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Purpose: The aim of this pilot study was to prospectively evaluate the first integrated positron emission tomography (PET)/dual-energy computed tomography (DECT) system performance in patients with non-small cell lung cancer (NSCLC)., Materials and Methods: In this single-center, prospective trial, consecutive patients with NSCLC referred for a PET study between May 2017 and June 2018 were enrolled. All patients received contrast-enhanced imaging on a clinical PET/DECT system. Data analysis included PET-based standard uptake values (SUVmax) and DECT-based iodine densities of tumor masses, lymph nodes, and distant metastases. Results were analyzed using correlation tests and receiver operating characteristics curves., Results: The study population was composed of 21 patients (median age 62 y, 14 male patients). A moderate positive correlation was found between iodine density values (2.2 mg/mL) and SUVmax (10.5) in tumor masses (ρ=0.53, P<0.01). Iodine density values (2.3 mg/mL) and SUVmax (5.4) of lymph node metastases showed a weak positive correlation (ρ=0.23, P=0.14). In addition, iodine quantification analysis provided no added value in differentiating between pathologic and nonpathologic lymph nodes with an area under the curve (AUC) of 0.55 using PET-based SUVmax as the reference standard. A weak positive correlation was observed between iodine density (2.2 mg/mL) and SUVmax in distant metastases (14.9, ρ=0.23, P=0.52)., Conclusions: The application of an integrated PET/DECT system in lung cancer might provide additional insights in the assessment of tumor masses. However, the added value of iodine density quantification for the evaluation of lymph nodes and distant metastases seems limited., Competing Interests: U.J.S. receives institutional research support and/or honoraria for speaking and consulting from Astellas, Bayer, Bracco, Elucid BioImaging, Guerbet, HeartFlow Inc., and Siemens Healthineers. C.N.D.C. is a consultant for/receives institutional research support from Siemens Healthineers. S.S.M. received speakers’ fees from Siemens Healthineers. The remaining authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

During the latest years, artificial intelligence, and especially machine learning (ML), have experienced a growth in popularity due to their versatility and potential in solving complex problems. In fact, ML allows the efficient handling of big volumes of data, allowing to tackle issues that were unfeasible before, especially with deep learning, which utilizes multilayered neural networks. Cardiac computed tomography (CT) is also experiencing a rise in examination numbers, and ML might help handle the increasing derived information. Moreover, cardiac CT presents some fields wherein ML may be pivotal, such as coronary calcium scoring, CT angiography, and perfusion. In particular, the main applications of ML involve image preprocessing and postprocessing, and the development of risk assessment models based on imaging findings. Concerning image preprocessing, ML can help improve image quality by optimizing acquisition protocols or removing artifacts that may hinder image analysis and interpretation. ML in image postprocessing might help perform automatic segmentations and shorten examination processing times, also providing tools for tissue characterization, especially concerning plaques. The development of risk assessment models from ML using data from cardiac CT could aid in the stratification of patients who undergo cardiac CT in different risk classes and better tailor their treatment to individual conditions. While ML is a powerful tool with great potential, applications in the field of cardiac CT are still expanding, and not yet routinely available in clinical practice due to the need for extensive validation. Nevertheless, ML is expected to have a big impact on cardiac CT in the near future.

Objective: The objective of this study was to compare the use of pulmonary regurgitation volume (PRV) or indexed PRV (PRVi) with that of pulmonary regurgitation fraction (PRF) in the assessment of patients with pulmonary regurgitation (PR) undergoing cardiac magnetic resonance (CMR) imaging., Materials and Methods: CMR of 176 patients with PR were retrospectively evaluated. Their right ventricular diastolic (end-diastolic volume index [EDVi]) and systolic (end-systolic volume index) volume indexes, stroke volume, and ejection fraction were obtained from cine CMR sequences, whereas phase-contrast flow sequences were analyzed to obtain PRV, PRVi, and PRF. Patients were divided into subgroups, according to underlying pathology and according to PR severity. Correlations between PRV or PRF and RV parameters were studied through Spearman ρ, both in the main group and subgroups. Follow-up examinations were analyzed, and correlations between PRV or PRF from the first CMR examination and volume data from the second were calculated., Results: Tetralogy of Fallot was the main setting of PR (98/179). Overall, EDVi strongly correlates with PRV (ρ=0.592, P<0.001) than with PRF (ρ=0.522, P<0.001), and end-systolic volume index strongly correlates with PRV (ρ=0.454, P<0.001) and PRF (ρ=0.406, P <0.001). As regards subgroup analysis, in moderate or severe PR patients, EDVi strongly correlates (P=0.043) with PRV (ρ=0.499, P<0.001) than with PRF (ρ=0.317, P<0.001). Follow-up EDVi correlates with PRV (ρ=0.450, P=0.031), but not with PRF., Conclusions: Especially when assessing moderate to severe PR, PRV and PRVi may be better indicators of right ventricular dysfunction than PRF. Moreover, PRV may be a predictor of worsening RV dilation.