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Review
. 2017 Sep 7:4:108-114.
doi: 10.1016/j.ejro.2017.08.002. eCollection 2017.

Patient-specific workup of adrenal incidentalomas

Romy R de Haan  1 , Johannes B R Visser  2 , Ewoud Pons  1   3 , Richard A Feelders  4 , Uzay Kaymak  2 , M G Myriam Hunink  5   1   6 , Jacob J Visser  1
Affiliations
Review

Patient-specific workup of adrenal incidentalomas

Romy R de Haan et al. Eur J Radiol Open. .

Abstract

Purpose: : To develop a clinical prediction model to predict a clinically relevant adrenal disorder for patients with adrenal incidentaloma.

Materials and methods: : This retrospective study is approved by the institutional review board, with waiver of informed consent. Natural language processing is used for filtering of adrenal incidentaloma cases in all thoracic and abdominal CT reports from 2010 till 2012. A total of 635 patients are identified. Stepwise logistic regression is used to construct the prediction model. The model predicts if a patient is at risk for malignancy or hormonal hyperfunction of the adrenal gland at the moment of initial presentation, thus generates a predicted probability for every individual patient. The prediction model is evaluated on its usefulness in clinical practice using decision curve analysis (DCA) based on different threshold probabilities. For patients whose predicted probability is lower than the predetermined threshold probability, further workup could be omitted.

Results: : A prediction model is successfully developed, with an area under the curve (AUC) of 0.78. Results of the DCA indicate that up to 11% of patients with an adrenal incidentaloma can be avoided from unnecessary workup, with a sensitivity of 100% and specificity of 11%.

Conclusion: : A prediction model can accurately predict if an adrenal incidentaloma patient is at risk for malignancy or hormonal hyperfunction of the adrenal gland based on initial imaging features and patient demographics. However, with most adrenal incidentalomas labeled as nonfunctional adrenocortical adenomas requiring no further treatment, it is likely that more patients could be omitting from unnecessary diagnostics.

Keywords: Adrenal incidentaloma; Patient-specific workup; Prediction model.

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Figures

Fig. 1
Fig. 1
Flowchart study design.
Fig. 2
Fig. 2
Selected variables for 10-fold cross-validation on the validation data.
Fig. 3
Fig. 3
The ROC curves (a) and AUK curves (b) of the cross-validation and validation data.
Fig. 4
Fig. 4
Decision curve analysis. Note − At a threshold probability of 3% or lower, the model does not differ from treating all patients.
See this image and copyright information in PMC

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