Predictive value of positron emission tomography for the prognosis of molecularly targeted therapy in solid tumors
Xianhe Xie1, Huijuan Chen1, Haitao Yang1
1Department of Chemotherapy, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China, xiexianhe@yahoo.com.
Objective:
This study aimed at comprehensively exploring the value applying positron emission tomography (PET) to predict the effect of molecularly targeted therapy in solid tumors.
Materials And Methods:
A systematic search was performed for potentially relevant studies from the time of inception to February 2017. The primary endpoints were progression-free survival (PFS), overall survival (OS), and time to progression (TTP). The results were analyzed by Review Manager version 5.3 (RevMan 5.3) statistical software. Subgroup analyses were implemented based on the type of molecularly targeted agents (monoclonal antibodies arm and small molecular targeted agents arm), mechanism (erlotinib/gefitinib arm and bevacizumab arm), radioactive tracers, type of tumor, and reevaluated PET timing.
Results:
Twenty-six studies incorporating 865 individuals were eligible. Compared with PET nonresponse group, PET response group displayed a decrease in maximal standard uptake value (SUVmax), which was associated with a significantly prolonged PFS (HR =0.41, 95% CI [0.29, 0.59]; P<0.00001), OS (HR =0.52, 95% CI [0.40, 0.67]; P<0.00001), and TTP (HR =0.30, 95% CI [0.14, 0.66]; P=0.003). Similar results were obtained in the subgroup analyses of PFS in erlotinib/gefitinib arm and small molecular targeted agents arm; and OS in lung cancer arm, erlotinib/gefitinib arm, bevacizumab arm, small molecular targeted agents arm, monoclonal antibodies arm, 18F-fluorodeoxythymidine (18F-FLT) arm, 18F-fluorodeoxyglucose (18F-FDG) arm, and early PET timing arm.
Conclusion:
Our study demonstrated that PET was a favorable approach to predict the prognosis of molecularly targeted therapy for solid tumors. PET assessment within 2 weeks could be useful to predict clinical outcome.
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