Erschienen in:
01.06.2015 | Head and Neck
Intravoxel water diffusion heterogeneity MR imaging of nasopharyngeal carcinoma using stretched exponential diffusion model
verfasst von:
Vincent Lai, Victor Ho Fun Lee, Ka On Lam, Henry Chun Kin Sze, Queenie Chan, Pek Lan Khong
Erschienen in:
European Radiology
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Ausgabe 6/2015
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Abstract
Purpose
To determine the utility of stretched exponential diffusion model in characterisation of the water diffusion heterogeneity in different tumour stages of nasopharyngeal carcinoma (NPC).
Materials and methods
Fifty patients with newly diagnosed NPC were prospectively recruited. Diffusion-weighted MR imaging was performed using five b values (0–2,500 s/mm2). Respective stretched exponential parameters (DDC, distributed diffusion coefficient; and alpha (α), water heterogeneity) were calculated. Patients were stratified into low and high tumour stage groups based on the American Joint Committee on Cancer (AJCC) staging for determination of the predictive powers of DDC and α using t test and ROC curve analyses.
Results
The mean ± standard deviation values were DDC = 0.692 ± 0.199 (×10−3 mm2/s) for low stage group vs 0.794 ± 0.253 (×10−3 mm2/s) for high stage group; α = 0.792 ± 0.145 for low stage group vs 0.698 ± 0.155 for high stage group. α was significantly lower in the high stage group while DDC was negatively correlated. DDC and α were both reliable independent predictors (p < 0.001), with α being more powerful. Optimal cut-off values were (sensitivity, specificity, positive likelihood ratio, negative likelihood ratio) DDC = 0.692 × 10−3 mm2/s (94.4 %, 64.3 %, 2.64, 0.09), α = 0.720 (72.2 %, 100 %, −, 0.28).
Conclusion
The heterogeneity index α is robust and can potentially help in staging and grading prediction in NPC.
Key Points
• Stretched exponential diffusion models can help in tissue characterisation in nasopharyngeal carcinoma
• α and distributed diffusion coefficient (DDC) are negatively correlated
• α is a robust heterogeneity index marker
• α can potentially help in staging and grading prediction