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Residual tumour detection in post-treatment granulation tissue by using advanced diffusion models in head and neck squamous cell carcinoma patients

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Title: Residual tumour detection in post-treatment granulation tissue by using advanced diffusion models in head and neck squamous cell carcinoma patients
Authors: Fujima, Noriyuki Browse this author →KAKEN DB
Yoshida, Daisuke Browse this author
Sakashita, Tomohiro Browse this author →KAKEN DB
Homma, Akihiro Browse this author →KAKEN DB
Kudo, Kohsuke Browse this author →KAKEN DB
Shirato, Hiroki Browse this author →KAKEN DB
Keywords: Diffusion weighted imaging
Advanced diffusion model
Residual tumour
Post-treatment granulation tissue
Head and neck squamous cell carcinoma
Issue Date: May-2017
Publisher: Elsevier
Journal Title: European journal of radiology
Volume: 90
Start Page: 14
End Page: 19
Publisher DOI: 10.1016/j.ejrad.2017.02.025
PMID: 28583625
Abstract: Purpose: To evaluate the detectability of the residual tumour in post-treatment granulation tissue using parameters obtained with an advanced diffusion model in patients with head and neck squamous cell carcinoma (HNSCC) treated by chemoradiation therapy. Materials and methods: We retrospectively evaluated 23 patients with HNSCC after the full course of chemoradiation therapy. The diffusion-weighted image (DWI) acquisition used single-shot spin-echo echo-planar imaging with 11 b-values (0-1000). We calculated 10 DWI parameters using a mono-exponential model, a bi-exponential model, a stretched exponential model (SEM), a diffusion kurtosis imaging (DKI) model and a statistical diffusion model (SDM) in the region of interest (ROI) placed on the post-treatment granulation tissue. The presence of residual tumour was determined by histological findings or clinical follow-up. Results: Among the 23 patients, seven patients were revealed to have residual tumour. The univariate analysis revealed significant differences in six parameters between the patients with and without residual tumour. From the receiver operating characteristic curve analysis, the highest area under curve was detected in the center of the Gaussian distribution of diffusion coefficient (Ds) obtained by the SDM. The multivariate analysis revealed that the Ds and diffusion heterogeneity (α) obtained by the SEM were predictors for the presence of residual tumour. Conclusion: DWI parameters obtained by advanced fitting models will be one of the diagnostic tools for the detection of residual tumour.
Rights: © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Type: article (author version)
Appears in Collections:北海道大学病院 (Hokkaido University Hospital) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 藤間 憲幸

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