Assessment of metastatic lymph nodes in head and neck squamous cell carcinomas using simultaneous 18F-FDG-PET and MRI
Autor: | Mari Hagiwara, Artem Mikheev, Henry Rusinek, Brian L. Schmidt, Cheng Liu, Qi Chen, Jean Logan, Jenny Chen, Sungheon Kim, Babak Givi |
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Rok vydání: | 2020 |
Předmět: |
Adult
Male medicine.medical_specialty Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Text mining Image processing Fluorodeoxyglucose F18 Humans Medicine Head and neck cancer Lymph node Aged Aged 80 and over Multidisciplinary Clinical pathology medicine.diagnostic_test Squamous Cell Carcinoma of Head and Neck business.industry Diagnostic markers Magnetic resonance imaging Middle Aged Prognosis medicine.disease Magnetic Resonance Imaging Head and neck squamous-cell carcinoma medicine.anatomical_structure Head and Neck Neoplasms Positron emission tomography Lymphatic Metastasis Positron-Emission Tomography 030220 oncology & carcinogenesis Female Lymph Nodes Lymph Radiopharmaceuticals business Nuclear medicine Follow-Up Studies Diffusion MRI |
Zdroj: | Scientific Reports |
ISSN: | 2045-2322 |
Popis: | 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 = e 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 −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. |
Databáze: | OpenAIRE |
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