using 3D radiomic features
3. Grade differentiation: Developed two-stage RFC model separating high/low-grade disease (Accuracy: 0.67-0.99)
4. HPC-optimized registration: Reduced spatial uncertainty to 3.3mm using deformable histology alignment

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Computational Pipeline

1. Multi-modal Image Registration

• Data Integration: Co-registered PSMA PET/CT (5 scanners), mpMRI (2x Siemens 3T), and whole-mount histology
• Key Steps:

align_pet_ct() → rigid_registration(mpMRI) →
deformable_registration(ex_vivo_MRI) →
histology_annotation_propagation()

• Challenges Solved:
• Bladder filling artifacts in PET
• Partial volume effects in 3.27mm PET slices
• Non-linear prostate deformation post-resection

2. Radiomic Feature Engineering

Modality	Features Extracted	Key Parameters
PSMA PET	3D LoG, LBP, GLCM textures	SUVmax, metabolic tumor volume
DCE MRI	Ktrans, iAUGC60, TTP perfusion maps	Pharmacokinetic modeling
ADC Maps	NGTDM coarseness, percentile values	b=1200 s/mm² diffusion restriction

Feature Selection:

• ANOVA filtering → Gini impurity ranking
• Final feature set: 50 most discriminative parameters

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Machine Learning Architecture

Tumor Detection Model

• Two-stage RFC Framework:

1. Location Detection: 842 sensitivity/804 specificity
2. Grade Classification: Low vs High Grade (ISUP ≥3)

• Performance Comparison:

Model	AUC	Sensitivity	Specificity
PET Alone	0.865	0.781	0.799
mpMRI Alone	0.882	0.802	0.801
Combined	0.890	0.842	0.804

Receiver operating characteristics for different models

Key Radiomic Predictors

1. PET: 3D LoG(σ=3mm) minimum
2. ADC: NGTDM Coarseness
3. DCE MRI: Ktrans 90th percentile

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Clinical Implications

• Personalized Radiotherapy: Enables voxel-level dose painting based on metabolic/perfusion features
• Early Recurrence Prediction: High-grade lesions showed 2.8× higher PSMA uptake (p<0.01)
• Technical Impact:
• Solved partial volume effects in PET-guided planning
• Demonstrated perfusion > diffusion for grade prediction
• Open-source registration framework [3D Slicer Plugin]

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Future Directions

• Multi-institutional validation across PET/MRI scanners
• DL Enhancement: Replace handcrafted features with 3D CNNs
• Real-time Adaptation: Integrate with MR-Linac systems

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This project received funding from Prostate Cancer Foundation of Australia and Health Research Council of New Zealand.