In this paper four scatter-compensation schemes are considered. The four schemes are all based on a previously developed two-dimensional (2-D) scatter model. Reconstruction is achieved using the iterative expectation-maximization maximum-likelihood (EM-ML) algorithm. The schemes consist of: 1) including the model in both the forward and back projector; 2) just including the model in the forward projector; 3) and 4) implementing the model in a subtraction and addition scheme, respectively. Monte Carlo simulated projection data are used to test the accuracy, convergence properties, and noise properties of the four scatter-compensation schemes. Data are simulated for both uniformly and nonuniformly attenuating objects. The results show that all four correction schemes yield images which are similar in terms of accuracy to that obtained from reconstructing scatter-free data. The subtraction scheme is shown to converge faster than the other compensation schemes, both in terms of iterations and actual time required for reconstruction. The scheme in which the model is only used in the forward-projector and the scatter-addition scheme both performs slightly better, in terms of signal-to-noise ratio (SNR), than the subtraction scheme. However, the forward projector scheme requires significantly more CPU time for reconstruction. The correction scheme in which the scatter model was included in both the forward and backprojectors is shown to produce accurate images with SNR's higher than even a perfect scatter rejection scheme. While the scatter correction scheme with the model in both the forward projector and backprojector has superior noise properties to the other algorithms, the results suggest that the faster subtraction/addition schemes will probably prove most useful for routine clinical scatter compensation.

Background: Positron emission tomography (PET) images of head and neck squamous cell carcinoma (HNSCC) patients can assess the functional and biochemical processes at cellular levels. Therefore, PET radiomics‐based prediction and prognostic models have the potentials to understand tumour heterogeneity and assist clinicians with diagnosis, prognosis and management of the disease. We conducted a systematic review of published modelling information to evaluate the usefulness of PET radiomics in the prediction and prognosis of HNSCC patients.Methods: We searched bibliographic databases (MEDLINE, Embase, Web of Science) from 2010 to 2021 and considered 31 studies with pre‐defined inclusion criteria. We followed the CHARMS checklist for data extraction and performed quality assessment using the PROBAST tool. We conducted a meta‐analysis to estimate the accuracy of the prediction and prognostic models using the diagnostic odds ratio (DOR) and average C‐statistic, respectively.Results: Manual segmentation method followed by 40% of the maximum standardised uptake value (SUV_max) thresholding is a commonly used approach. The area under the receiver operating curves of externally validated prediction models ranged between 0.60–0.87, 0.65–0.86 and 0.62–0.75 for overall survival, distant metastasis and recurrence, respectively. Most studies highlighted an overall high risk of bias (outcome definition, statistical methodologies and external validation of models) and high unclear concern in terms of applicability. The meta‐analysis showed the estimated pooled DOR of 6.75 (95% CI: 4.45, 10.23) for prediction models and the C‐statistic of 0.71 (95% CI: 0.67, 0.74) for prognostic models.Conclusions: Both prediction and prognostic models using clinical variables and PET radiomics demonstrated reliable accuracy for detecting adverse outcomes in HNSCC, suggesting the prospect of PET radiomics in clinical settings for diagnosis, prognosis and management of HNSCC patients. Future studies of prediction and prognostic models should emphasise the quality of reporting, external model validation, generalisability to real clinical scenarios and enhanced reproducibility of results.

Background: Patients with breast cancer undergoing biological therapy and/or chemotherapy perform multiple radionuclide angiography (RNA) or multigated acquisition (MUGA) scans to assess cardiotoxicity. The association between RNA imaging parameters and left ventricular (LV) ejection fraction (LVEF) remains unclear. Objectives: This study aimed to extract and evaluate the association of several novel imaging biomarkers to detect changes in LVEF in patients with breast cancer undergoing chemotherapy. Methods: We developed and optimized a novel set of MATLAB routines called the “RNA Toolbox” to extract parameters from RNA images. The code was optimized using various statistical tests (e.g., ANOVA, Bland-Altman, and intraclass correlation tests). We quantitatively analyzed the images to determine the association between these parameters using regression models and receiver operating characteristic (ROC) curves. Results: The code was reproducible and showed good agreement with validated clinical software for the parameters extracted from both packages. The regression model and ROC results were statistically significant in predicting LVEF (R² = 0.40, P < 0.001) (AUC = 0.78). Some time-based, shape-based, and count-based parameters were significantly associated with post-chemotherapy LVEF (β = 0.09, P < 0.001), LVEF of phase image (β = 4, P = 0.030), approximate entropy (ApEn) (β = 11.6, P = 0.001), ApEn (diastolic and systolic) (β = 39, P = 0.002) and LV systole size (β = 0.03, P = 0.010). Conclusions: Despite the limited sample size, we observed evidence of associations between several parameters and LVEF. We believe that these parameters will be more beneficial than the current methods for patients undergoing cardiotoxic chemotherapy. Moreover, this approach can aid physicians in evaluating subclinical cardiac changes during chemotherapy, and in understanding the potential benefits of cardioprotective drugs.

High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models—penalized Cox model, random forest, gradient boosted model and support vector machine—to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months of follow-up, respectively. We developed models with five-fold cross-validation, selected the best-performing model for each outcome based on the concordance index (C-statistic) and the integrated Brier score (IBS) and validated them in an independent cohort of 102 patients. The penalized Cox model demonstrated better performance for ACM (C-statistic = 0.70, IBS = 0.12) and DM (C-statistic = 0.70, IBS = 0.08) while the random forest model displayed better performance for LR (C-statistic = 0.76, IBS = 0.07). We conclude that the ML-based prognostic model can aid clinicians in quantifying prognosis and determining effective treatment strategies, thereby improving favorable outcomes in HNSCC patients.

1 Background: Predictive biomarkers (BMs) for EGJAc would optimise treatment selection and avoid ineffective therapy. Metabolic response (MR) defined as >35% decrease in tumour FDG Standardized Uptake Value (SUV) between day 0 & 14 after starting chemotherapy has a high negative predictive value (95%) for response, but limited positive predictive value (50%). Combining molecular BMs with FDG-PET may optimise response prediction. We used global gene expression profiling (GEP) to identify molecular BMs that when combined with FDG-PET would improve predictive accuracy. Methods: 28 patients with locally advanced or metastatic EGJAc received platinum based chemotherapy (PBC). FDG PET CT scans were at day 0 and day 14 and GEP (Affymetrix ST1.0 Exon Genechips) on day 0 tumour biopsies. A tissue microarray comprising an independent set of 154 OGJAc who underwent surgery +/− neoadjuvant PBC was used with immunohistochemistry (IHC) for qualification of GEP results. Radiological response was assessed after 3/ 4 cycles of PBC by RECISTv1.1. Results: We identified a gene expression signature (86 genes) that separated FDG PET MR patients(>35% fall SUV day 0 to14) into those that do and do not go on to have a RECIST response. In cross validation, this signature correctly predicted response in 28/28. Pathway analysis on GEP data identified potential novel mechanisms of response, including the Leptin pathway. Leptin mRNA was higher in FDG metabolic responders who did not have a RECIST response compared to those that did. In the independent set, high Leptin protein by IHC was strongly associated with lack of histopathologic response to neoadjuvant PBC (n=64, p=0.002). High Leptin expression also had a therapy independent prognostic effect with longer survival in the absence of histopathologic response or with no neoadjuvant PBC and in low Leptin patients poor survival was mitigated to a certain extent by neoadjuvant PBC (n=154, Kaplan-Mieier, log rank p=0.041 & Cox MVA p=0.040). Conclusions: Molecular biomarkers (Leptin in particular) combine with FDG PET to optimise response prediction in EGJAc. Further investigation of this combined molecular and imaging approach is warranted.

The bile acid analogue [¹⁸F]LCATD (LithoCholic Acid Triazole Derivative) is transported in vitro by hepatic uptake transporters such as OATP1B1 and NTCP and efflux transporter BSEP. In this in vivo “proof of principle” study, we tested if [¹⁸F]LCATD may be used to evaluate drug-drug interactions (DDIs) caused by inhibition of liver transporters. Hepatic clearance of [¹⁸F]LCATD in rats was significantly modified upon coadministration of rifamycin SV or sodium fusidate, which are known to inhibit clinically relevant uptake transporters (OATP1B1, NTCP) and canalicular hepatic transporters (BSEP) in humans. Treatment with rifamycin SV (total dose 62.5 mg·Kg⁻¹) reduced the maximum radioactivity of [¹⁸F]LCATD recorded in the liver from 14.2 ± 0.8% to 10.2 ± 0.9% and delayed t_{_max} by 90 seconds relative to control rats. AUC_{liver 0–5 min}, AUC_{bile 0–10 min} and hepatic uptake clearance CL_{uptake,in vivo} of rifamycin SV treated rats were significantly reduced, whereas AUC_{liver 0–30 min} was higher than in control rats. Administration of sodium fusidate (30 mg·Kg⁻¹) inhibited the liver uptake of [¹⁸F]LCATD, although to a lesser extent, reducing the maximum radioactivity in the liver to 11.5 ± 0.3%. These preliminary results indicate that [¹⁸F]LCATD may be a good candidate for future applications as an investigational tracer to evaluate altered hepatobiliary excretion as a result of drug-induced inhibition of hepatic transporters.