Assessment of metastatic lymph nodes in head and neck squamous cell carcinomas using simultaneous 18F-FDG-PET and MRI

Jenny Chen, Mari Hagiwara, Babak Givi, Brian Schmidt, Cheng Liu, Qi Chen, Jean Logan, Artem Mikheev, Henry Rusinek, Sungheon Gene Kim

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, we investigate the feasibility of using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), diffusion weighted imaging (DWI), and dynamic positron emission tomography (PET) for detection of metastatic lymph nodes in head and neck squamous cell carcinoma (HNSCC) cases. Twenty HNSCC patients scheduled for lymph node dissection underwent DCE-MRI, dynamic PET, and DWI using a PET-MR scanner within one week prior to their planned surgery. During surgery, resected nodes were labeled to identify their nodal levels and sent for routine clinical pathology evaluation. Quantitative parameters of metastatic and normal nodes were calculated from DCE-MRI (ve, vp, PS, Fp, Ktrans), DWI (ADC) and PET (Ki, K1, k2, k3) to assess if an individual or a combination of parameters can classify normal and metastatic lymph nodes accurately. There were 38 normal and 11 metastatic nodes covered by all three imaging methods and confirmed by pathology. 34% of all normal nodes had volumes greater than or equal to the smallest metastatic node while 4 normal nodes had SUV > 4.5. Among the MRI parameters, the median vp, Fp, PS, and Ktrans values of the metastatic lymph nodes were significantly lower (p = <0.05) than those of normal nodes. ve and ADC did not show any statistical significance. For the dynamic PET parameters, the metastatic nodes had significantly higher k3 (p value = 8.8 × 10−8) and Ki (p value = 5.3 × 10−8) than normal nodes. K1 and k2 did not show any statistically significant difference. Ki had the best separation with accuracy = 0.96 (sensitivity = 1, specificity = 0.95) using a cutoff of Ki = 5.3 × 10−3 mL/cm3/min, while k3 and volume had accuracy of 0.94 (sensitivity = 0.82, specificity = 0.97) and 0.90 (sensitivity = 0.64, specificity = 0.97) respectively. 100% accuracy can be achieved using a multivariate logistic regression model of MRI parameters after thresholding the data with Ki < 5.3 × 10−3 mL/cm3/min. The results of this preliminary study suggest that quantitative MRI may provide additional value in distinguishing metastatic nodes, particularly among small nodes, when used together with FDG-PET.

Original languageEnglish (US)
Article number20764
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 2020

ASJC Scopus subject areas

  • General

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