| Title |
Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy
|
|---|---|
| Published in |
EJNMMI Research, April 2023
|
| DOI | 10.1186/s13550-023-00984-5 |
| Pubmed ID | |
| Authors |
Tsz Him Chan, Annette Haworth, Alan Wang, Mahyar Osanlouy, Scott Williams, Catherine Mitchell, Michael S. Hofman, Rodney J. Hicks, Declan G. Murphy, Hayley M. Reynolds |
| Abstract |
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. 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. 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. 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 3 | 17% |
| Researcher | 3 | 17% |
| Student > Ph. D. Student | 2 | 11% |
| Other | 1 | 6% |
| Student > Doctoral Student | 1 | 6% |
| Other | 3 | 17% |
| Unknown | 5 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 3 | 17% |
| Medicine and Dentistry | 3 | 17% |
| Engineering | 2 | 11% |
| Physics and Astronomy | 1 | 6% |
| Nursing and Health Professions | 1 | 6% |
| Other | 2 | 11% |
| Unknown | 6 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2023.
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#21,376,027
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Outputs from EJNMMI Research
#418
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#310,019
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Outputs of similar age from EJNMMI Research
#14
of 14 outputs
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