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Erschienen in:

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 | 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.
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Metadaten
Titel
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
Publikationsdatum
18.04.2022
Verlag
Springer US
Erschienen in
Pituitary / Ausgabe 3/2022
Print ISSN: 1386-341X
Elektronische ISSN: 1573-7403
DOI
https://doi.org/10.1007/s11102-022-01216-0

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