Hendra Budiawan1, Gi Jeong Cheon2, Hyung-Jun Im3, Soo Jin Lee3, Jin Chul Paeng3, Keon Wook Kang4, June-Key Chung3, Dong Soo Lee3. 1. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Department of Nuclear Medicine, Mochtar Riady Comprehensive Cancer Centre, Siloam Hospitals Semanggi, Jakarta, Indonesia. 2. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Cancer Research Institute, Seoul National University, Seoul, Korea ; Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehangro, Jongro-gu, Seoul 110-744 Korea. 3. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea. 4. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Cancer Research Institute, Seoul National University, Seoul, Korea.
Abstract
PURPOSE: Lymph node (LN) characterization is crucial in determining the stage and treatment decisions in patient with lung cancer. Although (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) has a higher diagnostic accuracy in LN characterization than anatomical imaging, differentiating between metastatic and inflammatory LNs is still challenging because both could show high (18)F-FDG uptake. The purpose of this study was to assess if the heterogeneity of the (18)F-FDG uptake could help in differentiating between inflammatory and metastatic LNs in lung cancer, and to compare with other parameters. METHODS: A total of 44 patients with adenocarcinoma of the lung, who underwent preoperative (18)F-FDG PET/CT without having any previous treatments and were revealed to have (18)F-FDG-avid LNs, were enrolled. There were 52 pathology-proven metastatic lymph nodes in 26 subjects. The pathology-proven metastatic LNs were compared with 42 pathology-proven inflammatory/benign LNs in 18 subjects. The coefficient of variation (CV) was used to assess the heterogeneity of (18)F-FDG uptake by dividing the standard deviation of standardized uptake value (SUV) by mean SUV. The volume of interest was manually drawn based on the combined CT images of (18)F-FDG PET/CT (no threshold is used). Comparisons were made with the maximum standardized uptake values (SUVmax), visual assessment of (18)F-FDG uptake, longest diameter, and maximum Hounsfield units (HUmax). RESULTS: Metastatic lymph nodes tended to have higher CVs than the inflammatory LNs. The mean CV of metastatic LNs (0.30 ± 0.08; range: 0.08-0.55) was higher than that of inflammatory LNs (0.17 + 0.06; range, 0.07-0.32; P < 0.0001). On receiver operating characteristic (ROC) curve analysis, the area under curve was 0.901, and using 0.20 as cut-off value, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 88.5 %, 76.2 %, 82.2 %, 84.3, and 83.0 % respectively. Accuracy of CV was slightly higher than SUVmax and diameter, but significantly higher than visual assessment and HUmax. CONCLUSIONS: In patients with adenocarcinoma of the lung having no prior treatments, metastatic LNs showed more heterogeneous (18)F-FDG uptake than inflammatory LNs. Measuring the CV of the SUV derived from a manual volume of interest (VOI) can be helpful in determining metastatic LN of adenocarcinoma of the lung. Including diagnostic criteria of CV into the diagnostic approach can increase the accuracy of mediastinal node status.
PURPOSE: Lymph node (LN) characterization is crucial in determining the stage and treatment decisions in patient with lung cancer. Although (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) has a higher diagnostic accuracy in LN characterization than anatomical imaging, differentiating between metastatic and inflammatory LNs is still challenging because both could show high (18)F-FDG uptake. The purpose of this study was to assess if the heterogeneity of the (18)F-FDG uptake could help in differentiating between inflammatory and metastatic LNs in lung cancer, and to compare with other parameters. METHODS: A total of 44 patients with adenocarcinoma of the lung, who underwent preoperative (18)F-FDG PET/CT without having any previous treatments and were revealed to have (18)F-FDG-avid LNs, were enrolled. There were 52 pathology-proven metastatic lymph nodes in 26 subjects. The pathology-proven metastatic LNs were compared with 42 pathology-proven inflammatory/benign LNs in 18 subjects. The coefficient of variation (CV) was used to assess the heterogeneity of (18)F-FDG uptake by dividing the standard deviation of standardized uptake value (SUV) by mean SUV. The volume of interest was manually drawn based on the combined CT images of (18)F-FDG PET/CT (no threshold is used). Comparisons were made with the maximum standardized uptake values (SUVmax), visual assessment of (18)F-FDG uptake, longest diameter, and maximum Hounsfield units (HUmax). RESULTS: Metastatic lymph nodes tended to have higher CVs than the inflammatory LNs. The mean CV of metastatic LNs (0.30 ± 0.08; range: 0.08-0.55) was higher than that of inflammatory LNs (0.17 + 0.06; range, 0.07-0.32; P < 0.0001). On receiver operating characteristic (ROC) curve analysis, the area under curve was 0.901, and using 0.20 as cut-off value, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 88.5 %, 76.2 %, 82.2 %, 84.3, and 83.0 % respectively. Accuracy of CV was slightly higher than SUVmax and diameter, but significantly higher than visual assessment and HUmax. CONCLUSIONS: In patients with adenocarcinoma of the lung having no prior treatments, metastatic LNs showed more heterogeneous (18)F-FDG uptake than inflammatory LNs. Measuring the CV of the SUV derived from a manual volume of interest (VOI) can be helpful in determining metastatic LN of adenocarcinoma of the lung. Including diagnostic criteria of CV into the diagnostic approach can increase the accuracy of mediastinal node status.
Entities:
Keywords:
18F-FDG uptake; Adenocarcinoma of the lung; Heterogeneity; Lymph node metastasis; Positron emission tomography (PET)
Authors: J F Vansteenkiste; S G Stroobants; P R De Leyn; P J Dupont; J Bogaert; A Maes; G J Deneffe; K L Nackaerts; J A Verschakelen; T E Lerut; L A Mortelmans; M G Demedts Journal: J Clin Oncol Date: 1998-06 Impact factor: 44.544
Authors: Xuan Canh Nguyen; Young So; Jin-Haeng Chung; Won Woo Lee; So Yeon Park; Sang Eun Kim Journal: Eur J Cancer Date: 2008-03-07 Impact factor: 9.162