Deciphering Tumor Response: The Role of Fluoro-18-d-Glucose Uptake in Evaluating Targeted Therapies with Tyrosine Kinase Inhibitors.
Kalevi KairemoMohamed GoudaHubert H ChuangHomer A MacapinlacVivek SubbiahPublished in: Journal of clinical medicine (2024)
Background/Objectives : The inhibitory effects of tyrosine kinase inhibitors (TKIs) on glucose uptake through their binding to human glucose transporter-1 (GLUT-1) have been well documented. Thus, our research aimed to explore the potential impact of various TKIs of GLUT-1 on the standard [ 18 F]FDG-PET monitoring of tumor response in patients. Methods : To achieve this, we conducted an analysis on three patients who were undergoing treatment with different TKIs and harbored actionable alterations. Alongside the assessment of FDG data (including SUVmax, total lesion glycolysis (TLG), and metabolic tumor volume (MTV)), we also examined the changes in tumor sizes through follow-up [ 18 F]FDG-PET/CT imaging. Notably, our patients harbored alterations in BRAFV600, RET, and c-KIT and exhibited positive responses to the targeted treatment. Results : Our analysis revealed that FDG data derived from SUVmax, TLG, and MTV offered quantifiable outcomes that were consistent with the measurements of tumor size. Conclusions : These findings lend support to the notion that the inhibition of GLUT-1, as a consequence of treatment efficacy, could be indirectly gauged through [ 18 F] FDG-PET/CT imaging in cancer patients undergoing TKI therapy.
Keyphrases
- end stage renal disease
- positron emission tomography
- chronic kidney disease
- pet ct
- patients undergoing
- ejection fraction
- high resolution
- newly diagnosed
- pet imaging
- prognostic factors
- blood glucose
- stem cells
- artificial intelligence
- type diabetes
- endothelial cells
- replacement therapy
- patient reported outcomes
- drug delivery
- insulin resistance
- cancer therapy
- single cell
- big data
- adipose tissue
- machine learning
- risk assessment
- advanced non small cell lung cancer
- climate change
- deep learning
- patient reported