Safety assessment of gadolinium-based contrast agents (GBCAs) requires consideration of long-term adverse effects in all human tissues

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN performance comparison in respect to expert interpretation of CMR with late gadolinium enhancement (LGE) images, used as ground truth (GT), was conducted on 206 patients (158 scar, 48 control) from Centro Cardiologico Monzino (Milan, Italy) at both slice- and patient-levels. Left ventricle dynamic features were extracted in non-enhanced cine images using parametric images based on both Fourier and monogenic signal analyses. The CNN, fed with cine images and Fourier-based parametric images, achieved an area under the ROC curve of 0.86 (accuracy 0.79, F1 0.81, sensitivity 0.9, specificity 0.65, and negative (NPV) and positive (PPV) predictive values 0.83 and 0.77, respectively), for individual slice classification. Remarkably, it exhibited 1.0 prediction accuracy (F1 0.98, sensitivity 1.0, specificity 0.9, NPV 1.0, and PPV 0.97) in patient classification as a control or pathologic. The proposed approach represents a first step towards scar detection in contrast-free CMR images. Patient-level results suggest its preliminary potential as a screening tool to guide decisions regarding LGE-CMR prescription, particularly in cases where indication is uncertain.

Functional and anatomical imaging in pediatric oncology: which is best for which tumors

Functional imaging techniques are playing an increasingly important role in the management of pediatric cancer. Technological advances have pushed the development of hybrid imaging techniques, including positron emission tomography (PET)/CT, PET/MR and single-photon emission computed tomography (SPECT)/CT. Together with an increasing need to identify surrogate biomarkers for response to novel therapies, the use of functional imaging techniques, which had been reserved primarily for lymphoma patients, is now being recognized as standard of care for the management of many other pediatric solid tumors. The purpose of this review is to summarize recent data describing the use of functional and metabolic imaging strategies for the staging and response assessment of common pediatric solid tumors, and to offer some guidance as to which techniques are most appropriate for which tumor types.