The Predictive Value of Early In-Treatment18F-FDG PET/CT Response to Chemotherapy in Combination with Bevacizumab in Advanced Nonsquamous Non-Small Cell Lung Cancer.
van der Drift, MA
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18 F-FDG PET/CT is potentially applicable to predict response to chemotherapy in combination with bevacizumab in patients with advanced non-small cell lung cancer (NSCLC).Methods:In 25 patients with advanced nonsquamous NSCLC,18F-FDG PET/CT was performed before treatment and after 2 wk, at the end of the second week of first cycle carboplatin-paclitaxel and bevacizumab (CPB) treatment. Patients received up to a total of 4 cycles of CPB treatment. Maintenance treatment with bevacizumab monotherapy was continued until progressive disease without significant treatment-related toxicities of first-line treatment. In the case of progressive disease, bevacizumab was combined with erlotinib. SUV corrected for lean body mass (SUL and SULpeak) were obtained. PERCIST were used for response evaluation. These semiquantitative parameters were correlated with progression-free survival and overall survival (OS).Results:Metabolic response, defined by a significant reduction in SULpeakof 30% or more after 2 wk of CPB, was predictive of progression-free survival and OS. For partial metabolic responders (n= 19), the median OS was 22.8 mo. One-year and 2-y OS were 79% and 47%, respectively. Nonmetabolic responders (n= 6) (stable metabolic disease or progressive disease) showed a median OS of 4.4 mo (1-y and 2-y OS was 33% and 0%, respectively) (P <0.001).Conclusion:18F-FDG PET/CT after 1 treatment cycle is predictive of outcome to first-line chemotherapy with bevacizumab in patients with advanced nonsquamous NSCLC. This enables identification of patients at risk of treatment failure, permitting treatment alternatives such as early switch to a different therapy.
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advanced non-small cell lung cancer
early response prediction
Aged, 80 and over
Carcinoma, Non-Small-Cell Lung
Positron Emission Tomography Computed Tomography
Predictive Value of Tests
Translational Molecular Imaging
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J Nucl Med, 2017, 58 (8), pp. 1243 - 1248