Oncotarget

Research Papers:

Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients

Noriyuki Fujima _, Tomohiro Sakashita, Akihiro Homma, Taisuke Harada, Yukie Shimizu, Khin Khin Tha, Kohsuke Kudo and Hiroki Shirato

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Oncotarget. 2017; 8:33631-33643. https://doi.org/10.18632/oncotarget.16851

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Abstract

Noriyuki Fujima1, Tomohiro Sakashita2, Akihiro Homma2, Taisuke Harada1, Yukie Shimizu1, Khin Khin Tha3,4, Kohsuke Kudo1, Hiroki Shirato3,4

1Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan

2Department of Otolaryngology, Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan

3Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan

4The Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Sapporo, Japan

Correspondence to:

Noriyuki Fujima, email: Noriyuki.Fujima@mb9.seikyou.ne.jp

Keywords: head and neck squamous cell carcinoma, tumor growth rate, magnetic resonance imaging, diffusion weighted imaging, advanced diffusion model

Received: December 22, 2016     Accepted: March 28, 2017     Published: April 05, 2017

ABSTRACT

We assessed parameters of advanced diffusion weighted imaging (DWI) models for the prediction of the tumor growth rate in 55 head and neck squamous cell carcinoma (HNSCC) patients. The DWI acquisition used single-shot spin-echo echo-planar imaging with 12 b-values (0−2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, tri-exponential, stretched exponential and diffusion kurtosis imaging models. We directly measured the tumor growth rate from two sets of different-date imaging data. We divided the patients into a discovery group (n = 40) and validation group (n = 15) based on their MR acquisition dates. In the discovery group, we performed univariate and multivariate regression analyses to establish the multiple regression equation for the prediction of the tumor growth rate using diffusion parameters. The equation obtained with the discovery group was applied to the validation group for the confirmation of the equation’s accuracy. After the univariate and multivariate regression analyses in the discovery-group patients, the estimated tumor growth rate equation was established by using the significant parameters of intermediate diffusion coefficient D2 and slow diffusion coefficient D3 obtained by the tri-exponential model. The discovery group’s correlation coefficient between the estimated and directly measured tumor growth rates was 0.74. In the validation group, the correlation coefficient (r = 0.66) and intra-class correlation coefficient (0.65) between the estimated and directly measured tumor growth rates were respectively good. In conclusion, advanced DWI model parameters can be a predictor for determining HNSCC patients’ tumor growth rate.


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