dc.contributor.author |
Chan, Tsz Him |
|
dc.contributor.author |
Haworth, Annette |
|
dc.contributor.author |
Wang, Alan |
|
dc.contributor.author |
Osanlouy, Mahyar |
|
dc.contributor.author |
Williams, Scott |
|
dc.contributor.author |
Mitchell, Catherine |
|
dc.contributor.author |
Hofman, Michael S |
|
dc.contributor.author |
Hicks, Rodney J |
|
dc.contributor.author |
Murphy, Declan G |
|
dc.contributor.author |
Reynolds, Hayley M |
|
dc.coverage.spatial |
Germany |
|
dc.date.accessioned |
2023-10-06T00:38:05Z |
|
dc.date.available |
2023-10-06T00:38:05Z |
|
dc.date.issued |
2023-04 |
|
dc.identifier.citation |
(2023). EJNMMI Research, 13(1), 34-. |
|
dc.identifier.issn |
2191-219X |
|
dc.identifier.uri |
https://hdl.handle.net/2292/66173 |
|
dc.description.abstract |
Background Prostate-Specifc Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are wellestablished 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 Difusion Coefcient (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. Classifcation
models were built using radiomic and clinical features to predict IPLs at a voxel level and then classifed 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 Classifer using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specifcity 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 classifers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and diferentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning. |
|
dc.format.medium |
Electronic |
|
dc.language |
eng |
|
dc.publisher |
Springer Nature |
|
dc.relation.ispartofseries |
EJNMMI research |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
Multiparametric MRI |
|
dc.subject |
PSMA PET/CT |
|
dc.subject |
Prostate cancer |
|
dc.subject |
Radiation therapy |
|
dc.subject |
Radiomics |
|
dc.subject |
32 Biomedical and Clinical Sciences |
|
dc.subject |
3202 Clinical Sciences |
|
dc.subject |
3211 Oncology and Carcinogenesis |
|
dc.subject |
Cancer |
|
dc.subject |
Urologic Diseases |
|
dc.subject |
Bioengineering |
|
dc.subject |
Aging |
|
dc.subject |
Biomedical Imaging |
|
dc.subject |
4 Detection, screening and diagnosis |
|
dc.subject |
4.4 Population screening |
|
dc.subject |
4.2 Evaluation of markers and technologies |
|
dc.subject |
3 Good Health and Well Being |
|
dc.subject |
Science & Technology |
|
dc.subject |
Life Sciences & Biomedicine |
|
dc.subject |
Radiology, Nuclear Medicine & Medical Imaging |
|
dc.subject |
PSMA PET |
|
dc.subject |
CT |
|
dc.subject |
POSITRON-EMISSION-TOMOGRAPHY |
|
dc.subject |
DIAGNOSIS |
|
dc.subject |
SYSTEM |
|
dc.subject |
1101 Medical Biochemistry and Metabolomics |
|
dc.subject |
1103 Clinical Sciences |
|
dc.subject |
1112 Oncology and Carcinogenesis |
|
dc.title |
Detecting localised prostate cancer using radiomic features in PSMA PET and multiparametric MRI for biologically targeted radiation therapy |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1186/s13550-023-00984-5 |
|
pubs.issue |
1 |
|
pubs.begin-page |
34 |
|
pubs.volume |
13 |
|
dc.date.updated |
2023-09-04T03:50:26Z |
|
dc.rights.holder |
Copyright: The authors |
en |
dc.identifier.pmid |
37099047 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/37099047 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
research-article |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
959002 |
|
pubs.org-id |
Bioengineering Institute |
|
dc.identifier.eissn |
2191-219X |
|
dc.identifier.pii |
10.1186/s13550-023-00984-5 |
|
pubs.number |
34 |
|
pubs.record-created-at-source-date |
2023-09-04 |
|
pubs.online-publication-date |
2023-04-26 |
|