[Pituitary insufficiency: basics in diagnosis and therapy]. / Hypophyseninsuffizienz ­ das Einmaleins in Diagnostik und Therapie.

Pituitary insufficiency is a partial or complete failure of secretion of one or more hormones from the pituitary gland. The pituitary gland is located in the hypophysial fossa of the sella turcica of the os sphenoidale and produces ACTH, LH, FSH, GH, TSH, and prolactin. Pituitary insufficiency can be caused by acute damage, such as secondary to traumatic brain injury. It can also be a result of chronic alterations, such as increasing tumor expansion.Pituitary insufficiency often presents with nonspecific symptoms (e.g. fatigue, listlessness, decreased performance, sleep disturbances, weight change) that leads to a challenging and sometimes delayed diagnosis. The symptoms correspond to the failure of the corresponding endorgans. Occasionally, symptoms such as a loss of libido, secondary amenorrhea or nausea in stressful situations are diagnostically indicative.Further clarification includes a clinical examination with endocrinological testing of the pituitary function. Alteration of pituitary hormone secretion can also occur physiologically as in pregnancy, depression or obesity. Substitution therapy of the failed corticotropic, thyrotropic and gonadotropic axis is corresponding to the therapy of a primary endorgan insufficiency. Adequate diagnosis and treatment of pituitary insufficiency is important, as this may prevent life-threatening crises such as an adrenal crisis.

Computer Vision Technology in the Differential Diagnosis of Cushing's Syndrome.

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.