Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy Journal Article


Authors: Aliotta, E.; Paudyal, R.; Diplas, B.; Han, J.; Hu, Y. C.; Hun Oh, J.; Hatzoglou, V.; Jensen, N.; Zhang, P.; Aristophanous, M.; Riaz, N.; Deasy, J. O.; Lee, N. Y.; Shukla-Dave, A.
Article Title: Multi-modality imaging parameters that predict rapid tumor regression in head and neck radiotherapy
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 SUVmean (Spearman ρ = 0.46, p < 0.001), CT HUmean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, Dmean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUVmean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUVmean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors. 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. © 2024
Keywords: adult; treatment response; aged; primary tumor; major clinical study; intensity modulated radiation therapy; cancer patient; cancer radiotherapy; nuclear magnetic resonance imaging; positron emission tomography; follow up; lymph node metastasis; antineoplastic agent; computer assisted tomography; cohort analysis; tumor regression; validation study; retrospective study; prediction; correlation coefficient; head and neck cancer; quantitative imaging; diffusion weighted imaging; fluorodeoxyglucose; diffusion coefficient; oropharynx carcinoma; chemoradiotherapy; longitudinal study; wart virus; standardized uptake value; gross tumor volume; clinical trial (topic); multimodal imaging; fdg pet; diffusion weighted mri; human; male; female; article; multimodality imaging; network analysis; positron emission tomography-computed tomography; intravoxel incoherent motion imaging; t2 weighted imaging; hpv+; treatment response monitoring
Journal Title: Physics and Imaging in Radiation Oncology
Volume: 31
ISSN: 2405-6316
Publisher: Elsevier B.V.  
Date Published: 2024-07-01
Start Page: 100603
Language: English
DOI: 10.1016/j.phro.2024.100603
PROVIDER: scopus
PMCID: PMC11261256
PUBMED: 39040433
DOI/URL:
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: Eric Aliotta -- Source: Scopus
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MSK Authors
  1. Nadeem Riaz
    417 Riaz
  2. Nancy Y. Lee
    876 Lee
  3. Amita Dave
    138 Dave
  4. Jung Hun Oh
    187 Oh
  5. Joseph Owen Deasy
    524 Deasy
  6. Yu-Chi Hu
    118 Hu
  7. James Edward Han
    17 Han
  8. Ramesh Paudyal
    39 Paudyal
  9. Peng Zhang
    11 Zhang
  10. Bill Diplas
    17 Diplas
  11. Eric Aliotta
    16 Aliotta
  12. Naomi Jensen
    1 Jensen