ArticlePhysics and imaging in radiation oncology2024
Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy.
Article in Physics and imaging in radiation oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Longitudinal assessment of diffusion-weighted imaging during magnetic resonance-guided radiotherapy in head and neck cancer.Radiation oncology (London, England) · 2025Trial
- Radiosensitivity Prediction of Tumor Patient Based on Deep Fusion of Pathological Images and Genomics.Bioengineering (Basel, Switzerland) · 2026Article
- Human Papillomavirus-Positive Oropharyngeal Cancer: Clinical and Radiologic Characteristics.Journal of the Korean Society of Radiology · 2026Review
- Predicting and monitoring response to head and neck cancer radiotherapy using multimodality imaging and radiobiological digital twin simulations.Physics in medicine and biology · 2025Article
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Authors and funding
14 authors.
Funding
Abstract
Background and purpose: Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma. Materials and methods: Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT. Results: Significant correlations with volume loss were observed for baseline FDG SUV Conclusions: Multiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis.
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