Chest Dynamic MRI as Early Biomarker of Respiratory Impairment in Amyotrophic Lateral Sclerosis Patients: A Pilot Study.

Background: Amyotrophic lateral sclerosis (ALS) is a neuromuscular progressive disorder characterized by limb and bulbar muscle wasting and weakness. A total of 30% of patients present a bulbar onset, while 70% have a spinal outbreak. Respiratory involvement represents one of the worst prognostic factors, and its early identification is fundamental for the early starting of non-invasive ventilation and for the stratification of patients. Due to the lack of biomarkers of early respiratory impairment, we aimed to evaluate the role of chest dynamic MRI in ALS patients. Methods: We enrolled 15 ALS patients and 11 healthy controls. We assessed the revised ALS functional rating scale, spirometry, and chest dynamic MRI. Data were analyzed by using the Mann-Whitney U test and Cox regression analysis. Results: We observed a statistically significant difference in both respiratory parameters and pulmonary measurements at MRI between ALS patients and healthy controls. Moreover, we found a close relationship between pulmonary measurements at MRI and respiratory parameters, which was statistically significant after multivariate analysis. A sub-group analysis including ALS patients without respiratory symptoms and with normal spirometry values revealed the superiority of chest dynamic MRI measurements in detecting signs of early respiratory impairment. Conclusions: Our data suggest the usefulness of chest dynamic MRI, a fast and economically affordable examination, in the evaluation of early respiratory impairment in ALS patients.

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows.

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.