Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of ‘virtual’ and ‘augmented’ contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.

The “Sign-tracker/Goal-tracker” (ST/GT) is an animal model of individual differences in learning and motivational processes attributable to distinctive conditioned responses to environmental cues. While GT rats value the reward-predictive cue as a mere predictor, ST rats attribute it with incentive salience, engaging in aberrant reward-seeking behaviors that mirror those of impulse control disorders. Given its potential clinical value, the present study aimed to map such model onto humans and investigated resting state functional magnetic resonance imaging correlates of individuals categorized as more disposed to sign-tracking or goal-tracking behavior. To do so, eye-tracking was used during a translationally informed Pavlovian paradigm to classify humans as STs (n = 36) GTs (n = 35) or as Intermediates (n = 33), depending on their eye-gaze towards the reward-predictive cue or the reward location. Using connectivity and network-based approach, measures of resting state functional connectivity and centrality (role of a node as a hub) replicated preclinical findings, suggesting a major involvement of subcortical areas in STs, and dominant cortical involvement in GTs. Overall, the study strengthens the translational value of the ST/GT model, with important implications for the early identification of vulnerable phenotypes for psychopathological conditions such as substance use disorder.

In the last decade, it has been recognized that tumors do not exist in isolation but interact with surrounding cells, blood vessels, immune cells, and extracellular matrix components. This understanding has shifted the focus from tumor cells alone to the broader context in which they exist, known as tumor microenvironment (TME). The TME is highly heterogeneous, consisting of various cell types, mainly cancer cells, immune cells, and stromal cells. The interactions among different cell types in the TME significantly influence tumor progression, immune evasion, and response to therapy. Spatial profiling helps to map these interactions, providing insights into how cells communicate and influence each other, analyzing them in their spatial context. However, there is a lack of tools capable of efficiently analyzing this type of data. As a matter of fact, the most commonly used tool, phenoptr, is time consuming, lacks automation, and is often not user friendly. In this scenario, ALOA (Analysis spatiaL prOfiling imAging), represents a tool that, starting from inForm™ data, provides a complete and accurate analysis along with accompanying graphs and statistical analysis. Of note, ALOA is specifically designed to handle spatial coordinates and image-based data derived from multiplexed immunohistochemistry (IHC) and immunofluorescence (IF). Therefore, it is not suited to work with single cell transcriptomics or non-spatial single cell transcriptomics data, which require specific tools for handling high-dimensional gene expression information. We integrated Phenoimager multiplexed tissue imaging with the ALOA modeling algorithm.

Background: Hypoxic-ischemic encephalopathy (HIE) is a severe pathology, and no unique predictive biomarker has been identified. Our aims are to identify associations of perinatal and outcome parameters with morphological anomalies and ADC values from MRI. The secondary aims are to define a predictive ADC threshold value and detect ADC value fluctuations between MRIs acquired within 7 days (MR0) and at 1 year (MR1) of birth in relation to perinatal and outcome parameters. Methods: Fifty-one term children affected by moderate HIE treated with hypothermia and undergoing MRI0 and MRI1 were recruited. Brain MRIs were evaluated through the van Rooij score, while ADC maps were co-registered on a standardized cerebral surface, on which 29 ROIs were drawn. Statistical analysis was performed in Matlab, with the statistical significance value at 0.05. Results: ADC0 < ADC1 in the left and right thalami, left and right frontal white matter, right visual cortex, and the left dentate nucleus of children showing abnormal perinatal and neurodevelopmental parameters. At ROC analysis, the best prognostic ADC cut-off value was 1.535 mm²/s × 10⁻⁶ (sensitivity 80%, specificity 86%) in the right frontal white matter. ADC1 > ADC0 in the right visual cortex and left dentate nucleus, positively correlated with multiple abnormal perinatal and neurodevelopmental parameters. The van Rooij score was significantly higher in children presenting with sleep disorders. Conclusions: ADC values could be used as prognostic biomarkers to predict children’s neurodevelopmental outcomes. Further studies are needed to address these crucial topics and validate our results. Early and multidisciplinary perinatal evaluation and the subsequent re-assessment of children are pivotal to identify physical and neuropsychological disorders to guarantee early and tailored therapy.