Objective Cushing’s syndrome is a rare disease characterized by clinical features that show morphological similarity with the metabolic syndrome. Distinguishing these diseases in clinical practice is challenging. We have previously shown that computer vision technology can be a potentially useful diagnostic tool in Cushing’s syndrome. In this follow-up study, we addressed the described problem by increasing the sample size and including controls matched by body mass index.
Methods We enrolled 82 patients (22 male, 60 female) and 98 control subjects (32 male, 66 female) matched by age, gender and body-mass-index. The control group consisted of patients with initially suspected, but biochemically excluded Cushing’s syndrome. Standardized frontal and profile facial digital photographs were acquired. The images were analyzed using specialized computer vision and classification software. A grid of nodes was semi-automatically placed on disease-relevant facial structures for analysis of texture and geometry. Classification accuracy was calculated using a leave-one-out cross-validation procedure with a maximum likelihood classifier.
Results The overall correct classification rates were 10/22 (45.5%) for male patients and 26/32 (81.3%) for male controls, and 34/60 (56.7%) for female patients and 43/66 (65.2%) for female controls. In subgroup analyses, correct classification rates were higher for iatrogenic than for endogenous Cushing’s syndrome.
Conclusion Regarding the advanced problem of detecting Cushing’s syndrome within a study sample matched by body mass index, we found moderate classification accuracy by facial image analysis. Classification accuracy is most likely higher in a larger sample with healthy control subjects. Further studies might pursue a more advanced analysis and classification algorithm.
The dopaminergic treatment represents the primary treatment in prolactinomas, which are the most common pituitary adenomas and account for about 40% of all pituitary tumours with an annual incidence of six to ten cases per million population. The dopaminergic treatment includes ergot and non-ergot derivatives with high affinity for the dopamine receptors D1 or/and D2. Through the activation of the dopaminergic pathway on pituitary lactotrophs, the dopamine agonists inhibit the prolactin synthesis and secretion, therefore normalizing the prolactin levels and restoring eugonadism, but they also lead to tumour shrinkage. Treatment with dopamine agonists has been associated – apart from the common side effects such as gastrointestinal symptoms, dizziness and hypotension – with neuropsychiatric side effects such as impulse control disorders (e.g. pathological gambling, compulsive shopping, hypersexuality and binge eating) and also with behavioral changes from low mood, irritability and verbal aggressiveness up to psychotic and manic symptoms and paranoid delusions not only in patients with prolactinomas but also in patients with Parkinson’s disease and restless leg syndrome. They usually have de novo onset after initiation of the dopaminergic treatment and have been mainly reported in patients with Parkinson’s disease, who are being treated with higher doses of dopamine agonists. Moreover, dopamine and prolactin seem to play an essential role in the metabolic pathway. Patients with hyperprolactinemia tend to have increased body weight and an altered metabolic profile with hyperinsulinemia and increased prevalence of diabetes mellitus in comparison to healthy individuals and patients with non-functioning pituitary adenomas. Treatment with dopamine agonists in these patients in short-term studies seems to lead to weight loss and amelioration of the metabolic changes. Together these observations provide evidence that dopamine and prolactin have a crucial role both in the regard and metabolic system, findings that merit further investigation in long-term studies.
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