18.04.2022
Machine learning as a clinical decision support tool for patients with acromegaly
verfasst von:
Cem Sulu, Ayyüce Begüm Bektaş, Serdar Şahin, Emre Durcan, Zehra Kara, Ahmet Numan Demir, Hande Mefkure Özkaya, Necmettin Tanrıöver, Nil Çomunoğlu, Osman Kızılkılıç, Nurperi Gazioğlu, Mehmet Gönen, Pınar Kadıoğlu
Erschienen in:
Pituitary
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Ausgabe 3/2022
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Abstract
Objective
To develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical features associated with the prognosis.
Methods
We studied outcomes using the area under the receiver operating characteristics (AUROC) values, which were reported as the performance metric. To determine the importance of each feature and easy interpretation, Shapley Additive explanations (SHAP) values, which help explain the outputs of ML models, are used.
Results
One-hundred fifty-two patients with acromegaly were included in the final analysis. The mean AUROC values resulting from 100 independent replications were 0.728 for postoperative 3 months remission status classification, 0.879 for remission at last visit classification, and 0.753 for SRL resistance status classification. Extreme gradient boosting model demonstrated that preoperative growth hormone (GH) level, age at operation, and preoperative tumor size were the most important predictors for early remission; resistance to SRL and preoperative tumor size represented the most important predictors of remission at last visit, and postoperative 3-month insulin-like growth factor 1 (IGF1) and GH levels (random and nadir) together with the sparsely granulated somatotroph adenoma subtype served as the most important predictors of SRL resistance.
Conclusions
ML models may serve as valuable tools in the prediction of remission and SRL resistance.