Purpose. After radiofrequency ablation (RFA) of hepatocellular carcinoma (HCC), pre- and postinterventional contrast-enhanced CT (CECT) images are usually qualitatively interpreted to determine technical success, by eyeballing. The objective of this study was to evaluate the feasibility of quantitative assessment, using a nonrigid CT-CT coregistration algorithm. Materials and Methods. 25 patients treated with RFA for HCC between 2009 and 2014 were retrospectively included. Semiautomated coregistration of pre- and posttreatment CECT was performed independently by two radiologists. In scans with a reliable registration, the tumor and ablation area were delineated to identify the side and size of narrowest RFA margin. In addition, qualitative assessment was performed independently by two other radiologists to determine technical success and the anatomical side and size of narrowest margin. Interobserver agreement rates were determined for both methods, and the outcomes were compared with occurrence of local tumor progression (LTP). Results. CT-CT coregistration was technically feasible in 18/25 patients with almost perfect interobserver agreement for quantitative analysis (κ = 0.88). The interobserver agreement for qualitative RFA margin analysis was κ = 0.64. Using quantitative assessment, negative ablative margins were found in 12/18 patients, with LTP occurring in 8 of these patients. In the remaining 6 patients, quantitative analysis demonstrated complete tumor ablation and no LTP occurred. Conclusion. Feasibility of quantitative RFA margin assessment using nonrigid coregistration of pre- and postablation CT is limited, but appears to be a valuable tool in predicting LTP in HCC patients (p=0.013).
Purpose To evaluate whether quantitative [18F]FDG-PET/CT assessment, including radiomic analysis of [18F]FDG-positive thyroid nodules, improved the preoperative differentiation of indeterminate thyroid nodules of non-Hürthle cell and Hürthle cell cytology. Methods Prospectively included patients with a Bethesda III or IV thyroid nodule underwent [18F]FDG-PET/CT imaging. Receiver operating characteristic (ROC) curve analysis was performed for standardised uptake values (SUV) and SUV-ratios, including assessment of SUV cut-offs at which a malignant/borderline neoplasm was reliably ruled out (≥ 95% sensitivity). [18F]FDG-positive scans were included in radiomic analysis. After segmentation at 50% of SUVpeak, 107 radiomic features were extracted from [18F]FDG-PET and low-dose CT images. Elastic net regression classifiers were trained in a 20-times repeated random split. Dimensionality reduction was incorporated into the splits. Predictive performance of radiomics was presented as mean area under the ROC curve (AUC) across the test sets. Results Of 123 included patients, 84 (68%) index nodules were visually [18F]FDG-positive. The malignant/borderline rate was 27% (33/123). SUV-metrices showed AUCs ranging from 0.705 (95% CI, 0.601–0.810) to 0.729 (0.633–0.824), 0.708 (0.580–0.835) to 0.757 (0.650–0.864), and 0.533 (0.320–0.747) to 0.700 (0.502–0.898) in all (n = 123), non-Hürthle (n = 94), and Hürthle cell (n = 29) nodules, respectively. At SUVmax, SUVpeak, SUVmax-ratio, and SUVpeak-ratio cut-offs of 2.1 g/mL, 1.6 g/mL, 1.2, and 0.9, respectively, sensitivity of [18F]FDG-PET/CT was 95.8% (95% CI, 78.9–99.9%) in non-Hürthle cell nodules. In Hürthle cell nodules, cut-offs of 5.2 g/mL, 4.7 g/mL, 3.4, and 2.8, respectively, resulted in 100% sensitivity (95% CI, 66.4–100%). Radiomic analysis of 84 (68%) [18F]FDG-positive nodules showed a mean test set AUC of 0.445 (95% CI, 0.290–0.600) for the PET model. Conclusion Quantitative [18F]FDG-PET/CT assessment ruled out malignancy in indeterminate thyroid nodules. Distinctive, higher SUV cut-offs should be applied in Hürthle cell nodules to optimize rule-out ability. Radiomic analysis did not contribute to the additional differentiation of [18F]FDG-positive nodules. Trial registration number This trial is registered with ClinicalTrials.gov: NCT02208544 (5 August 2014), https://clinicaltrials.gov/ct2/show/NCT02208544.
Background Radiomic features, extracted from positron emission tomography, aim to characterize tumour biology based on tracer intensity, tumour geometry and/or tracer uptake heterogeneity. Currently, radiomic features are derived from static images. However, temporal changes in tracer uptake might reveal new aspects of tumour biology. This study aims to explore additional information of these novel dynamic radiomic features compared to those derived from static or metabolic rate images. Methods Thirty-five patients with non-small cell lung carcinoma underwent dynamic [ 18 F]FDG PET/ CT scans. Spatial intensity, shape and texture radiomic features were derived from volumes of interest delineated on static PET and parametric metabolic rate PET. Dynamic grey level cooccurrence matrix (GLCM) and grey level run length matrix (GLRLM) features, assessing the temporal domain unidirectionally, were calculated on eight and sixteen time frames of equal length. Spearman's rank correlations of parametric and dynamic features with static features were calculated to identify features with potential additional information. Survival analysis was performed for the non-redundant temporal features and a selection of static features using Kaplan-Meier analysis. Results Three out of 90 parametric features showed moderate correlations with corresponding static features (ρ�0.61), all other features showed high correlations (ρ>0.7). Dynamic features are robust independent of frame duration. Five out of 22 dynamic GLCM features showed a negligible to moderate correlation with any static feature, suggesting additional information. All sixteen dynamic GLRLM features showed high correlations with static features, implying redundancy. Log-rank analyses of Kaplan-Meier survival curves for all features dichotomised at the median were insignificant.
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