Dataset Information

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 SUV_mean (Spearman ρ = 0.46, p < 0.001), CT HU_mean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, D_mean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUV_mean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUV_mean 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.

SUBMITTER: Aliotta E

PROVIDER: S-EPMC11261256 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Aliotta Eric E Paudyal Ramesh R Diplas Bill B Han James J Hu Yu-Chi YC Hun Oh Jung J Hatzoglou Vaios V Jensen Naomi N Zhang Peng P Aristophanous Michalis M Riaz Nadeem N Deasy Joseph O JO Lee Nancy Y NY Shukla-Dave Amita A

Physics and imaging in radiation oncology 20240624

<h4>Background and purpose</h4>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.<h4>Materials and methods</h4>Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-M ...[more]

PMID: 39040433

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Project description:PurposeTumor hypoxia and other microenvironmental factors are key determinants of treatment resistance. Hypoxia positron emission tomography (PET) and functional magnetic resonance imaging (MRI) are established prognostic imaging modalities to identify radiation resistance in head-and-neck cancer (HNC). The aim of this preclinical study was to develop a multi-parametric imaging parameter specifically for focal radiotherapy (RT) dose escalation using HNC xenografts of different radiation sensitivities.MethodsA total of eight human HNC xenograft models were implanted into 68 immunodeficient mice. Combined PET/MRI using dynamic [18F]-fluoromisonidazole (FMISO) hypoxia PET, diffusion-weighted (DW), and dynamic contrast-enhanced MRI was carried out before and after fractionated RT (10 × 2 Gy). Imaging data were analyzed on voxel-basis using principal component (PC) analysis for dynamic data and apparent diffusion coefficients (ADCs) for DW-MRI. A data- and hypothesis-driven machine learning model was trained to identify clusters of high-risk subvolumes (HRSs) from multi-dimensional (1-5D) pre-clinical imaging data before and after RT. The stratification potential of each 1D to 5D model with respect to radiation sensitivity was evaluated using Cohen's d-score and compared to classical features such as mean/peak/maximum standardized uptake values (SUVmean/peak/max) and tumor-to-muscle-ratios (TMRpeak/max) as well as minimum/valley/maximum/mean ADC.ResultsComplete 5D imaging data were available for 42 animals. The final preclinical model for HRS identification at baseline yielding the highest stratification potential was defined in 3D imaging space based on ADC and two FMISO PCs ([Formula: see text]). In 1D imaging space, only clusters of ADC revealed significant stratification potential ([Formula: see text]). Among all classical features, only ADCvalley showed significant correlation to radiation resistance ([Formula: see text]). After 2 weeks of RT, FMISO_c1 showed significant correlation to radiation resistance ([Formula: see text]).ConclusionA quantitative imaging metric was described in a preclinical study indicating that radiation-resistant subvolumes in HNC may be detected by clusters of ADC and FMISO using combined PET/MRI which are potential targets for future functional image-guided RT dose-painting approaches and require clinical validation.

Dataset Information

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

Background and purpose

Materials and methods

Results

Conclusions

Publications

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

Similar Datasets

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