The Image Biomarker Standardization Initiative validated consensus-based reference values for 169 radiomics features, thus enabling calibration and verification of radiomics software. Key results: • research teams found agreement for calculation of 169 radiomics features derived from a digital phantom and a human lung cancer on CT scan. • Of these 169 candidate radiomics features, good to excellent reproducibility was achieved for 167 radiomics features using MRI, 18F-FDG PET and CT images obtained in 51 patients with soft-tissue sarcoma.

There is evidence in some solid tumors that textural features of tumoral uptake in 18 F-FDG PET images are associated with response to chemoradiotherapy and survival. We have investigated whether a similar relationship exists in non-small cell lung cancer (NSCLC). Methods: Fifty-three patients (mean age, 65.8 y; 31 men, 22 women) with NSCLC treated with chemoradiotherapy underwent pretreatment 18 F-FDG PET/CT scans. Response was assessed by CT Response Evaluation Criteria in Solid Tumors (RECIST) at 12 wk. Overall survival (OS), progression-free survival (PFS), and local PFS (LPFS) were recorded. Primary tumor texture was measured by the parameters coarseness, contrast, busyness, and complexity. The following parameters were also derived from the PET data: primary tumor standardized uptake values (SUVs) (mean SUV, maximum SUV, and peak SUV), metabolic tumor volume, and total lesion glycolysis. Results: Compared with nonresponders, RECIST responders showed lower coarseness (mean, 0.012 vs. 0.027; P 5 0.004) and higher contrast (mean, 0.11 vs. 0.044; P 5 0.002) and busyness (mean, 0.76 vs. 0.37; P 5 0.027). Neither complexity nor any of the SUV parameters predicted RECIST response. By Kaplan-Meier analysis, OS, PFS, and LPFS were lower in patients with high primary tumor coarseness (median, 21.1 mo vs. not reached, P 5 0.003; 12.6 vs. 25.8 mo, P 5 0.002; and 12.9 vs. 20.5 mo, P 5 0.016, respectively). Tumor coarseness was an independent predictor of OS on multivariable analysis. Contrast and busyness did not show significant associations with OS (P 5 0.075 and 0.059, respectively), but PFS and LPFS were longer in patients with high levels of each (for contrast: median of 20.5 vs. 12.6 mo, P 5 0.015, and median not reached vs. 24 mo, P 5 0.02; and for busyness: median of 20.5 vs. 12.6 mo, P 5 0.01, and median not reached vs. 24 mo, P 5 0.006). Neither complexity nor any of the SUV parameters showed significant associations with the survival parameters. Conclusion: In NSCLC, baseline 18 F-FDG PET scan uptake showing abnormal texture as measured by coarseness, contrast, and busyness is associated with nonresponse to chemoradiotherapy by RECIST and with poorer prognosis. Measurement of tumor metabolic heterogeneity with these parameters may provide indices that can be used to stratify patients in clinical trials for lung cancer chemoradiotherapy.

Response to erlotinib is associated with reduced heterogeneity at (18)F-FDG PET. Changes in first-order entropy are independently associated with OS and treatment response.

PurposeRadiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior.Methods and MaterialsRadiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly.ResultsIn relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response.ConclusionsAlthough at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging.

Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

BackgroundMeasures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) 18F-FDG PET/CT images.Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy 18F-FDG PET/CT scans. Three readers segmented tumours using freehand (FH), 40% of maximum intensity threshold (40P), and fuzzy locally adaptive Bayesian (FLAB) algorithms. Intraclass correlation coefficient (ICC) was used to measure the inter-observer variability of the texture features derived by the three segmentation algorithms. Univariate cox regression was used on 12 commonly reported texture features to predict overall survival (OS) for each segmentation algorithm. Model quality was compared across segmentation algorithms using Akaike information criterion (AIC).Results40P was the most reproducible algorithm (median ICC 0.9; interquartile range [IQR] 0.85–0.92) compared with FLAB (median ICC 0.83; IQR 0.77–0.86) and FH (median ICC 0.77; IQR 0.7–0.85). On univariate cox regression analysis, 40P found 2 out of 12 variables, i.e. first-order entropy and grey-level co-occurence matrix (GLCM) entropy, to be significantly associated with OS; FH and FLAB found 1, i.e., first-order entropy. For each tested variable, survival models for all three segmentation algorithms were of similar quality, exhibiting comparable AIC values with overlapping 95% CIs.ConclusionsCompared with both FLAB and FH, segmentation with 40P yields superior inter-observer reproducibility of texture features. Survival models generated by all three segmentation algorithms are of at least equivalent utility. Our findings suggest that a segmentation algorithm using a 40% of maximum threshold is acceptable for texture analysis of 18F-FDG PET in NSCLC.

Alzheimer disease (AD) is a fatal neurodegenerative disorder characterized by progressive neuronal loss and cognitive decline. The lack of reliable and objective diagnostic markers for AD hampers early disease detection and treatment. Growing evidence supports the existence of a dysregulation in brain copper trafficking in AD. The aim of this study was to investigate brain copper trafficking in a transgenic mouse model of AD by PET imaging with 64 Cu, to determine its potential as a diagnostic biomarker of the disorder. Methods: Brain copper trafficking was evaluated in 6-to 8-mo-old TASTPM transgenic mice and age-matched wild-type controls using the 64 Cu bis(thiosemicarbazone) complex 64 Cu-GTSM (glyoxalbis(N 4 -methyl-3-thiosemicarbazonato) copper(II)), which crosses the blood-brain barrier and releases 64 Cu bioreductively into cells. Animals were intravenously injected with 64 Cu-GTSM and imaged at 0-30 min and 24-25 h after injection. The images were analyzed by atlas-based quantification and texture analysis. Regional distribution of 64 Cu in the brain 24 h after injection was also evaluated via ex vivo autoradiography and compared with amyloid-β plaque deposition in TASTPM mice. Results: Compared with controls, in TASTPM mice PET image analysis demonstrated significantly increased (by a factor of ∼1.3) brain concentration of 64 Cu at 30 min (P , 0.01) and 24 h (P , 0.05) after injection of the tracer and faster (by a factor of ∼5) 64 Cu clearance from the brain (P , 0.01). Atlas-based quantification and texture analysis revealed significant differences in regional brain uptake of 64 Cu and PET image heterogeneity between the 2 groups of mice. Ex vivo autoradiography showed that regional brain distribution of 64 Cu at 24 h after injection did not correlate with amyloid-β plaque distribution in TASTPM mice. Conclusion: The trafficking of 64 Cu in the brain after administration of 64 Cu-GTSM is significantly altered by AD-like pathology in the TASTPM mouse model, suggesting that 64 Cu-GTSM PET imaging warrants clinical evaluation as a diagnostic tool for AD and possibly other neurodegenerative disorders.

Aims Coronary CT angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular Fat Attenuation Index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardised FAI mapping together with clinical risk factors and plaque metrics to provide individualised cardiovascular risk prediction. Methods and Results The study included 3912 consecutive patients undergoing CCTA as part of clinical care in the United States (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardised interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model (Δ[C-statistic] of 0.085, P = 0.01 in the US Cohort and 0.149, P < 0.001 in the European cohort) and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk. Conclusion CaRi-Heart® reliably improves cardiovascular risk prediction by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a screening tool among patients referred for CCTA. Translational Perspective Mapping of perivascular Fat Attenuation Index (FAI) on coronary computed tomography angiography (CCTA) enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardised FAI mapping together with clinical risk factors and plaque metrics to provide age-standardised reference maps and individualised cardiovascular risk prediction. This integration advances the prognostic value of CCTA and paves the way for its use as a screening tool among patients referred for CCTA.