Background In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24 min imaging window and an input function modelled using measurements from a region of interest placed over the left ventricle. Methods To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36 minute validation imaging window to compare 1) the modelled AIF against the input function measured in the validation window; and 2) the net influx rate (\({K}_{i}\)) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. Results Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson’s correlation between \({K}_{i}\) estimates from the short imaging window and the validation imaging window exceeded 0.95. Conclusion The proposed 24 min triple injection protocol enables parametric 18F-FDG neuroimaging with non-invasive estimation of the AIF from cardiac images using a standard field of view PET scanner.
Objective: Radiomics studies have shown promising results defining image surrogates that predict treatment response and survival in oesophageal squamous cell carcinoma. There are limited studies examining radiogenomics profiles in oesophageal adenocarcinoma (OAC) and this warrants further exploration. Background: Although advances have been made in the treatment of OAC, the five-year overall survival remains poor. Hence, there is a need for biomarkers to improve therapeutic approaches. Methods: Image analysis was performed on pre-treatment PET/CT scans from 80 OAC patients. In the discovery cohort (n=50), CT texture analysis was performed using a filtration-histogram method to evaluate the prognostic value of image features. Whole-genome sequencing, RNA sequencing (RNAseq) and immunohistochemistry characterised the molecular pathways associated with the CT markers. A validation cohort (n=30) was used to confirm radiomics biomarkers. Results: Analysis showed 3 CT markers were significantly associated with both disease-specific and disease-free survival. Combining these markers provided further prognostic significance. Furthermore, we identified a correlation between CT markers and major pathological response rate. Both RNAseq and immunohistochemistry confirmed that lower CD8 T-cell expression in the tumour correlated with the poor survival group predicted using CT markers. RNAseq gene set enrichment analysis identified pathways associated with these radiomics biomarkers. Finally, radiomics analysis in a validation cohort confirmed CT marker (SD, standard deviation) as a biomarker of survival and identified additional image features associated with patient survival. Conclusions: This study demonstrates that radiogenomics has the potential to identify OAC subgroups with prognostic significance and support clinical decision making.
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