Ratio of Mediastinal Lymph Node SUV to Primary Tumor SUV in 18F-FDG PET/CT for Nodal Staging in Non-Small-Cell Lung Cancer.
Nucl Med Mol Imaging
; 51(2): 140-146, 2017 Jun.
Article
in En
| MEDLINE
| ID: mdl-28559938
PURPOSE: Following determination of the maximum standardized uptake values (SUVmax) of the mediastinal lymph nodes (SUV-LN) and of the primary tumor (SUV-T) on 18F-FDG PET/CT in patients with non-small-cell lung cancer (NSCLC), the aim of the study was to determine the value of the SUV-LN/SUV-T ratio in lymph node staging in comparison with that of SUV-LN. METHODS: We retrospectively reviewed a total of 289 mediastinal lymph node stations from 98 patients with NSCLC who were examined preoperatively for staging and subsequently underwent pathologic studies of the mediastinal lymph nodes. We determined SUV-LN and SUV-R for each lymph node station on 18F-FDG PET/CT and then classified each station into one of three groups based on SUV-T (low, medium and high SUV-T groups). Diagnostic performance was assessed based on receiver operating characteristic (ROC) curve analysis, and the optimal cut-off values that would best discriminate metastatic from benign lymph nodes were determined for each method. RESULTS: The average of SUV-R of malignant lymph nodes was significantly higher than that of benign lymph nodes (0.79 ± 0.45 vs. 0.36 ± 0.23, P < 0.0001). In the ROC curve analysis, the area under the curve (AUC) of SUV-R was significantly higher than that of SUV-LN in the low SUV-T group (0.885 vs. 0.810, P = 0.019). There were no significant differences between the AUCs of SUV-LN and of SUV-R in the medium and high SUV-T groups. The optimal cut-off value for SUV-R in the low SUV-T group was 0.71 (sensitivity 87.5 %, specificity 85.9 %). CONCLUSIONS: The SUV-R performed well in distinguishing between metastatic and benign lymph nodes. In particular, SUV-R was found to have a better diagnostic performance than SUV-LN in the low SUV-T group.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Prognostic_studies
Language:
En
Journal:
Nucl Med Mol Imaging
Year:
2017
Document type:
Article
Country of publication:
Germany