OBJECTIVE: The purpose of the study was to assess metabolic tumor volume and total glycolytic activity of the primary tumor as prognostic parameters for outcome in patients with non-small cell lung carcinoma (NSCLC). MATERIALS AND METHODS: Thirty-nine patients who had undergone a baseline staging PET/CT examination at our institution for the diagnosis of NSCLC were retrospectively identified. The maximum standardized uptake value (SUV(max)), metabolic tumor volume, and total glycolytic activity were segmented from PET using the gradient method; 12-month survival and overall survival at the end of follow-up were used as outcome measures. Multivariate logistic regression, receiver operating characteristic curve analysis, and Kaplan-Meier curves for survival analysis were generated and compared using the Mantel-Cox log-rank test. RESULTS: The mean gradient-based metabolic tumor volume and gradient-based total glycolytic activity were significantly greater in the patients who died (93.3 mL and 597.5 g) than in those who survived (19.3 mL and 193.9 g, respectively) (p < 0.003 and p < 0.031). There was no statistically significant difference in the mean SUV(max) between the patients who survived (12.7) at 12 months and those who had died (13.1) (p = 0.85). On multivariate analysis, gradient-based metabolic tumor volume was the only variable associated with 12-month mortality when adjusted for all other factors.(.) The area under the curve (AUC) for gradient-based metabolic tumor volume was 0.77 (p < 0.006). A significant difference in the time to survival was observed between high and low gradient-based metabolic tumor volume (log-rank p < 0.05) cohorts using the median gradient-based metabolic tumor volume (9.7 mL) as the cut point. CONCLUSION: PET-based volumetric imaging parameters are potential prognostic markers of outcome in patients with NSCLC.
OBJECTIVE: The purpose of the study was to assess metabolic tumor volume and total glycolytic activity of the primary tumor as prognostic parameters for outcome in patients with non-small cell lung carcinoma (NSCLC). MATERIALS AND METHODS: Thirty-nine patients who had undergone a baseline staging PET/CT examination at our institution for the diagnosis of NSCLC were retrospectively identified. The maximum standardized uptake value (SUV(max)), metabolic tumor volume, and total glycolytic activity were segmented from PET using the gradient method; 12-month survival and overall survival at the end of follow-up were used as outcome measures. Multivariate logistic regression, receiver operating characteristic curve analysis, and Kaplan-Meier curves for survival analysis were generated and compared using the Mantel-Cox log-rank test. RESULTS: The mean gradient-based metabolic tumor volume and gradient-based total glycolytic activity were significantly greater in the patients who died (93.3 mL and 597.5 g) than in those who survived (19.3 mL and 193.9 g, respectively) (p < 0.003 and p < 0.031). There was no statistically significant difference in the mean SUV(max) between the patients who survived (12.7) at 12 months and those who had died (13.1) (p = 0.85). On multivariate analysis, gradient-based metabolic tumor volume was the only variable associated with 12-month mortality when adjusted for all other factors.(.) The area under the curve (AUC) for gradient-based metabolic tumor volume was 0.77 (p < 0.006). A significant difference in the time to survival was observed between high and low gradient-based metabolic tumor volume (log-rank p < 0.05) cohorts using the median gradient-based metabolic tumor volume (9.7 mL) as the cut point. CONCLUSION: PET-based volumetric imaging parameters are potential prognostic markers of outcome in patients with NSCLC.
Authors: Hao Zhang; Kristen Wroblewski; Yulei Jiang; Bill C Penney; Daniel Appelbaum; Cassie A Simon; Ravi Salgia; Yonglin Pu Journal: Lung Cancer Date: 2015-04-09 Impact factor: 5.705
Authors: Abdel K Tahari; Vasavi Paidpally; Alin Chirindel; Richard L Wahl; Rathan M Subramaniam Journal: AJR Am J Roentgenol Date: 2015-02 Impact factor: 3.959
Authors: Satoshi Takeuchi; Eric M Rohren; Reham Abdel-Wahab; Lianchun Xiao; Jeffrey S Morris; Homer A Macapinlac; Manal M Hassan; Ahmed O Kaseb Journal: Eur J Nucl Med Mol Imaging Date: 2016-12-12 Impact factor: 9.236
Authors: Vasavi Paidpally; Alin Chirindel; Christine H Chung; Jeremy Richmon; Wayne Koch; Harry Quon; Rathan M Subramaniam Journal: AJR Am J Roentgenol Date: 2014-08 Impact factor: 3.959