Karla J. Suchacki 1*, Rucha Ronghe1, David Dye2, Catriona Wimberley2,3, Simon Cherry4, Ramsey Badawi4, Lorenzo Nardo4, Elizabeth Li4, Benjamin Spencer4, Adriana A. S. Tavares1,2 1University/British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, UK; 2Edinburgh Imaging, University of Edinburgh, UK; 3Centre for Clinical Brain Sciences, University of Edinburgh, UK; 4EXPLORER Molecular Imaging Center, UC Davis, USA Correspondence: Karla J. Suchacki (karla.suchacki@ed.ac.uk)

EJNMMI Physics 2023, 10(1):P6

Background

Total-body dynamic positron emission tomography computerised tomography (PET/CT) lends itself to deciphering complex biological processes and interactions. Recently, we found that different bones within the murine skeleton have a unique glucose metabolism and form a complex metabolic network [1]. Thus we hypothesised that (1) individual bones have different glucose metabolism and form complex skeletal metabolism networks in humans. We also wanted to investigate how (2) changes in multi-bone glucose metabolism in humans associate with cancer disease development and progression.

Materials and methods

(1) 13 (4 male, 9 female) volunteers (age 49.7 ± 13.4y, BMI 29.1 ± 5.9 kg/m2) underwent dynamic total-body [18F]FDG-PET/CT scanning (uEXPLORER (United Imaging Healthcare, Shanghai, China; ethics:IRB). (2) 27 (19 male, 8 female) stage IIB lung cancer patients (age 59.6 ± 9.2y, BMI 24.9 ± 4.4 kg/m2) underwent conventional static [18F]FDG-PET/CT scanning (ethics:ACRIN6668) [2,3,4]. Reconstructed PET/CT images were quantified using PMOD 3.117 (PMOD Technologies, Switzerland). Bone volumes of interest were segmented as previously described [1]. (1) Time-activity curves (TACs) were generated and SUVs were calculated for each time point (0–60 min), the average SUV for the last 3 frames were generated. (2) Skeletal energy networks were generated using Graphia (Kajeka, UK)[1]. SUV at equilibrium was extracted for static PET images and CT Hounsfield Units were extracted for each bone (1&2).

Results

Skeletal [18F]FDG uptake was equal or higher in humans compared to mice, with higher uptake in bones of the axial skeleton (Fig. 1). [18F]FDG murine networks translated to healthy humans. Skeletal [18F]FDG uptake was sex-dependent in healthy volunteers (Fig. 2), however this sex effect was not observed in cancer patients. PET network analysis clustered lung cancer patients into two clusters translating to a two-tiered survival plot (Fig. 3).

Conclusions

Network analysis could be used to identify new physiological and pathological tissue interactions beyond individual bones metabolism and to stratify patients with ability to predict patient survival.

Fig. 1

[18F]FDG uptake in healthy human subjects versus healthy mice. Data are mean ± SEM * **** (p < 0.0001).

Fig. 2

[18F]FDG SUV in healthy human males versus healthy human females. Data are mean ± SEM * (p < 0.05).

Fig. 3

Skeletal energy networks identified with positron emission tomography imaging. Functional networks and survival plot identified by network analysis of lung cancer patients (A–D). (A) Clustered network from PET data where node sizes represent days since last clinical assessment (kNN value of 5). (B) Clustered network from PET data where node colours represent males and females (kNN value of 5). (C) Clustered network from PET data where node colours represent vital status (kNN value of 5). (D) Survival plot of lung cancer patients with days from last clinical assessment on x axis and probability of survival on y axis.

References

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