Dual Energy CT in Oncology: Benefits for Both Patients and Radiologists From an Emerging Quantitative and Functional Diagnostic Technique.

Dual-energy CT (DECT) imaging makes it possible to identify the characteristics of materials that cannot be recognized with conventional single-energy CT (SECT). In the postprocessing study phase, virtual monochromatic images and virtual-non-contrast (VNC) images, also permits reduction of dose exposure by eliminating the precontrast acquisition scan. Moreover, in virtual monochromatic images, the iodine contrast increases when the energy level decreases resulting in better visualization of hypervascular lesions and in a better tissue contrast between hypovascular lesions and the surrounding parenchyma; thus, allowing for reduction of required iodinate contrast material, especially important in patients with renal impairment. All these advantages are particularly important in oncology, providing the possibility of overcoming many SECT imaging limits and making CT examinations safer and more feasible in critical patients. This review explores the basis of DECT imaging and its utility in routine oncologic clinical practice, with particular attention to the benefits of this technique for both the patients and the radiologists.

Quantitative imaging decision support (QIDSTM) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan.

OBJECTIVE: To evaluate the consistency of the quantitative imaging decision support (QIDSTM) tool and radiomic analysis using 594 metrics in lung carcinoma on chest CT scan. MATERIALS AND METHODS: We included, retrospectively, 150 patients with histologically confirmed lung cancer who underwent chemotherapy and baseline and follow-ups CT scans. Using the QIDSTM platform, 3 radiologists segmented each lesion and automatically collected the longest diameter and the density mean value. Inter-observer variability, Bland Altman analysis and Spearman's correlation coefficient were performed. QIDSTM tool consistency was assessed in terms of agreement rate in the treatment response classification. Kruskal Wallis test and the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross validation were used to identify radiomic metrics correlated with lesion size change. RESULTS: Good and significant correlation was obtained between the measurements of largest diameter and of density among the QIDSTM tool and the radiologists measurements. Inter-observer variability values were over 0.85. HealthMyne QIDSTM tool quantitative volumetric delineation was consistent and matched with each radiologist measurement considering the RECIST classification (80-84%) while a lower concordance among QIDSTM and the radiologists CHOI classification was observed (58-63%). Among 594 extracted metrics, significant and robust predictors of RECIST response were energy, histogram entropy and uniformity, Kurtosis, coronal long axis, longest planar diameter, surface, Neighborhood Grey-Level Different Matrix (NGLDM) dependence nonuniformity and low dependence emphasis as Volume, entropy of Log(2.5 mm), wavelet energy, deviation and root man squared. CONCLUSION: In conclusion, we demonstrated that HealthMyne quantitative volumetric delineation was consistent and that several radiomic metrics extracted by QIDSTM were significant and robust predictors of RECIST response.