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Machine learning as a clinical decision support tool for patients with acromegaly.
- Source :
-
Pituitary [Pituitary] 2022 Jun; Vol. 25 (3), pp. 486-495. Date of Electronic Publication: 2022 Apr 18. - Publication Year :
- 2022
-
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.<br />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.<br />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.<br />Conclusions: ML models may serve as valuable tools in the prediction of remission and SRL resistance.<br /> (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
- Subjects :
- Humans
Insulin-Like Growth Factor I metabolism
Machine Learning
Retrospective Studies
Treatment Outcome
Acromegaly metabolism
Acromegaly surgery
Adenoma metabolism
Adenoma surgery
Decision Support Systems, Clinical
Growth Hormone-Secreting Pituitary Adenoma metabolism
Growth Hormone-Secreting Pituitary Adenoma surgery
Human Growth Hormone
Subjects
Details
- Language :
- English
- ISSN :
- 1573-7403
- Volume :
- 25
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Pituitary
- Publication Type :
- Academic Journal
- Accession number :
- 35435565
- Full Text :
- https://doi.org/10.1007/s11102-022-01216-0