A new risk stratification algorithm for the management of patients with adrenal incidentalomas.

[adrenal incidentaloma]

Although adrenal incidentalomas (AI ) are detected in ≤5 % of patients undergoing chest and abdominal computed tomography (CT ), their management is challenging . The current guidelines include recommendations from the National Institutes of Health , the American Association of Endocrine Surgeons (AAES ), and the American Association for Cancer Education (AACE ). The aim of this study was to develop a new risk stratification model and compare its performance against the existing guidelines for managing AI .A risk stratification model was designed by assigning points for adrenal size (1 , 2 , or 3 points for tumors <4 , 4 -6 , or >6 cm , respectively ) and Hounsfield unit (HU ) density on noncontrast CT (1 , 2 , or 3 points for HU <10 , 10 -20 , or >20 , respectively ). This model was applied retrospectively to 157 patients with AI managed in an endocrine surgery clinic to assign a score to each tumor . The utility of this model versus the AAES /AACE guidelines was assessed .Of the 157 patients , 54 (34 %), had tumors <4 cm with HU <10 (a score of 2 ). One third of these were hormonally active on biochemical workup and underwent adrenalectomy . The remaining two thirds were nonsecretory lesions and have been followed conservatively with annual testing . In 103 patients (66 %), the adrenal mass was >4 cm and /or had indeterminate features on noncontrast CT (HU >10 , irregular borders , heterogeneity ), and adrenalectomy was performed after hormonal evaluation was completed (10 were hormonally active on biochemical testing ). Seven of these patients (7 %) had adrenocortical cancer on final pathology with tumor size <4 cm in 0 , 4 -6 cm in 1 , and >6 cm in 5 patients . Of the hormonally inactive patients , 32 % had a score of 3 , 38 % 4 , and 30 % 5 or 6 . The incidence of adrenocortical cancer in these subgroups was 0 , 0 , and 25 %, respectively .This study shows that an algorithm that utilizes the hormonal activity at the first decision step followed by a consolidated risk stratification , based on tumor size and HU density , has a potential to spare a substantial number of patients from unnecessary "diagnostic " surgery for AI .

Diseases
Validation

Diseases presenting "cancer" symptom

achondroplasia

acute rheumatic fever

adrenal incidentaloma

alpha-thalassemia

benign recurrent intrahepatic cholestasis

cadasil

canavan disease

carcinoma of the gallbladder

cholangiocarcinoma

coats disease

congenital adrenal hyperplasia

congenital diaphragmatic hernia

cowden syndrome

cushing syndrome

cutaneous mastocytosis

dedifferentiated liposarcoma

dystrophic epidermolysis bullosa

epidermolysis bullosa simplex

erdheim-chester disease

erythropoietic protoporphyria

esophageal adenocarcinoma

esophageal carcinoma

esophageal squamous cell carcinoma

familial hypocalciuric hypercalcemia

familial mediterranean fever

gm1 gangliosidosis

heparin-induced thrombocytopenia

hereditary cerebral hemorrhage with amyloidosis

hirschsprung disease

hodgkin lymphoma, classical

inclusion body myositis

junctional epidermolysis bullosa

kabuki syndrome

kallmann syndrome

kindler syndrome

lamellar ichthyosis

liposarcoma

locked-in syndrome

lymphangioleiomyomatosis

monosomy 21

neuralgic amyotrophy

oculocutaneous albinism

oligodontia

oral submucous fibrosis

papillon-lefèvre syndrome

pendred syndrome

pleomorphic liposarcoma

primary effusion lymphoma

proteus syndrome

pyomyositis

pyruvate dehydrogenase deficiency

severe combined immunodeficiency

sneddon syndrome

systemic capillary leak syndrome

triple a syndrome

von hippel-lindau disease

waldenström macroglobulinemia

well-differentiated liposarcoma

werner syndrome

wiskott-aldrich syndrome

wolf-hirschhorn syndrome

x-linked adrenoleukodystrophy

This symptom has already been validated