Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy.
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| Název: | Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy. |
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| Autoři: | Chan, Tsz Him, Haworth, Annette, Wang, Alan, Osanlouy, Mahyar, Williams, Scott, Mitchell, Catherine, Hofman, Michael S., Hicks, Rodney J., Murphy, Declan G., Reynolds, Hayley M. |
| Zdroj: | EJNMMI Research; 4/26/2023, Vol. 13 Issue 1, p1-14, 14p |
| Témata: | CONTRAST-enhanced magnetic resonance imaging, RADIOTHERAPY treatment planning, PROSTATE cancer, RADIOTHERAPY, PROSTATE cancer patients, MAGNETIC resonance imaging |
| Abstrakt: | 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. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Complementary Index |
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