Gradient-Based Radiomics for Outcome Prediction and Decision-Making in PULSAR: A Preliminary Study
Our gradient-based radiomics approach, combining spatial segmentation and temporal features, significantly enhances treatment response prediction in PULSAR therapy. Its superior performance compared to conventional radiomics, coupled with its effectiveness in both PULSAR and non-PULSAR cohorts, highlights its potential as a robust tool for personalized treatment planning in neuro-oncology, applicable to both photon and particle therapies.
Authors
- Michael Dohopolski (ORCID: https://orcid.org/0000-0002-9043-1490)
- Haozhao Zhang (ORCID: https://orcid.org/0000-0002-0666-5404)
- Zabi Wardak (ORCID: https://orcid.org/0000-0001-9007-8415)
- Jiaqi Liu
- Hao Peng (ORCID: https://orcid.org/0000-0001-7018-8617)
- Robert Timmerman
Institutions
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- International Journal of Particle Therapy
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1016/j.ijpt.2025.100739
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08