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Updated: Aug 1, 2025
Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
Published on: April 9, 2019
Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically
Tsz Him Chan1, Annette Haworth2, Alan Wang1,3,4
1Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Background:
Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade.
Methods:
PSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels.
Results:
Perfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992.
Conclusions:
Machine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning.
Insights
Machine learning models combining PSMA PET/CT and mpMRI radiomic features accurately predict prostate cancer lesions and grade. This integration aids in planning targeted radiation therapy for improved patient outcomes.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
- Machine Learning
Background:
- Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are key for identifying intra-prostatic lesions (IPLs) in localized prostate cancer.
- Accurate tumor characterization is crucial for effective treatment planning.
Purpose of the Study:
- To evaluate PSMA PET/CT and mpMRI for biologically targeted radiation therapy planning.
- To analyze voxel-wise imaging parameter relationships.
- To assess radiomic machine learning models for predicting tumor location and grade.
Main Methods:
- Co-registration of PSMA PET/CT and mpMRI data with histopathology from 19 prostate cancer patients.
- Calculation of Apparent Diffusion Coefficient (ADC) maps and DCE MRI parameters.
- Voxel-wise correlation analysis and development of classification models using radiomic and clinical features.
Main Results:
- DCE MRI perfusion parameters showed stronger correlation with PET SUV than ADC or T2w.
- A Random Forest Classifier using combined PET and mpMRI radiomic features achieved high performance (AUC 0.890) for IPL detection.
- Tumor grading models demonstrated high accuracy, ranging from 0.671 to 0.992.
Conclusions:
- Machine learning classifiers integrating radiomic features from PSMA PET and mpMRI show significant potential.
- These models can accurately predict IPLs and differentiate high-grade from low-grade prostate cancer.
- This approach could enhance biologically targeted radiation therapy planning.
