Feature level quantitative ultrasound and CT information fusion to predict the outcome of head & neck cancer radiotherapy treatment: Enhanced principal component analysis
The results demonstrated that the proposed predictive model is able to predict a binary H&N cancer treatment outcome, feature level fusion of CT and QUS radiomics has superiority over single imaging modality and EPCA is an effective approach to fuse the features.
Authors
- Aryan Safakish
- Lakshmanan Sannachi (ORCID: https://orcid.org/0000-0002-9775-0224)
- David Alberico
- Amir Moslemi (ORCID: https://orcid.org/0000-0003-0849-1943)
- Gregory J. Czarnota (ORCID: https://orcid.org/0000-0002-9973-4586)
Institutions
- Sunnybrook Health Science Centre (CA)
- University of Toronto (CA)
- Health Sciences Centre (CA)
- Sunnybrook Research Institute
- Toronto Metropolitan University (CA)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1002/mp.18078
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Terry Fox Research Institute
- Natural Sciences and Engineering Research Council of Canada