Pituitary intensity at magnetic resonance imaging is reduced in obese patients: results from the CHIASM study

Puliani, Giulia; Sbardella, Emilia; Cozzolino, Alessia; Pozza, Carlotta; Feola, Tiziana; Gianfrilli, Daniele; Lubrano, Carla; Fiorelli, Marco; Lenzi, Andrea; Isidori, Andrea M.

doi:10.1530/endoabs.56.p873

Cited by 2 publications

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“…For example, part of a wider meta-inflammatory status specific for obese patients, chronic inflammation of the pituitary gland has been reported. 42 This is one of the main endocrine glands that also plays a role in modulating the immune response to infection, mainly by regulating lymphocyte differentiation processes. 43 Growth hormones and prolactin are essential for lymphocyte maturation, for ensuring the ability of lymphocytes to respond to antigen presence, and for maintaining the immunocompetence status.…”

Section: Endocrine Dysregulationmentioning

confidence: 99%

The Impact of Obesity on the Host–Pathogen Interaction with Influenza Viruses – Novel Insights: Narrative Review

Miron,

Drăgănescu,

Pițigoi

et al. 2024

DMSO

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After exposure to a viral pathogen, the host-pathogen interaction is essential to determine whether or not infection will ensue, and what the clinical outline of the infection will be. Recent research has shown that the patient with obesity presents a set of particular pathophysiological changes that lead to higher severity of viral infections, and this is particularly true for infection with influenza viruses. Herein, we describe the main metabolic, endocrine, and immune dysregulations that occur in the presence of obesity and their impact on driving intra-host viral diversity, leading to heightened severity and virulence of influenza. We show that obesity is linked to modified responses of both the innate and adaptive immune systems during viral infections, including influenza. Due to chronic inflammation and metabolic, endocrine, and signaling pathway disruptions, individuals with obesity have a suboptimal immune response. This results in longer illness duration, increased virus shedding, higher risk of hospitalization and complications, and greater mortality rates. Additionally, they may have a blunted response to vaccination and a higher likelihood of genetic mutation selection. Understanding the intricate interplay between obesity and viral pathogenesis is crucial for developing efficacious therapeutic approaches and public health policies, particularly in light of the escalating worldwide incidence of obesity.

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Section: Endocrine Dysregulationmentioning

confidence: 99%

The Impact of Obesity on the Host–Pathogen Interaction with Influenza Viruses – Novel Insights: Narrative Review

Miron,

Drăgănescu,

Pițigoi

et al. 2024

DMSO

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“…Magnetic resonance imaging (MRI) is the most widely used imaging modality to diagnose and monitor PG-related diseases such as tumor or mood disorders (11)(12)(13) as MRI can provide high-resolution images of the PG including its surrounding structures, such as the optic chiasm, cavernous sinuses, and carotid arteries (8,14,15). Through this modality, small changes in signal intensity or enhancement patterns that reflect different pathological processes of the PG can be detected (15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

“…These deep-learning-based segmentation methods have exhibited robust performances for an abnormal PG such as pituitary adenomas with varying architectures (9,35,36). However, those methods are not applicable to normal PG segmentation although the morphometry of the normal PG is equally important for the evaluation of health conditions such as obesity, growth disorders, and abnormal morphometry such as adenomas (2,5,17,25). A recent report indicated that obese patients exhibited a larger PG volume compared to both normal and overweight subjects (5).…”

Section: Introductionmentioning

confidence: 99%

deepPGSegNet: MRI-based pituitary gland segmentation using deep learning

Choi,

Sung,

Ogawa

2024

Front. Endocrinol.

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IntroductionIn clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, which is the traditional method, is tedious and susceptible to inter-observer differences. Thus, this study introduces an automated solution, utilizing deep learning, for PG segmentation from magnetic resonance imaging (MRI).MethodsA total of 153 university students were enrolled, and their MRI images were used to build a training dataset and ground truth data through manual segmentation of the PGs. A model was trained employing data augmentation and a three-dimensional U-Net architecture with a five-fold cross-validation. A predefined field of view was applied to highlight the PG region to optimize memory usage. The model’s performance was tested on an independent dataset. The model’s performance was tested on an independent dataset for evaluating accuracy, precision, recall, and an F1 score.Results and discussionThe model achieved a training accuracy, precision, recall, and an F1 score of 92.7%, 0.87, 0.91, and 0.89, respectively. Moreover, the study explored the relationship between PG morphology and age using the model. The results indicated a significant association between PG volume and midsagittal area with age. These findings suggest that a precise volumetric PG analysis through an automated segmentation can greatly enhance diagnostic accuracy and surveillance of pituitary disorders.

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Pituitary intensity at magnetic resonance imaging is reduced in obese patients: results from the CHIASM study

Cited by 2 publications

References 0 publications

The Impact of Obesity on the Host–Pathogen Interaction with Influenza Viruses – Novel Insights: Narrative Review

The Impact of Obesity on the Host–Pathogen Interaction with Influenza Viruses – Novel Insights: Narrative Review

deepPGSegNet: MRI-based pituitary gland segmentation using deep learning

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