Abstract 130P
Background
Recent discoveries show the skeleton’s endocrine role in regulating whole-body glucose homeostasis. This study investigated individual bone glucose metabolism and its integration as a network in lung cancer patients pre- and post-treatment. It evaluated associations of bone glucose metabolism with lung cancer progression and outcomes.
Methods
Data was acquired from ACRIN 6668 study on non-small cell lung cancer patients from Cancer Imaging Archive. 34/242 stage IIIB patients were selected. Dynamic whole-body 18F-FDG PET/CT scans pre- and post-treatment were analysed to measure bone glucose metabolism. PMOD was used for image analysis and processing. Bone volumes of interest (VOI) were segmented in CT images using Hounsfield Unit (HU) > 300. Average HU were extracted for VOIs. Corrected PET images were used to extract average standard uptake values (SUV). Network analysis was performed.
Results
In post-treatment patients, lung SUV peak was decreased significantly compared to pre-treatment patients. SUV and HU differed significantly between individual bones within each group. Comparing groups, significant differences were observed in sternum (lower SUV) post-treatment (Two-way ANOVA, p<0.05). PET and CT pre-treatment networks showed three distinct clusters (p=0.0003). Post-treatment networks had three less distinct clusters (p=0.77). There were significant differences in life expectancy between clusters in the PET pre-treatment network with life expectancy higher in clusters two and three compared to cluster one. This effect was not seen in PET post-treatment clusters.
Conclusions
This study showed that lung SUV peak significantly decreased after chemotherapy showing that treatment reduced glucose uptake at the primary tumour site. Network analysis indicated a shift in bone metabolic profiles of patients, with treatment having a homogenising effect on PET networks. The differences in life expectancy between the pre-treatment clusters suggests that specific initial bone metabolic profiles can be correlated with survival time. This novel technique of analysing glucose metabolism using network analysis could be used as a clinical tool to predict prognosis and guide management in lung cancer patients.
Clinical trial identification
NCT00083083.
Legal entity responsible for the study
The authors.
Funding
RR and TC research project was supported by the Biomedical Teaching Organisation (BMTO) of the Edinburgh Medical School and Biomedical Sciences School. CW was supported by a Wellcome Trust Technology Development Award (221295/Z/20/Z). KS was supported by Scotland’s Rural College (SRUC). AAST was funded by the British Heart Foundation (FS/19/34/34354) and is a recipient of a Welcome Trust Technology Development Award (221295/Z/20/Z). This paper has been made possible in part by a Chan Zuckerberg Initiative DAF grant number 2020-225273, an advised fund of Silicon Valley Community Foundation (https://doi.org/10.37921/690910twdfoo).
Disclosure
All authors have declared no conflicts of interest.